10
edits
Theses and Projects: Difference between revisions
→* New! Edge-Cloud Orchestration for LiDAR-based Traffic Analysis
| (141 intermediate revisions by 11 users not shown) | |||
| Line 1: | Line 1: | ||
== An introduction to the Computer Networks group == | |||
See a [https://wiki.net.informatik.uni-goettingen.de/w/images/5/5a/NETGroup_Poster-Jan2021.pdf poster] for a general overview, an [http://www.net.informatik.uni-goettingen.de/?q=research anchor] to our research activities, a list of [https://wiki.net.informatik.uni-goettingen.de/w/images/a/a3/Social_Computing_publications.pdf social computing related] or networking-related publications, and the | |||
[http://www.net.informatik.uni-goettingen.de/?q=news/annual-report-2020-best-wishes-2021 annual report(s)] for our recent activities. | |||
== Joint PhD Program with University of Sydney == | |||
From September 2024 on there will be the possibility to start a joint PhD with the University of Sydney (Australia). PhD students will stay in both Göttingen and Sydney for at least one year and can achieve two PhD degrees. | |||
For more information, please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de]. | |||
In November/December 2023, Fabian visited research groups in Melbourne and Sydney. Impressions of his visit can be seen here: [[Media:australia.pdf | pdf]] | |||
== Open Theses and Student Project Topics == | == Open Theses and Student Project Topics == | ||
| Line 7: | Line 21: | ||
* (P) Student project | * (P) Student project | ||
* '''New!''' | === * '''New!''' Efficient Live Volumetric Video Streaming System=== | ||
The exponential growth of digital data and multimedia content necessitates robust and efficient systems to handle the streaming of high-resolution, three-dimensional volumetric videos. These videos offer a more immersive and realistic experience, making them increasingly used in various sectors such as virtual reality, augmented reality, and entertainment. The challenge here lies in creating a system that can handle the high-bandwidth and computation-intensive demands of live volumetric video streaming while ensuring the delivery of a seamless and high-quality user experience. This project conceptualizes the development and optimization of efficient algorithms and systems to handle volumetric video streams, mitigating bandwidth cost and latency issues. We expect you to have a background in video streaming technologies, computer vision, and programming skills. | |||
Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de] | |||
=== * '''New!''' Edge-Cloud Orchestration for LiDAR-based Traffic Analysis=== | |||
* [[ | |||
The imminent era of smart cities and autonomous vehicles paves the way for the deployment and operation of advanced monitoring and processing systems. Among these, LiDAR technology stands out for its ability to provide high-resolution, three-dimensional traffic data, becoming an essential component for efficient traffic analysis and management. However, the computation-intensive and latency-sensitive nature of LiDAR data processing poses significant challenges and dictates the need for efficient orchestration between edge and cloud computing resources. Edge-Cloud Orchestration offers an innovative solution to this problem by bridging the gap between these two technologies, enabling the low-latency processing of complex LiDAR data. It would be good if you have a background in point cloud processing/cloud computing, K8s, and programming skills. | |||
* | |||
* | Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de] | ||
=== * '''New!''' Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks=== | |||
The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. We need students for this topic. We expect you have a background in computer network and programming skills in Python. | |||
Please contact Jin Xie [jin.xie@stud.uni-goettingen.de] | |||
=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)=== | |||
Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We need students for this topic. We expect you have a background in knowledge graph and programming skills in Python. | |||
Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de] | |||
=== * '''New!''' Using LLM for Knowledge Graph Completion (B/M/P)=== | |||
Large language models (LLMs), such as ChatGPT and GPT-4 (OpenAI, 2023), have extensive internal knowledge repositories from their vast pretraining corpora, which can be used as an extra knowledge base to alleviate information scarcity for the long-tail entities in Knowledge Graphs. However, there is no effective workflow design for LLM on KGC tasks. How to leverage the LLM to perform reasoning on the KG Completion (KGC) task is a noteworthy and significant topic. We need students for this topic. We expect you to have a background in knowledge graph and LLMs, you'd better have a programming skill in Python. | |||
Please contact Tong Shen [shen.tong@cs.uni-goettingen.de] | |||
=== * '''New!''' Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) === | |||
A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful. | |||
Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de] | |||
=== * '''New!''' Image-to-Image Translation of Different Nightlight Image Types (B/M/P) === | |||
Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated. | |||
Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de] | |||
=== [Occupied] Satellite Image Indices and Machine Learning for Socio-economic Estimation (B/M/P) === | |||
