Finding patterns and insights in data
Our work in this area focuses on solving key problems in database architecture and information management. Our current areas of focus are infrastructure for large-scale database systems; reducing the total cost of ownership of information management; enabling flexible ways to query, browse, and organize rich data sets containing both structured and unstructured data; and the management of database schemas and mappings.
Jianpeng Cheng, Zhongyuan Wang, Ji-Rong Wen, Jun Yan, and Zheng Chen, Contextual Text Understanding in Distributional Semantic Space, in ACM International Conference on Information and Knowledge Management (CIKM), ACM – Association for Computing Machinery, October 2015.
Zhongyuan Wang, Haixun Wang, Ji-Rong Wen, and Yanghua Xiao, An Inference Approach to Basic Level of Categorization, in ACM International Conference on Information and Knowledge Management (CIKM), ACM – Association for Computing Machinery, October 2015.
Yu Zheng, Trajectory Data Mining: An Overview, in ACM Transaction on Intelligent Systems and Technology, ACM – Association for Computing Machinery, September 2015.
Yu Zheng and Senior Member, Methodologies for Cross-Domain Data Fusion: An Overview, in IEEE Transactions on Big Data, IEEE – Institute of Electrical and Electronics Engineers, September 2015.
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|Data mining, management and computational ecology|
- Data Science at Microsoft
- Microsoft Academic Graph
- Concept Expansion
- ATL Cairo GPSP - Projects Ideas
- Urban Air
- Crowdsourcing and Human Computation
- Rethinking Eventual Consistency
- SQLVM: Performance Isolation in Multi-Tenant Relational Database-as-a-Service
- Hyder, a transactional indexed-record manager for shared flash
- Tools for Software Engineers
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28 August 2015
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27 August 2015
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24 August 2015
- Open source software from Microsoft for academics
20 August 2015