PPT-Lineage Processing over Correlated Probabilistic Databases

Bhargav Kanagal Amol Deshpande University of Maryland Motivation Information ExtractionIntegration GuptaampSarawagi2006 Jayram et al 2006 Structured entities extracted from text in the internet. 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”. Christopher . Ré. and Dan . Suciu. University of Washington. Approximate Lineage in One Slide. Lineage (Provenance) . In QP used to track correlations. Explain query/view results. VLDBs have lots of lineage. Asma. . Souihli. Oct. . 24. th. . 2012. Network and Computer Science . Department. XML. for semi-structured . data (. tree-like. structure). 2. Probabilistic Data - . PrXML. Jung-. Hee. Yun and Chin-Wan Chung, 2012.. Prithviraj Sen Amol Deshpande. outline. General Info. Introduction. Independent tuples . model. Tuple . correlations. Representing Dependencies. Query . evaluation. Experiments. Conclusions & Work to be done. Meng Yang. Phonetics Seminar. March 7, 2016. The Plan. Background: . C. ue weighting and cue shifting. Theories and predictions. My research questions. Methods (brace yourselves…). Results (yay!). Discussion. from. IE Models . Olga . Mykytiuk. , 21 July 2011. M.Theobald. Outline . Motivation for probabilistic databases. Model for automatic extraction. Different representation . One-row model. Multi-row model . . – 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 . Meng Yang. Phonetics Seminar. March 7, 2016. The Plan. Background: . C. ue weighting and cue shifting. Theories and predictions. My research questions. Methods (brace yourselves…). Results (yay!). Discussion. Life Without Databases: Lists. Lists are often sufficient for simple tasks. Not appropriate for complex information. Multiple lists lead to. Data redundancy. Data inconsistency. Duplicate data. Sorting issues . 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.. Chapter 2: . Data . Uncertainty Model. 2. Objectives. In this chapter, you will:. Learn the formal definition of uncertain data. Explore different granularities of data uncertainty. Become familiar with different representations of uncertain data. 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.