PPT-Approximate Lineage for Probabilistic Databases

Christopher Ré and Dan Suciu University of Washington Approximate Lineage in One Slide Lineage Provenance In QP used to track correlations Explain queryview results VLDBs have lots of lineage. (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. 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. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. the misconception that some organisms today are “primitive”…. Even David Attenborough is messing with us!. http://www.youtube.com/watch?v=VomgnJRRZyA. Start at 6:30. From Prothero, 2007, Evolution, What the Fossils Say and Why It Matters. . – What Next?. Martin Theobald. University of Antwerp. Joint work with . Maximilian Dylla, Sairam Gurajada, Angelika . Kimmig. , Andre . Melo. , Iris Miliaraki, . Luc de . Raedt. , Mauro . Sozio. lineaging. ). Acetree. QC . tools: . Edit->Quality Control->. Deaths/Adjacencies . ‘Deaths’ lists all track terminations. Check all deaths have expected morphology. ‘Jumps’ lists all links that make a large jump. With tracking off traverse . Andrew B. Kahng, . Seokhyeong Kang . VLSI CAD LABORATORY, . UC. San Diego. 49. th. Design Automation Conference. June 6. th. , 2012. Outline. Background and Motivation. Accuracy Configurable Adder Design. University of Washington. Adrian Sampson, . Hadi. Esmaelizadeh,. 1. Michael . Ringenburg. , . Reneé. St. Amant,. 2. . Luis . Ceze. , . Dan Grossman. , Mark . Oskin. , Karin Strauss,. 3. and Doug Burger. 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 . Computation Circuits. Wei-Ting Jonas Chan. 1. , Andrew B. Kahng. 1. , . Seokhyeong Kang. 1. , . Rakesh. Kumar. 2. , and John Sartori. 3. 1. VLSI . CAD LABORATORY, . UC San Diego. 2. PASSAT GROUP, Univ. of Illinois. 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. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. 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.