PPT-Top- k Queries on Uncertain Data

指導教授陳良弼 老師 報告者鄧雅文 97753034 Introduction Related Work Problem Formulation Future Work Outline Top k query on certain data Rank results according to a userdefined score. brownedu Stan Zdonik Computer Science Department Brown University Providence RI USA sbzcsbrownedu Samuel Madden CSAIL MIT Cambridge MA USA maddencsailmitedu 03 125 11 19 1050 T7 05 58 9 25 1050 T6 10 56 12 7 1049 T5 03 80 10 19 1050 T4 04 110 9 25 10 Software-Defined Networks. Srinivas Narayana. Jennifer Rexford and David Walker. Princeton University. Where’s the packet loss?. A. B. Faulty network device(s) along the way. But where?. 2. 100 . pkts. Doug Burdick, Prasad Deshpande, T. S. . Jayram. , . Raghu . Ramakrishnan. , . Shivakumar. . Vaithyanathan. Presented by . Raghav. . Sagar. OLAP Overview. Online Analytical Processing (OLAP). Interactive analysis of data, allowing data to be summarized and viewed in different ways in an online . Roger Levy. UC San Diego. LSA Institute 2011. 26 . July 2011. Garden Paths: What we don’t understand so well. In previous days we covered “ideal-observer” models of syntactic comprehension. Today we’ll add nuance to this picture. Software-Defined Networks. Srinivas Narayana. Jennifer Rexford and David Walker. Princeton University. Where’s the packet loss?. A. B. Faulty network device(s) along the way. But where?. 2. 100 . pkts. SPARQL Endpoints. Dilshod IBRAGIMOV, KATJA HOSE, TORBEN BACH PEDERSEN, ESTEBAN ZIM. ÁNYI.. Earthquake . in the . Pacific in March 2011 tsunami a . Finding Top . k. Most Influential Spatial Facilities over Uncertain Objects. Liming Zhan. Ying Zhang . Wenjie. Zhang . Xuemin. Lin. The . University of New South Wales, . Australia. Outline. Motivation. . over. . Incomplete. Databases. Werner . Nutt. j. oint. . work. . with. Marco . Montali. , Sergey . Paramonov. , Simon . Razniewski. , . Ognjen. . Savkovic. , . Alex . Tomasi. , . Fariz. . Darari. Wherever you roam. And admit that the waters. Around you have grown. And accept it that soon. You'll be drenched to the bone.. If your time to you. Is worth savin'. Then you better start swimmin'. Or you'll sink like a stone. 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 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. Wherever you roam. And admit that the waters. Around you have grown. And accept it that soon. You'll be drenched to the bone.. If your time to you. Is worth savin'. Then you better start swimmin'. Or you'll sink like a stone. 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. Dr. Sonalika's Eye Clinic in Pune is a top choice for individuals in need of exceptional ophthalmologists and eye clinics. They have multiple convenient locations throughout the city, including Hadapsar, Amanora,