PPT-Creating Probabilistic Databases

from IE Models Olga Mykytiuk 21 July 2011 MTheobald Outline Motivation for probabilistic databases Model for automatic extraction Different representation Onerow model Multirow model . (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. 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. Jane Long. MLIS, University of Oklahoma. MA, Wright State University. Reference Services Librarian. Al Harris Library . jane.long@swosu.edu. How do I get started?. 1. . Keywords. 2. Boolean Operators. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. 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. An . entity. is anything about which the organization wishes to store data. At your college or university, one entity would be the student.. STUDENTS. Student ID. Last Name. First Name. Phone Number. Distinguish between the physical and logical views of . data.. Describe how data is organized: characters, fields, records, tables, and . databases.. Define key fields and how they are used to integrate data in a database.. presentation and training. Databases. Databases contain information gathered from thousands of scholarly journals, books, book series, reports, conferences, and more. . Databases can be used for narrowing/ enlarging the research topic, verifying citations, and protocols/ patent search. . ~Max Frisch. (Swiss playwright, novelist, philosopher, social critic). Contents. Citing Databases. General Info. MLA. APA. Visuals—Internal Citations. MLA. APA. Visuals—Bibliographic Citations. MLA. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . 17. Victor Matos. Cleveland State University. Notes are based on: . Android Developers . http://developer.android.com/index.html. 2. 17. Android – SQL Databases. 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.