NET Wiki - User contributions [en]

Seminar on Internet Technologies (Summer 2018)

2018-04-11T18:36:31Z

Tzhao: /* Topics */

== Details ==

{{CourseDetails
|credits=4 ECTS (BSc/MSc AI); 4 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=April 12, 16:00-16:30: Introduction Meeting
|place=IFI Building, Room 1.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=211342&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20181&getglobal=semester&htmlBodyOnly=true&noDBAction=y&init=y]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser make sure that the student start to work on the topic and following an accepted methodology.
**Midterm milestone. (ex. programming tasks done etc... )

*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''April. 12, 16:00ct''': Introduction meeting
* TBD : Deadline for registration
* TBD : Presentations
* '''Sept. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Reinforcement Mechanism Design'''
| Mechanism design is a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Reinforcement mechanism design is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. The framework can be applied on many key application scenarios, such as Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, the framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, the frame-work automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that the solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios.
| Basic knowledge of machine learning, deep learning and big data analysis. Familar with mechanism design.
|Bo Zhao (bo.zhao@gwdg.de)
|[https://pdfs.semanticscholar.org/5ec9/ed5c9936c069fa554603aa773d3ee036b4ac.pdf][https://arxiv.org/abs/1708.07607][http://iiis.tsinghua.edu.cn/~kenshin/rmd_ec.pdf]
|-
| '''Understanding and implementing web crawler'''
| Web crawlers collect information such the URL of the website, the meta tag information, the Web page content, the links in the webpage and the destinations leading from those links, the web page title and any other relevant information. Web crawler can be used in data mining, wherein pages are analyzed for different properties like statistics, and data analytics are then performed on them. The topic has two main tasks: 1) explore and summarize existing python-based web crawling frameworks; 2) design and implement a simple web crawler with Python, e.g., crawl some information of a simple webpage in Twitter.
| The student interested in this topic should be familiar with Python, and ideally has some practical experience with Python.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://en.wikipedia.org/wiki/Web_crawler]
|-
| '''Getting a Practical Understanding of Segment Routing'''
| Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic enigneering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic.
| The student should be at least familiar with one programming language (eg. Java or Python), basic open-flow SDN and basic linux skills.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
| [http://www.segment-routing.net/][http://www.segment-routing.org/]
|-
| '''Hands-on study of Vector Packet Processing (VPP)'''
| The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs. The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
The student's task will be acquiring hands-on experience with the VPP platform (included source code). The expected outcome would be a detailed report on how to use VPP and how to create a custom plug-in (for this part a demo application should be implemented).

| Basic networking knowledge, C/C++ programming, Unix/Linux administration. Knowledge of Data Plane Development Kit (DPDK) would be beneficial (but not mandatory).
| [http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://wiki.fd.io/view/VPP]
|-

| '''Deep Learning and Smart City'''
| Smart city is a hot topic in recent years. And deep learning is another hot topic. However, the application of DL in smart city areas is largely overlooked by the reserach community. The student picking this topic need to read several recent papers about connecting DL to smart city questions. In this process, you will know basic concept, general problems and important approahces in this ﬁeld.
| Basic machine learning knowledge
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14501]
|-

| ''' Research and implementation of an OPC UA application in ICN'''
| THE OPC foundation is a consortium of industry partners that is responsible for creating and maintaining industry standards. There most recent standard is called Open Platform Communications Unified Architecture (OPC UA). OPC UA brings a significant enhancement to the existing OPC framework, especially making is platform independent, and turning it into a service oriented architecture. It is an open source architecture and in this topic you will be required to perform a research on the OPC UA standard and the communication protocols offered in this architecture and implement an application based on this architecture in Information Centric Networking (ICN).

| Basic networking knowledge, C/C++ programming, Unix/Linux, Java and Information Centric Networking (ICN).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://opcfoundation.org/]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2018)

2018-04-11T18:36:09Z

Tzhao: /* Topics */

== Details ==

{{CourseDetails
|credits=4 ECTS (BSc/MSc AI); 4 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=April 12, 16:00-16:30: Introduction Meeting
|place=IFI Building, Room 1.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=211342&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20181&getglobal=semester&htmlBodyOnly=true&noDBAction=y&init=y]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser make sure that the student start to work on the topic and following an accepted methodology.
**Midterm milestone. (ex. programming tasks done etc... )

