PPT-Materialized Views in Probabilistic Databases

for Information Exchange and Query Optimization Christopher Re and Dan Suciu University of Washington 1 Motivating Example Optimization materialized but imprecise view Similarity between users 8M. com surajitcmicrosoftcom viveknarmicrosoftcom Abstract Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a nontrivial task A judicious choice must be costdriven and influenced by the workload experience Seminar: Transaction Processing (Bachelor). SS 2009. Dennis . Stratmann. Outline. Goal and Overview. Examine Three Approaches to CC on RD:. 1. Predicate-Oriented Concurrency Control. 2. Relational Update Transactions. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. Frederic Murray. Assistant Professor . MLIS, University of British Columbia. BA, Political Science, University of Iowa. . Instructional Services Librarian. Al Harris Library . frederic.murray@swosu.edu. Todd J. Green. Zachary G. Ives Val Tannen. University of Pennsylvania. . March 24, 2009. @ ICDT 09, Saint Petersburg. Change is a Constant in Data Management. Databases are highly . dynamic. ; many kinds of . Materialized Instances After Changes to Mappings and Data. Todd J. Green. Zachary G. Ives. ICDE ’12 Washington, DC April 2, 2012. &. Change is a Constant in Data Management. Databases are highly . Asterios Katsifodimos. 1. , Ioana Manolescu. 1. & . Vasilis. . Vassalos. 2. 1. Inria . Saclay. & . Université. Paris-. Sud. , . 2. Athens . University of Economics and Business. Athens University of . Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. DATABASES. What do you think the word Database means?. DEFINITION:. A database is a collection of data or. information which is stored in a . logical and . structured way. Paper Based Databases. Paper Based Databases. Sanjay . Agrawal. . Surajit. . Chaudhuri. . Vivek. . Narasayya. Hasan Kumar Reddy A (09005065). 1. Outline. Motivation. Introduction. Architecture. Algorithm. Candidate Selection. Configuration Enumeration. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. What are databases?. Databases are a collection tables that are searchable. These databases are filled with periodical materials (newspapers, journals, magazines, and even blog posts). These databases prevent you from having to go from journal to journal by hand. Chapter 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .