PPT-System Aspects of Probabilistic DBs

Part II Advanced Topics Magdalena Balazinska Christopher Re and Dan Suciu University of Washington Recap of motivation Data are uncertain in many applications Business Dedup Info Extraction. (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. Magdalena . Balazinska. ,. Christopher . Ré. . and Dan . Suciu. University of Washington. One slide overview of motivation. Data are . uncertain. in many applications. Business: . Dedup. , Info. Extraction. 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. a Probabilistic . Lexical . Inference System. . Eyal Shnarch. ,. . Ido . Dagan, Jacob . Goldberger. PLIS - Probabilistic Lexical Inference System. 1. /34. The . entire talk in a single sentence. 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. 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. Andrea . Molod . GMAO Staff. GEOS FP system major upgrade on . January 24, . 2017. : . e5131fp . . f516fp. T. ransition . from . Hybrid 3D-Var to Hybrid 4D En-. Var. . assimilation . system. Doubled horizontal resolution from ~25 km to ~12.5. (November 16, PLWG). Contents. Introduction. Deterministic Vs Probabilistic. Applications . Efforts and Issues. Next Steps & Summary. Appendices. 2. Introduction. Motivation. Increasing uncertainties (intermittent generation, . 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 . 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 . Magdalena . Balazinska. ,. Christopher . Ré. . and Dan . Suciu. University of Washington. One slide overview of motivation. Data are . uncertain. in many applications. Business: . Dedup. , Info. Extraction. 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. 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. 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 .