*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''April. 12, 16:00ct''': Introduction meeting
* TBD : Deadline for registration
* TBD : Presentations
* '''Sept. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Reinforcement Mechanism Design'''
Mechanism design is a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Reinforcement mechanism design is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. The framework can be applied on many key application scenarios, such as Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, the framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, the frame-work automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that the solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios.
| Basic knowledge of machine learning, deep learning and big data analysis. Familar with mechanism design.
|Bo Zhao (bo.zhao@gwdg.de)
|[https://pdfs.semanticscholar.org/5ec9/ed5c9936c069fa554603aa773d3ee036b4ac.pdf][https://arxiv.org/abs/1708.07607][http://iiis.tsinghua.edu.cn/~kenshin/rmd_ec.pdf]
|-
| '''Understanding and implementing web crawler'''
| Web crawlers collect information such the URL of the website, the meta tag information, the Web page content, the links in the webpage and the destinations leading from those links, the web page title and any other relevant information. Web crawler can be used in data mining, wherein pages are analyzed for different properties like statistics, and data analytics are then performed on them. The topic has two main tasks: 1) explore and summarize existing python-based web crawling frameworks; 2) design and implement a simple web crawler with Python, e.g., crawl some information of a simple webpage in Twitter.
| The student interested in this topic should be familiar with Python, and ideally has some practical experience with Python.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://en.wikipedia.org/wiki/Web_crawler]
|-
| '''Getting a Practical Understanding of Segment Routing'''
| Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic enigneering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic.
| The student should be at least familiar with one programming language (eg. Java or Python), basic open-flow SDN and basic linux skills.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
| [http://www.segment-routing.net/][http://www.segment-routing.org/]
|-
| '''Hands-on study of Vector Packet Processing (VPP)'''
| The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs. The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
The student's task will be acquiring hands-on experience with the VPP platform (included source code). The expected outcome would be a detailed report on how to use VPP and how to create a custom plug-in (for this part a demo application should be implemented).

| Basic networking knowledge, C/C++ programming, Unix/Linux administration. Knowledge of Data Plane Development Kit (DPDK) would be beneficial (but not mandatory).
| [http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://wiki.fd.io/view/VPP]
|-

| '''Deep Learning and Smart City'''
| Smart city is a hot topic in recent years. And deep learning is another hot topic. However, the application of DL in smart city areas is largely overlooked by the reserach community. The student picking this topic need to read several recent papers about connecting DL to smart city questions. In this process, you will know basic concept, general problems and important approahces in this ﬁeld.
| Basic machine learning knowledge
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14501]
|-

| ''' Research and implementation of an OPC UA application in ICN'''
| THE OPC foundation is a consortium of industry partners that is responsible for creating and maintaining industry standards. There most recent standard is called Open Platform Communications Unified Architecture (OPC UA). OPC UA brings a significant enhancement to the existing OPC framework, especially making is platform independent, and turning it into a service oriented architecture. It is an open source architecture and in this topic you will be required to perform a research on the OPC UA standard and the communication protocols offered in this architecture and implement an application based on this architecture in Information Centric Networking (ICN).

| Basic networking knowledge, C/C++ programming, Unix/Linux, Java and Information Centric Networking (ICN).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://opcfoundation.org/]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2018)

2018-04-10T10:49:51Z

Tzhao: /* Details */

== Details ==

{{CourseDetails
|credits=4 ECTS (BSc/MSc AI); 4 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=April 12, 16:00-16:30: Introduction Meeting
|place=IFI Building, Room 1.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=211342&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20181&getglobal=semester&htmlBodyOnly=true&noDBAction=y&init=y]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser make sure that the student start to work on the topic and following an accepted methodology.
**Midterm milestone. (ex. programming tasks done etc... )

*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''April. 12, 16:00ct''': Introduction meeting
* TBD : Deadline for registration
* TBD : Presentations
* '''Sept. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Understanding and implementing web crawler'''
| Web crawlers collect information such the URL of the website, the meta tag information, the Web page content, the links in the webpage and the destinations leading from those links, the web page title and any other relevant information. Web crawler can be used in data mining, wherein pages are analyzed for different properties like statistics, and data analytics are then performed on them. The topic has two main tasks: 1) explore and summarize existing python-based web crawling frameworks; 2) design and implement a simple web crawler with Python, e.g., crawl some information of a simple webpage in Twitter.
| The student interested in this topic should be familiar with Python, and ideally has some practical experience with Python.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://en.wikipedia.org/wiki/Web_crawler]
|-
| '''Getting a Practical Understanding of Segment Routing'''
| Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic enigneering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic.
| The student should be at least familiar with one programming language (eg. Java or Python), basic open-flow SDN and basic linux skills.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
| [http://www.segment-routing.net/][http://www.segment-routing.org/]
|-
| '''Hands-on study of Vector Packet Processing (VPP)'''
| The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs. The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
The student's task will be acquiring hands-on experience with the VPP platform (included source code). The expected outcome would be a detailed report on how to use VPP and how to create a custom plug-in (for this part a demo application should be implemented).