There are several indices, which can be derived from satellite images. For example the Normalized Difference Vegetation Index (NDVI) indicates the presence and condition of vegetation, while the Normalized Difference Built-up Index (NDBI) indicates the presence of built-up areas such as buildings or roads. The distributions of these and other indices may have different explanatory power to estimate the socio-economic status of locations. Therefore in this project/thesis the regression performance of machine learning models - using statistics of these indices as features - to estimate socio-economic indicators shall be evaluated for the individual and also combined indices. Optionally, Convolutional Neural Networks (CNNs) can be applied additionally, which take the derived index images as input. | |||
Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de] | |||
=== * 3D natural hazard simulator === | |||
The aim of the project is to simulate representative natural hazards for hazard response, such as flooding and forest fire. A natural hazard response simulator will be implemented for both visualization and performance validation. For example, we can visualize the flooding of 2021 in Germany, and then validate the performance of drone deployment in hazard sensing and emergency communication. Here, we introduce some related works in virtual 3D scene which may help you to understand this project, e.g., Agents Toolkit (ML-Agents) [1], DisasterSim [2] and Airsim [3]. | |||
[1] Unity Technologies.Unity ML-Agents Toolkit. Jan 26, 2021.URL:https://github.com/Unity-Technologies/ml-agents. (accessed: 21.11.2021) | |||
[2] Wang, H., Liu, C. H., Dai, Z., Tang, J., & Wang, G. (2021, August). Energy-efficient 3D vehicular crowdsourcing for disaster response by distributed deep reinforcement learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3679-3687). | |||
[3] S. Shah, D. Dey, C. Lovett, and A. Kapoor. “Airsim: High-fidelity visual and physical simulation forautonomous vehicles”. In:Field and Service Robotics. Springer. 2018, pp. 621–635. | |||
Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) | |||
=== * '''[Occupied]''' OCR (Optical Character Recognition) and Annotation Transfer === | |||
The aim of the project is to develop a tool/software that can convert a printed paper with annotations and text into electronic versions with text highlighting and annotations. The successful candidate will be responsible for developing this tool/software that can perform the following tasks: | |||
1. Text Alignment: Develop algorithms to align the text in the electronic version with the original printed paper. | |||
2. Annotation Recognition: Develop software that can recognize annotation areas in the printed paper and transfer them to the electronic version. | |||
3. Transfer Annotations: Transfer annotations and highlighting from the paper-based article to the electronic version. | |||
[1] https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143 | |||
[2] https://developer.adobe.com/analytics-apis/docs/2.0/guides/endpoints/annotations/ | |||
[3] https://developer.adobe.com/document-services/apis/pdf-services/ | |||
[4] https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs | |||
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P) | |||
=== * Privacy-preserved Video Analytics=== | |||
This project/thesis topic focuses on the protection of privacy in video analytics. | |||
The project involves three key tasks: | |||
1) Implementation of a system utilizing YOLO and CycleGANs/DataGen for video analysis and processing. The code for this is already available for use. | |||
2) Development of a privacy protection mechanism by adjusting the level of blur applied to the video, taking into account a trade-off between inference accuracy (e.g., detection by YOLO) and the level of privacy protection. | |||
3) Optimize the blur level for Pan-tilt-zoom cameras to ensure that the system effectively captures key visual information while still preserving privacy. | |||
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | |||
[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618]. | |||
=== | === * AI for networking adaption === | ||
In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states). | |||
You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance. | |||
[1] Genet: Automatic Curriculum Generation for Learning Adaptation in Networking [https://francisyyan.org/documents/fyy-genet-sigcomm22.pdf] | |||
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P) | |||
=== | === * [Occupied] Video analytics with deep reinforcement learning === | ||
The proliferation of video analytics is facilitated by the advances of deep learning and the low prices of high-resolution network-connected cameras. However, the accuracy improvement from deep learning is at the high computational cost. Although the state-of-the-art smart cameras can support deep learning method, the deployed surveillance and traffic camera paint a much bleaker resource picture. For example, DNNCam that ships with a high-end embedded NVIDIA TX2 GPU costs more than $2000 while the price of deployed traffic cameras today ranges $40-$200; these cameras typically loaded with a single-core CPU only provide very scarce compute resource. Because of this huge gap, typical video analytics applications, e.g., traffic cameras stream live video to remote server for further analysis. | |||
As a result, a natural question occurs: which video streaming configuration also server decoding configuration should we select to guarantee high analysis accuracy as well as not incur network congestion? To answer this question, we attempt to explore the performance of deep reinforcement learning under this scenario. | |||
