PPT-Data & Query Anonymization

George Danezis gdanezisuclacuk With help from Luca Melis lucamelis14uclacuk Steve DodierLazaro sdodierlazaro12uclacuk Why anonymize data Raw data use cases. Group 4: . Nipun . Garg, Surabhi . Mithal. http://www-users.cs.umn.edu/~smithal/. Chapter Organization. 2. OLD Organization. 5.1 . Evaluation of Spatial Operations . 5.2 . Query Optimization . 5.3 . Michael . Armbrust. , Kristal Curtis, Tim . Kraska. Armando Fox, Michael J. Franklin, David A. Patterson. AMP Lab, EECS, UC Berkeley. Introduction. Large-scale websites are increasingly moving from relational databases to distributed key-value stores.. CSE 8330. Instructor: . Dr.Margaret. H. Dunham. Presenter: . Akshaya. . Aradhya. Introduction. Query optimization in XML databases. Query optimization in Parallel databases. Comparison. Conclusion and Future work. Stefan . Riezler. Yi Liu. Google. 2010 Association for Computational Linguistics. Introduction. Create a system that learns to generate query rewrite from a large amount of user query logs.. Use query expansion in Web search for evaluation of rewritten queries.. Sam Weber. Software Engineering Institute, CMU. Thesis. Laws and regulations concerning data sharing and privacy often have unintended consequences. Problem Space. Three dimensions of issue:. What is ethical?. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Jennifer Rexford. Princeton University. http://. www.cs.princeton.edu. /~. jrex. Joint work with . Srinivas. . Narayana. , Mina . Tahmasbi. , and David Walker. To appear in NSDI’16: . http://. www.cs.princeton.edu. in a Data Stream Management . System. Introduction. Two . fundamental . differences between DSMS and DBMS. In . addition to managing traditional stored data . such as . relations, a DSMS must handle multiple . CSE 8330. Conventional Query Processing. Statistics generation. Query optimization. Query execution. Unreliable cardinality . e. stimates. Complex queries. Changes in the runtime environment. Query interactivity. Standard Database Query Languages. Outline. What is a Query? Query Language. ?. Example Database Tables. SQL Overview: 3 Components. SELECT statement with 1 table. Multi-table SELECT statements. Why spatial extensions are needed?. Kristina . Lerman. What can we learn from web search queries?. Characteristics. Length has steadily grown over the years. 1990’s: < 2 terms. 2001: 2.4 terms. 2014: long search queries, e.g., “where is the nearest coffee shop”. Introduction to power query A.K.A The M Language About me BI Consultant Went from SQL Newbie to SQL Pro in the last 7 years. Experience with T-SQL, SSRS, and Power BI. Pluralsight Author Author of video training on Pluralsight. Currently focusing on Power BI content. Bhavana. . ganne. – 20. Srilekha. . vuyurru. – 21. Prasanna Kumar reddy . kukkala. – 22. Krishna . teja. . talasila. - 19. GROUP - 5. [226 – 235]. Agenda. Query Decomposition. Data Localization . of sequences and its Application to Mobility Data Mining. Ruggero. . Pensa. , . Anna . Monreale. , Fabio . Pinelli. and Dino . Pedreschi. PILBA’0. 8 - . Workshop. , Oct . 9, 200. 8. , Malaga. Knowledge Discovery and Delivery Lab.