| Basic networking knowledge, C/C++ programming, Unix/Linux administration. Knowledge of Data Plane Development Kit (DPDK) would be beneficial (but not mandatory).
| [http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://wiki.fd.io/view/VPP]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2018)

2018-04-04T09:40:52Z

Tzhao: /* Topics */

Seminar on Internet Technologies (Summer 2018)

2018-04-04T09:39:53Z

Tzhao: /* Passing requirements */

Seminar on Internet Technologies (Summer 2018)

2018-04-04T09:39:12Z

Tzhao:

2017-10-16T15:44:58Z

Tzhao: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques (Assigned to Yifan Chen)'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges (assigned to Tetiana Tolmachova)'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs (Assigned to Waqar Alamgir)'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning (Assigned to Fawad Abbasi)'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets (assigned to Dia Adden)'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey (assigned to Cheng Chang) '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming '''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas)
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of these standards and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (This can also be a starting point for a project/thesis).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''IoT with ICN''' (Assigned to : Md Tofiqul Islam)
IoT is a growing topic of Interest but existing technologies do not support the resource constrained devices efficiently. ICN is a promising new future Internet architecture and IoT can greatly benefit by using ICN. In this topic, you will explore the existing ICN proposals for IoT and will specifically work on naming challenges in IoT with ICN.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''Crawling the Internet''' (Assigned to : Hanna Holderied)
Many services specifically including Google use crawlers to systematically browse the Internet for Indexing and other purposes. In this task you will explore the different types of crawlers that exist in the internet and what are they used for. You will perform a research on how these crawlers work and what their results are used for. This topic can also lead to a potential Master project/thesis.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
|-
|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

Teaching

2017-09-18T13:54:37Z

Tzhao: /* Courses Winter Semester 2017/2018 */

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Computer Networks (Winter 2017/2018) | Computer Networks]] (BSc)
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)
* [[Seminar on Internet Technologies (Winter 2017/2018) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Teaching

2017-09-18T13:53:22Z

Tzhao: /* Courses Winter Semester 2017/2018 */

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Seminar on Internet Technologies (Winter 2017/2018) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2017/2018) | Computer Networks]] (BSc)
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

2016-10-14T02:01:01Z

Tzhao:

Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)

2016-06-23T10:18:11Z

Tzhao: /* Topics */

== Details ==
{{CourseDetails
|credits=5 ECTS
|module=M.Inf.223: Seminar Telematik III ''-or-'' M.Inf.224: Seminar Computernetzewerke II (old Regulations) ''-or-'' 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Dr. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao, MSc.], [http://www.net.informatik.uni-goettingen.de/people/hong_huang Ms. Hong Huang]
|time=10:15-12:00
|place=SR3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=157922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
People move and stay in different locations in different time. Human mobility has a lot of impact on the social group formation and dynamics, interaction, and other activities. AToMIC course in summer semester 2016 will be focused on social networks on mobile big data. It will start with introduction to related methods and theories, together with real dataset demonstration. Students are expected to be organized in groups, running some tools on selected datasets, and present some scientific work on related topics.

==Requirements==

Holding at least a bachelor's degree on computer science or related fields.