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de], Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/M/P) | |||
* | === * [Occupied] AI for Games === | ||
Can Artificial intelligence (AI) beat humans at games? | |||
AI has played an increasingly prominent and productive role in the gaming world. Implemented in many different ways, AI is used to improve game behaviors and environments. | |||
In this project, we will design AI algorithms (i.e., multi-agent reinforcement learning) for games (e.g., StarCraft: https://github.com/oxwhirl/smac). The main challenge here is to coordinate agents in achieving joint goals (i.e., win), such as by efficient communication. | |||
[1]https://www.nature.com/articles/d41586-019-03298-6 | |||
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | |||
=== * '''New!''' Socioecomonic analysis based on spatiotemporal and linguistic analysis on microblogging data === | |||
Identifying the socioeconomic status (SES) of users in social media like Twitter or Weibo is useful e.g., for digitized advertisements and social policies. This study aims to collect profiles of Twitter users on selected topics such as culture or foreign language learning, extract the temporal, spatial and linguistic features, and compare different classification algorithms (e.g., decision tree, random forest, na\"{i}ve Bayes, deep learning, and Gaussian processes classifier) to predict the socioeconomic status. | |||
[1] Ren Y, Xia T, Li Y, Chen X. Predicting socio-economic levels of urban regions via offline and online indicators. PLoS One. 2019;14(7):e0219058. Published 2019 Jul 10. doi:10.1371/journal.pone.0219058 | |||
[2] Pappalardo L, Pedreschi D, Smoreda Z, Giannotti F. Using Big Data to study the link between human mobility and socio-economic development. In: IEEE International Conference on Big Data 2015. doi:10.1109/BigData.2015.7363835 | |||
[3] Vasileios Lampos, Nikolaos Aletras, Jens K. Geyti, Bin Zou and Ingemar J. Cox (2016). Inferring the Socioeconomic Status of Social Media Users based on Behaviour and Language. Proceedings of the 38th European Conference on Information Retrieval (ECIR '16), pp. 689-695. doi:10.1007/978-3-319-30671-1_54 | |||
Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) | |||
=== [Closed] Super resolution technique for efficient video delivery === | |||
Super-resolution (SR) is one of the fundamental tasks in Computer vision. Video delivery on Internet or in WAN is important for various applications, eg., video analytics and video viewing. This project attempts to explore the potential of SR for video delivery. We expect you have Data Science and Computer Vision background, as well as programming skills like Python. | |||
Please contact Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/M/P) | |||
=== [closed] Assessing city livability with big data === | |||
* City livability is related to a number of factors, such as quality of life, job satisfaction, environment (green space, CO2/PM2.5, schooling/health support etc), policy, commuting time, entertainment. We utilize different data sources to understand their relation to the city livability, and analyze the coherent features which offer an evaluation framework for a city's attractiveness and livability for different types of citizens. We expect you have some statistics and machine learning background, as well as programming skills like Python. | |||
Please contact Prof. Xiaoming Fu (B/M/P). | |||
=== Socioeconomic analysis on commuters === | |||
* '''New!''' Understanding the commuter behaviour and the factors that lead to commuting are more important today than ever before. With steadily increasing commuter numbers, the commuter traffic can be a major bottleneck for many cities. The increasing awareness of a good work-life balance leads to more people wanting shorter commuting distances. The commuter behaviour consequently plays an increasingly important role in city and transport planning and policy making. This topic aims to infer knowledge from commuter data, analyzing the influence of GDP, housing prices, family situation, income and job market on the decision to commute. We expect you have some statistics and machine learning background, as well as programming skills like Python. | |||
Please contact Prof. Xiaoming Fu (B/M/P) | |||
=== | === Socioeconomic Status and Internet Language Usage === | ||
* | * '''New!''' Numerous people write social media posts and exchange messages with colleagues, friends, acquaintances or even strangers on different platforms. We would like to understand how the underlying social class membership (socioeconomic status) affects Internet users' language use, by investigating the sociolinguistic features in users' posts/messages across a multitude of datasets and their relationship to their socioeconomic status. We expect you have some statistics and textual analysis/natural language processing background, as well as programming skills like Python. | ||
Please contact Prof. Xiaoming Fu (B/M/P) | |||
===[closed] Multimedia Resource Allocation for QoE Improvement by Deep Learning=== | |||
* Deep learning has been widely used in various real-time applications and systems. Dynamic resource allocation for multimedia (e.g. Video) to improve QoE is an interesting topic. We need three students for this topic. We expect you have a background in deep learning and computer network, as well as programming skills like Python and Go. | |||
* | |||
(1) one to realize and improve the system for video transmission and network configuration according to resource allocation policy; | |||
