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Seminar on Internet Technologies (Winter 2017/2018): Difference between revisions
Seminar on Internet Technologies (Winter 2017/2018) (view source)
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* Present the selected topic (20 min. presentation + 10 min. Q&A). | * Present the selected topic (20 min. presentation + 10 min. Q&A). | ||
** This accounts for 40% of your grade. | ** This accounts for 40% of your grade. | ||
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp. | * 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. | ** This accounts for 40% of your grade. | ||
* Please check the [[#Schedule]] and adhere to it. | * Please check the [[#Schedule]] and adhere to it. | ||
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==Schedule== | ==Schedule== | ||
* '''Oct. 19, 16:00ct''': Introduction meeting | * '''Oct. 19, 16:00ct''': Introduction meeting | ||
* ''' | * '''Jan. 11''' : Deadline for registration | ||
* ''' | * '''Jan. 18 and Jan. 19''' : Presentations | ||
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!) | * '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!) | ||
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|{{Hl2}} |'''Initial Readings''' | |{{Hl2}} |'''Initial Readings''' | ||
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| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical) | | '''Strengths and Limitations of Visualization Libraries for Data Science''' (assigned to Hannah Rauterberg; 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. | 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. | 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. | ||
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| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html] | | [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html] | ||
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| '''A survey of clustering algorithms''' | | '''A survey of clustering algorithms (assigned)''' | ||
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”: | 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 .. | a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN .. | ||
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| [https://arxiv.org/abs/1402.4645] | | [https://arxiv.org/abs/1402.4645] | ||
|- | |- | ||
| '''A Survey on Multi-view Learning''' | | '''A Survey on Multi-view Learning (Assigned to Oleh Astappiev)''' | ||
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. | 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] | | [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao] | ||
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|[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] | |[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] | ||
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| '''Segment Routing - a Survey''' | | '''Segment Routing - a Survey (assigned to Albert Demba )''' | ||
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. | 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. | '''NOTE:'''This topic could be a good entry for master project and thesis later. | ||
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|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/] | |[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/] | ||
|- | |- | ||
| '''Open Topic''' | | '''Open Topic (assigned to iman alobaidi) ''' | ||
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. | 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] | | [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] | ||
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|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831] | |[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''' | | '''Parallel Processing Systems for Big Data (Assigned to Muhammad Jawad)''' | ||
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. | 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) | |Bo Zhao (bo.zhao@gwdg.de) | ||
|[http://ieeexplore.ieee.org/abstract/document/7547948/] | |[http://ieeexplore.ieee.org/abstract/document/7547948/] | ||
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| '''Towards SDN and NFV Fault Management and High Availability''' | | '''Towards SDN and NFV Fault Management and High Availability (assigned to Hesham Hosney) ''' | ||
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. | 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. | ||
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| [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] | | [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''' | |'''Service Plane for Network Functions: Network Service Headers and Other alternatives (assigned to Gulzaib Amjad)''' | ||
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. | 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. | ||
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| [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] | | [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''' | | '''Online Convex Optimization Algorithms for Machine learning (assigned to Jihan Munkar)''' | ||
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. | 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.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad] | ||
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| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf] | | [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''' | | '''Fuctional Zone Discovery inside Cities -- A survey assigned to Rifat Rahman''' | ||
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. | 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] | | [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] | | [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''' | | '''Human Trajectory Clustering -- A survey assigned to Shruthi Shetty''' | ||
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- | 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, | 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, | ||
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| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y] | | [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y] | ||
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| '''Adaptive Video Streaming ''' | | '''Adaptive Video Streaming ''' (Assigned to: Muhammad Salman Gurmani) | ||
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??) | 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 ] | | [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] | | [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] | ||
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| '''D2D Proximity Services''' | | '''D2D Proximity Services''' (Assigned to: hamid reza Karimian) | ||
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. | 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]. | '''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 ] | | [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] | | [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] | ||
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| '''360-degree Videos & Virtual Reality''' (Assigned to: Masih Ghaderi) | |||
360-degree videos are video recordings where a view in every direction is recorded at the same time, shot using an omnidirectional camera or a collection of cameras. During playback the viewer has control of the viewing direction like a panorama. They are often associated with VR (Virtual Reality), where a person using special equipment is able to "look around" in an artificial world. This task consists in study the actual solutions and protocols that enables the transmission of 360-degree videos, highlighting the challenges related to an efficient transmission of the video stream. | |||
'''NOTE: possiblity to extend the work for master project or thesis. | |||
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ] | |||
| [http://www.com583.com/files/Redefining%20The%20Axiom%20Of%20Story_%20The%20VR%20And%20360%20Video%20Complex%20_%20TechCrunch.pdf] [http://delivery.acm.org/10.1145/2990000/2980056/p1-qian.pdf?ip=134.76.81.35&id=2980056&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=819974159&CFTOKEN=46402817&__acm__=1508238751_aa9aa8f7a54b27ba5cfa252d87c7d5df] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823660] | |||
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| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas) | | '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas) | ||