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. eduau Todd Mytkowicz Microsoft Research toddmmicrosoftcom Kathryn S McKinley Microsoft Research mckinleymicrosoftcom Abstract Emerging applications increasingly use estimates such as sen sor data GPS probabilistic models machine learning big data and 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 joint work with Dan . Suciu. (University of Washington). Alexandra Meliou. Hypothetical (What-if. ). Queries. Brokerage company. DB. Key Performance Indicators (KPI). Example from [. Balmin. et al. VLDB’00]:. 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 . Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data. Arijit Khan. Systems Group. ETH Zurich. Lei Chen. Hong . Kong University of Science and Technology. Social Network. Transportation Network. Chemical Compound. Biological Network. Graphs are Everywhere. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. 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 . 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. 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 . Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data. 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.