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. Query Formulation. Ling573. NLP Systems and Applications. April 14, 2011. Roadmap. Motivation:. Retrieval gaps. Query F. ormulation:. Question Series. Query reformulation:. AskMSR. patterns. MULDER parse-based formulation. 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. Similarity Search. Alex . Andoni. (Columbia University). Nearest Neighbor Search (NNS). Preprocess: . a set . . of points. Query:. given a . query point . , report a point . with the smallest distance to . 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 . 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 . Database System Implementation CSE 507. Some slides . adapted from . S. ilberschatz. , . Korth. and . Sudarshan. Database System Concepts – 6. th. Edition.. And . Elamsri. and . Navathe. , Fundamentals of Database Systems – 6. 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. Query Processing. Document-at-a-time. Calculates complete scores for documents by processing all term lists, one document at a time. Term-at-a-time. Accumulates scores for documents by processing term lists one at a time.