PPT-Indexing Correlated Probabilistic Databases

Bhargav Kanagal amp Amol Deshpande University of Maryland Introduction Correlated Probabilistic data generated in many scenarios Data Integration AFM06 Conflicting information best captured using mutual exclusivity. This paper addresses an indexing scheme capable of e64259 ciently processing range queries in a largescale trajectory database Af ter discussing the drawbacks of previous indexing schemes we propose a new scheme that divides the temporal dimension i databases. ESRC Research Methods Festival. St. Catherine’s College, Oxford, July 9 2014. Rob Newman (Product Manager). Rebecca Ursell (Alliance Manager). … . or, why A&I services are important to your research, and how you can make the most of them. BioASQ. Workshop. September 27, 2013. Alan R. Aronson. Lister Hill Center, US National Library of Medicine. alan@nlm.nih.gov. The views and opinions expressed do not necessarily state or reflect those of the U.S. Government, and they may not be used for advertising or product endorsement purposes.. School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. 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 . Goals:. Store large files. Support multiple search keys. Support efficient insert, delete, and range queries. 2. Files and Indexing. Entry sequenced file. : Order records by time of insertion.. Search with sequential search. on. NoSQL. Databases. (. MongoDB. ). By:. . Avni. Malhan (MT15012). . Karishma. . Tirthani. (MT15027). . Neeti. . Arora. (MT15039). What is . NoSQL. ?. NoSQL Data models. MongoDB. : Brief Overview. 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. 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. Current Status and Role in Improving Access. to Biomedical Information. A Report to the Board of Scientific Counselors. April 5, 2012. Alan R. Aronson . (Principal Investigator). James G. . Mork. 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.