* You will use QUIC [https://github.com/lucas-clemente/quic-go] protocol (Go language) to implement network allocation and place the server part on AWS/other clouds. | |||
(2) one to implement the deep learning algorithm to design the controller for dynamic resource allocations. | |||
(3) one student for the QoE model using deep learning. | |||
-- | Please contact Dr.Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ] and Weijun Wang [weijun.wang@informatik.uni-goettingen.de](B/M/P) | ||
== Ongoing Topics == | == Ongoing Topics == | ||
== Completed Topics == | |||
{| align="center" class="wikitable sortable" {{Prettytable}} | {| align="center" class="wikitable sortable" {{Prettytable}} | ||
| Line 118: | Line 201: | ||
|{{Hl2}} |'''Description''' | |{{Hl2}} |'''Description''' | ||
|{{Hl2}} |'''Student''' | |{{Hl2}} |'''Student''' | ||
|- | |||
| OCR (Optical Character Recognition) and Annotation Transfer (Bachelor Project+Thesis) | |||
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan] | |||
| | |||
| | |||
| Assigned to Jiaying | |||
|- | |||
| AI for Games (Bachelor Project+Thesis) | |||
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan] | |||
| | |||
| | |||
| Completed by Jason | |||
|- | |||
| Neural video analytics(Master Thesis) | |||
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan] | |||
| | |||
| | |||
| Completed by Mai | |||
|- | |||
| Submodel Federated learning (Bachelor Project + Thesis) | |||
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan] | |||
| | |||
| | |||
| Completed by Zilin | |||
|- | |- | ||
|Bio-Data analysis (Student project) | |Bio-Data analysis (Student project) | ||
| Line 156: | Line 262: | ||
| Assigned to Janosch Ruff | | Assigned to Janosch Ruff | ||
|- | |- | ||
| Build a personalized context-aware recommender system for customers according to their own interest. | | Build a personalized context-aware recommender system for customers according to their own interest. | ||
| | |||
| | |||
| | |||
| Completed by Haile Misgna | | Completed by Haile Misgna | ||
|- | |- | ||
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project) | | Emotion Patterns Analysis in OSNs (Bachelor thesis Project) | ||
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen] | | [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen] | ||
| | | | ||
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users. | | We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users. | ||
| Completed by Stefan Peters | | Completed by Stefan Peters | ||
| Line 236: | Line 329: | ||
|- | |- | ||
|} | |} | ||
<!--=== Congestion Control === | |||
* [[A network friendly congestion control protocol]] (M) | |||
* [[A study to improve video/voice distribution based on the congestion in the network]] (B/P) | |||
* [[A study of the use of Admission control in MPLS networks]] (B/M/P) | |||
* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]--> | |||
===QUIC or Multipath QUIC Design=== | |||
* '''New!''' Implement algorithms for improving QUIC or Multipath QUIC performance. (B/M/P, at least familiar with one programming language (eg. [https://github.com/devsisters/libquic C++], [https://github.com/lucas-clemente/quic-go go] or Python).) Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] (Finished) | |||
===Segment Routing based SDN=== | |||
* '''<span style="color:#8B0000">NEW! Winter 2018/2019 </span>''' There are many topics opened for Master and Bachelor theses and projects. Please contact [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] | |||
=== Software Defined Networks (SDN) === | |||
* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details] | |||
* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details] | |||
* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details] | |||
<!--foo | |||
* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai] | |||
* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai] | |||
* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad] | |||
* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad] | |||
* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad] | |||
* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad] | |||
--> | |||
<!--foo | |||
===Network Function Virtualization (NFV) === | |||
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai] | |||
* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai] | |||
* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai] | |||
--> | |||
=== Data Analysis with Bio data === | |||
* '''<span style="color:#8B0000">NEW! 2019 </span>' if you are interested in topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/?q=people/dr-mayutan-arumaithurai Mayutan Arumaithurai] | |||
=== Data Crawling and analysis === | |||
* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P) | |||
* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]) | |||
=== Massive Data Mining and Recommender System=== | |||
* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | |||
* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | |||
* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | |||
* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | |||
=== Social Networking(finished) === | |||
* '''New!''' [[Goettingen Assistant: Android App Development (completed)]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]) | |||
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]) | |||
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]) | |||
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]) | |||
* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here]. | * For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here]. | ||
</noinclude> | </noinclude> | ||