==Passing requirements==
* Demonstration (20 ~ 25 min. presentation + 10 min. Q&A for each group)
** This accounts for 20% of your grade.
** Present practical work in groups (each group member should present your own specific work).
* Final presentation (30 ~ 35 min. presentation + 10 min. Q&A for each group).
** This accounts for 40% of your grade.
** Give a final presentation in groups (each group member should present your own specific work).
** The final presentation should contain a comprehensive survey about the selected topic and final experiment results.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
** Everyone in each group writes a report on your specific work in your topic (including your own comprehensive survey on the selected topic and your own practical work).
* The Demonstration and final presentation must be given in English.
* The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).
* If your group consists of more than or less than 2 students, you can adjust your total presentation duration.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 15.04.2016
| Introduction, mobile big data; literatures
| [[Media:AToMIC_01_introduction.pdf | pdf]]
|-
| align="right" | 22.04.2016
| Big data methods (machine learning, data mining, etc)
| [https://wiki.net.informatik.uni-goettingen.de/w/images/7/7c/ATOMIC-SS16-02-BigDataAnalysis.pdf pdf]
|-
| align="right" | 29.04.2016
| Big data methods (cont.); data samples
|[https://wiki.net.informatik.uni-goettingen.de/w/images/c/c9/ATOMIC-Demo%26CourseAssignment.pdf pdf]
|-
| align="right" | 06.05.2016
| Social network theory
|[https://wiki.net.informatik.uni-goettingen.de/w/images/5/53/ATOMIC-SS16-03-SocialNetworkTheory.pdf pdf]
|-
| align="right" | 13.05.2016
| Interdisciplinary methods and case study
|[https://wiki.net.informatik.uni-goettingen.de/w/images/8/8b/ATOMIC-SS16-04-Integrating_Data_Mining_and_Qualitative_Studies.pdf pdf][https://wiki.net.informatik.uni-goettingen.de/w/images/e/e5/ATOMIC-SS16-04-Triadic.pdf pdf]
|-
| align="right" | 20.05.2016
|cancelled due to business trips
|
|-
| align="right" | 27.05.2016
| cancelled due to business trips
|
|-
| align="right" | 03.06.2016
| cancelled due to business trips
|
|-
| align="right" | 10.06.2016
| cancelled due to business trips
|
|-
| align="right" | 17.06.2016
| cancelled due to business trips
|
|-
| align="right" | 24.06.2016
| Practical session (Demonstration)
|
|-
| align="right" | 01.07.2016
| cancelled
|
|-
| align="right" | 08.07.2016
| Final presentations
|
|-
| align="right" | 15.07.2016
| cancelled due to business trip
|
|-
|}

== Topics ==

The list of topics is as follows. The topic description shows a basic task for each topic. The literature provided here is only for reference. Each group should read more related literatures about your topic to give a comprehensive survey.

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Dataset'''
|{{Hl2}} |'''Literature'''
|-
| Influential user identification (assigned to Alireza Amiri and Tayyebe Emadinia)
|The project is to identify influential users based on users’ features.
|Twitter [http://snap.stanford.edu/data/egonets-Twitter.html]
|[http://dl.acm.org/citation.cfm?id=2503797] [http://dl.acm.org/citation.cfm?id=1718520]
|-
| Community detection (assigned to Aynur Amirfallah)
|The project is to cluster different communities based on topics.
|Facebook [http://snap.stanford.edu/data/egonets-Facebook.html]
|[http://dl.acm.org/citation.cfm?id=2501657][http://dl.acm.org/citation.cfm?id=1348552]
|-
|Point-of-Interest recommendation
|The project is to make point-of-interest(POI) recommendation based on social influence and check-ins.
|Gowalla [http://snap.stanford.edu/data/loc-gowalla.html]
|[http://dl.acm.org/citation.cfm?id=2525357][http://dl.acm.org/citation.cfm?id=2484030]
|-
|Link prediction and friend recommendation
|The project is to make friend recommendation based on social networks and check-ins.
|Brightkite [http://snap.stanford.edu/data/loc-brightkite.html]
|[https://www.researchgate.net/publication/226566834_A_Survey_of_Link_Prediction_in_Social_Networks][https://www.cl.cam.ac.uk/~cm542/papers/kdd2011.pdf]
|-
|Analysis of individual activity and mobile pattern (assigned to Chencheng Liang)
|The project is to give a detailed analysis of individual activity and mobile pattern based on everyday life tracks.
|Social Evolution Dataset [http://realitycommons.media.mit.edu/socialevolution4.html]
|[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5284112][http://www.mdpi.com/2220-9964/4/3/1512]
|-
|}

==Final Presentations & Report==

[[Category:Courses]]

Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)

2016-06-10T14:59:10Z

Tzhao: /* Passing requirements */

== Details ==
{{CourseDetails
|credits=5 ECTS
|module=M.Inf.223: Seminar Telematik III ''-or-'' M.Inf.224: Seminar Computernetzewerke II (old Regulations) ''-or-'' 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Dr. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao, MSc.], [http://www.net.informatik.uni-goettingen.de/people/hong_huang Ms. Hong Huang]
|time=10:15-12:00
|place=SR3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=157922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
People move and stay in different locations in different time. Human mobility has a lot of impact on the social group formation and dynamics, interaction, and other activities. AToMIC course in summer semester 2016 will be focused on social networks on mobile big data. It will start with introduction to related methods and theories, together with real dataset demonstration. Students are expected to be organized in groups, running some tools on selected datasets, and present some scientific work on related topics.

==Requirements==

Holding at least a bachelor's degree on computer science or related fields.

