PPT-DQR: A Probabilistic Approach to Diversified Query Recommen

Ruirui Li Ben Kao Bin Bi Reynold Cheng Eric Lo Speaker Ruirui Li 1 The University of Hong Kong Outline Motivation Problem Statement DQR Model Experiments amp Evaluation 2. Query Semantics:. (“Marginal Probabilities”). Run query Q against each instance . D. i. ; for each answer tuple t, sum up the probabilities of all instances . D. i. where t exists.. A probabilistic . 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”. Yuichi Iijima and . Yoshiharu Ishikawa. Nagoya University, Japan. Outline. Background and Problem Formulation. Related Work. Query Processing Strategies. Experimental Results. Conclusions. 1. 2. Imprecise. 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. (100% EQ / Alts). Core Fixed Income. (100% FI / Alts). Total Yield. (S-T Bond). Real Balanced Growth & Income. (60% FI / 40% EQ / Alts). Real Wealth Accumulation. (40% FI / 60% EQ / Alts). Aggressive Real Growth. Presented by Camilla Leigh . Founding Partner, . Philanthropica. May 20, 2017. Show of hands…. Organization Mission. , Vision & Fundraising. The reason for asking!. Identifies who to ask. Illustrates the impact giving will have. 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 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. Jeffrey . Xu. Yu (. 于旭. ). Department of Systems Engineering and Engineering Management. The Chinese University of Hong Kong. yu@se.cuhk.edu.hk. Books on Social Networks. Social and Economic Networks.