PPT-On Uncertain Graphs Modeling and Queries

Arijit Khan Systems Group ETH Zurich Lei Chen Hong Kong University of Science and Technology Social Network Transportation Network Chemical Compound Biological Network Graphs are Everywhere. We consider graphs of bounded arboricity ie graphs with no dense subgraphs l ike for example planar graphs Brodal and Fagerberg WADS99 described a very simple line arsize data structure which processes queries in constant worstcase time and performs eduau Todd Mytkowicz Microsoft Research toddmmicrosoftcom Kathryn S McKinley Microsoft Research mckinleymicrosoftcom Abstract Emerging applications increasingly use estimates such as sen sor data GPS probabilistic models machine learning big data and ifilmude Department of Computer Science University of Hong Kong nikoscshkuhk Department of Computer Science and Engineering Hong Kong University of Science and Technology leichencseusthk ABSTRACT Nearest neighbor NN queries in trajectory databases ha brownedu Stan Zdonik Computer Science Department Brown University Providence RI USA sbzcsbrownedu Samuel Madden CSAIL MIT Cambridge MA USA maddencsailmitedu 03 125 11 19 1050 T7 05 58 9 25 1050 T6 10 56 12 7 1049 T5 03 80 10 19 1050 T4 04 110 9 25 10 on . Evolving Graphs Research. Speaker: . Chenghui. . Ren. Supervisors: Prof. Ben Kao, . Prof. . David Cheung. 1. Motivation. Evolving graphs are everywhere. Social networks. Users join social networks. School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data. Arijit Khan, . Yinghui. Wu, Xifeng Yan. Department of Computer Science. University of California, Santa Barbara. {. arijitkhan. , . yinghui. , . xyan. }@. cs.ucsb.edu. Graph Data. 2. Graphs are everywhere.. School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. 2. Methods based on k-anonymity. k-candidate. k-degree. k-neighborhood. k-. automorphism. Publishing Social Graphs. 3. k-candidate Anonymity. M Hay et al. , . Resisting Structural Re-identification in Anonymized Social Networks. 指導教授：陳良弼 老師. 報告者：鄧雅文 . 97753034. Introduction. Related Work. Problem Formulation. Future Work. Outline. Top-. k. query on certain data. Rank results according to a user-defined score. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data.