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. 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 . Embedded CPU-GPU Architectures. Xuntao Cheng. , Bingsheng He, Chiew Tong Lau. Nanyang Technological University, Singapore. 1. Outline. Motivations. System Design. Evaluations. Conclusion. 2. Query Processing in the Era of IoT. 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.. 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. 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 . 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. Dr. Susan Gauch. Query Term Weights. . The vector space model matches queries to documents with the inner product/cosine similarity measure. Query vector * Document vector (inner product). Normalized_q_vector. 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). Bhargav Kanagal. Amol Deshpande. University of Maryland. Motivation: Information Extraction/Integration. [Gupta&Sarawagi’2006, . Jayram et al. 2006. ]. Structured entities extracted from text in the internet. 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. Arizona Corporation Commission Additional time above and beyond the processing time may be required for returning examined documents to customers 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.