PPT-Querying Probabilistic XML Databases

Author : alida-meadow | Published Date : 2016-06-25

Asma Souihli Oct 24 th 2012 Network and Computer Science Department XML for semistructured data treelike structure 2 Probabilistic Data PrXML Jung Hee Yun

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Querying Probabilistic XML Databases: Transcript


Asma Souihli Oct 24 th 2012 Network and Computer Science Department XML for semistructured data treelike structure 2 Probabilistic Data PrXML Jung Hee Yun and ChinWan Chung 2012. 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. Hsuan-Heng , Wu . Shawn Ju. XML V.S. HTML. XML is designed to describe data. XML don’t use predefined tags. XML is used to exchange data between applications. Tree Structure but allow Reference to other node. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Abstract. A large number of organizations today generate and share textual descriptions of their products, services, and actions. Such collections of textual data contain significant amount of structured information, which remains buried in the unstructured text. While information extraction algorithms facilitate the extraction of structured relations, they are often expensive and inaccurate, especially when operating on top of text that does not contain any instances of the targeted structured information. . Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Prithviraj Sen Amol Deshpande. outline. General Info. Introduction. Independent tuples . model. Tuple . correlations. Representing Dependencies. Query . evaluation. Experiments. Conclusions & Work to be done. XML: a "skeleton" for creating markup languages. you already know it!. syntax is identical to XHTML's:. <. element. . attribute. ="value">content</. element. >. languages written in XML specify:. Service-Oriented Computing: . Semantics, Processes, Agents. – Munindar P. Singh and Michael N. Huhns, Wiley, 2005. Appendix A. 2. Service-Oriented Computing: Semantics, Processes, Agents . - Munindar Singh and Michael Huhns. Dr. . Qusai. . Abuein. June, 2012. XML Dr. Qusai Abuein. 1. Part (I). XML Basics. What is XML?. XML . stands for . EXtensible. Markup Language. XML is a markup language much like HTML. XML was designed to carry data, not to display data. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. kindly visit us at www.examsdump.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. Professionally researched by Certified Trainers,our preparation materials contribute to industryshighest-99.6% pass rate among our customers. kindly visit us at www.examsdump.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. Professionally researched by Certified Trainers,our preparation materials contribute to industryshighest-99.6% pass rate among our customers. CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access).

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