==Passing requirements==
* Demonstration (20 ~ 25 min. presentation + 10 min. Q&A for each group)
** This accounts for 20% of your grade.
** Present practical work in groups (each group member should present your own specific work).
* Final presentation (30 ~ 35 min. presentation + 10 min. Q&A for each group).
** This accounts for 40% of your grade.
** Give a final presentation in groups (each group member should present your own specific work).
** The final presentation should contain a comprehensive survey about the selected topic and final experiment results.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
** Everyone in each group writes a report on your specific work in your topic (including your own comprehensive survey on the selected topic and your own practical work).
* The Demonstration and final presentation must be given in English.
* The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).
* If your group consists of more than or less than 2 students, you can adjust your total presentation duration.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 15.04.2016
| Introduction, mobile big data; literatures
| [[Media:AToMIC_01_introduction.pdf | pdf]]
|-
| align="right" | 22.04.2016
| Big data methods (machine learning, data mining, etc)
| [https://wiki.net.informatik.uni-goettingen.de/w/images/7/7c/ATOMIC-SS16-02-BigDataAnalysis.pdf pdf]
|-
| align="right" | 29.04.2016
| Big data methods (cont.); data samples
|[https://wiki.net.informatik.uni-goettingen.de/w/images/c/c9/ATOMIC-Demo%26CourseAssignment.pdf pdf]
|-
| align="right" | 06.05.2016
| Social network theory
|[https://wiki.net.informatik.uni-goettingen.de/w/images/5/53/ATOMIC-SS16-03-SocialNetworkTheory.pdf pdf]
|-
| align="right" | 13.05.2016
| Interdisciplinary methods and case study
|[https://wiki.net.informatik.uni-goettingen.de/w/images/8/8b/ATOMIC-SS16-04-Integrating_Data_Mining_and_Qualitative_Studies.pdf pdf][https://wiki.net.informatik.uni-goettingen.de/w/images/e/e5/ATOMIC-SS16-04-Triadic.pdf pdf]
|-
| align="right" | 20.05.2016
|cancelled due to business trips
|
|-
| align="right" | 27.05.2016
| cancelled due to business trips
|
|-
| align="right" | 03.06.2016
| cancelled due to business trips
|
|-
| align="right" | 10.06.2016
| cancelled due to business trips
|
|-
| align="right" | 17.06.2016
| cancelled due to business trips
|
|-
| align="right" | 24.06.2016
| Practical session (Demonstration)
|
|-
| align="right" | 01.07.2016
| cancelled
|
|-
| align="right" | 08.07.2016
| Final presentations
|
|-
| align="right" | 15.07.2016
| cancelled due to business trip
|
|-
|}

== Topics ==

The list of topics is as follows. The topic description shows a basic task for each topic. The literature provided here is only for reference. Each group should read more related literatures about your topic to give a comprehensive survey.

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Dataset'''
|{{Hl2}} |'''Literature'''
|-
| Influential user identification (assigned to Alireza Amiri and Tayyebe Emadinia)
|The project is to identify influential users based on users’ features.
|Twitter [http://snap.stanford.edu/data/egonets-Twitter.html]
|[http://dl.acm.org/citation.cfm?id=2503797] [http://dl.acm.org/citation.cfm?id=1718520]
|-
| Community detection (assigned to Aynur Amirfallah)
|The project is to cluster different communities based on topics.
|Facebook [http://snap.stanford.edu/data/egonets-Facebook.html]
|[http://dl.acm.org/citation.cfm?id=2501657][http://dl.acm.org/citation.cfm?id=1348552]
|-
|Point-of-Interest recommendation
|The project is to make point-of-interest(POI) recommendation based on social influence and check-ins.
|Gowalla [http://snap.stanford.edu/data/loc-gowalla.html]
|[http://dl.acm.org/citation.cfm?id=2525357][http://dl.acm.org/citation.cfm?id=2484030]
|-
|Link prediction and friend recommendation
|The project is to make friend recommendation based on social networks and check-ins.
|Brightkite [http://snap.stanford.edu/data/loc-brightkite.html]
|[https://www.researchgate.net/publication/226566834_A_Survey_of_Link_Prediction_in_Social_Networks][https://www.cl.cam.ac.uk/~cm542/papers/kdd2011.pdf]
|-
|Analysis of individual activity and mobile pattern (assigned to Atefeh Khajeh and Chencheng Liang)
|The project is to give a detailed analysis of individual activity and mobile pattern based on everyday life tracks.
|Social Evolution Dataset [http://realitycommons.media.mit.edu/socialevolution4.html]
|[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5284112][http://www.mdpi.com/2220-9964/4/3/1512]
|-
|}

==Final Presentations & Report==

[[Category:Courses]]