PPT-Part 4: Data Dependent Query Processing Methods

Yin David Yang Zhenjie Zhang Gerome Miklau Prev Session Marianne Winslett Xiaokui Xiao 1 What we talked in the last session Privacy is a major concern in data publishing. A. pproximate . N. ear . N. eighbors. Alexandr Andoni . (Simons Inst. . /. . Columbia). Ilya Razenshteyn . (MIT, now at IBM . Almaden. ). Near Neighbor Search. Dataset: . points in . , . Goal: . a data point within . Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Speaker: . Rui-Rui. Li. Supervisor: Prof. Ben Kao. 1. Outline. Introduction. Motivation. Methods. -using click-through data. -using session data. -context aware query suggestion. -other methods. Conclusion. Magdalena . Balazinska. ,. Christopher . Ré. . and Dan . Suciu. University of Washington. One slide overview of motivation. Data are . uncertain. in many applications. Business: . Dedup. , Info. Extraction. Abstract. With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed.. Chapter 1. The Worlds of Database Systems. Section 1.1 . Evolution of Database Systems . The DBMS is expected to :. 1 . Allow users to create new databases and specify their schemas ( logical structure of the data ) , using a specialized data - definition language.. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. Performance tuning SAP NetWeaver BW 7.x. Dr. Bjarne Berg . 2. Agenda. . Background. The Right Design. InfoCubes, DSOs and MultiProviders. Performance at All Levels. Query Design and BOBJ Tips . John . Sirmon. Senior Escalation Engineer – Microsoft Corporation. Microsoft CSS at PASS 2009. Session Objectives And Takeaways. Processing issues . When good cubes go bad. Troubleshooting processing simplified . of Massive Trajectory Data based on MapReduce. Qiang. Ma, Bin Yang (. Fudan. University). Weining. . Qian. , . Aoying. Zhou (ECNU). Presented By: . Xin. Cao (Aalborg University). Outline. Introduction . CSE 8330. Conventional Query Processing. Statistics generation. Query optimization. Query execution. Unreliable cardinality . e. stimates. Complex queries. Changes in the runtime environment. Query interactivity. L. ocality-. S. ensitive . H. ashing. Alexandr . Andoni. . (Columbia). Ilya Razenshteyn . (MIT CSAIL). Near Neighbor Search. Dataset: . points in . , . Goal: . a data point within . from a query. Space, query time. Divya Spandana . Marneni. Agenda. What is Big Data. Big Data and image processing. Why to analyze big images. Complexity involved in processing. Hadoop Image processing framework. Image Retrieval in big data.