PPT-Query Processing

A query is stated using SQL or other languages ideally natural languages After query parsing each query is essentially treated as a relational algebra expression Query optimizer Enumerates the possible plans to evaluate expression. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Formulation & Expansion. Ling573. NLP Systems and Applications. May 2, 2013. Deeper Processing for Query Formulation. MULDER (Kwok, . Etzioni. , & Weld). Converts question to multiple search queries. 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.. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Formulation & Expansion. Ling573. NLP Systems and Applications. May 2, 2013. Deeper Processing for Query Formulation. MULDER (Kwok, . Etzioni. , & Weld). Converts question to multiple search queries. IDOL Under . the . Hood. Techniques and algorithms. #. SeizeTheData. Read the presenter notes before presenting.. Most of the content of this presentation is contained therein.. IDOL knowledge flow. Acquisition. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. 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 . Deliverable #2. Jonggun Park . Haotian. He. Maria . Antoniak. Ron Lockwood. System architecture. Two modules:. Indexing. Querying . query processing. p. assage retrieval. answer processing/ranking. CSE 8330. Conventional Query Processing. Statistics generation. Query optimization. Query execution. Unreliable cardinality . e. stimates. Complex queries. Changes in the runtime environment. Query interactivity. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Query optimization. :. The process of choosing a suitable execution strategy for processing a query.. Two internal representations of a query:. Query Tree. Query Graph. Introduction to Query Processing (2). 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.