PPT-Top-K Query Evaluation on Probabilistic Data

Christopher Ré Nilesh Dalvi and Dan Suciu University of Washington Evaluating Complex SQL on PDBs 2 1282006 High Level Overview DBMS Precise answers over clean data Data are often imprecise. SUM 2013. Batya. . Kenig. Avigdor. Gal. Ofer. . Strichman. Probabilistic Databases for managing uncertain data. A variety of data sources generate incomplete, noisy and uncertain data (sensor networks, information extraction, data integration…). . Bhargav Kanagal & Amol Deshpande. University of Maryland. Introduction. Correlated Probabilistic data generated in many scenarios. Data Integration [AFM06]: Conflicting information best captured using “mutual exclusivity”. 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. M. ultiple . A. lternative . R. ectifications in . Data Cleaning. Preet. . Inder. Singh . Rihan. . Master’s Thesis. 1. Committee Members. Dr. . Subbarao. . Kambhampati. (Chair). Dr. . Huan. . Prithviraj Sen Amol Deshpande. outline. General Info. Introduction. Independent tuples . model. Tuple . correlations. Representing Dependencies. Query . evaluation. Experiments. Conclusions & Work to be done. . Models. . 1. Overview. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Binary Independence . Model. Bayesian Model. 2. Outline. . Probabilistic Approach to Retrieval. . Basic Probability Theory. 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. . – 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. 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 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. 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.