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. 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…). . (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. Yuichi Iijima and . Yoshiharu Ishikawa. Nagoya University, Japan. Outline. Background and Problem Formulation. Related Work. Query Processing Strategies. Experimental Results. Conclusions. 1. 2. Imprecise. We currently serve more than 60 container lines across our facilities world-wide. . Our key Client Program is structured around 26 Global Customers covered by 10 Key Account Managers who have regular meetings at the CEO level. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. 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. . 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. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Access to Truly Diversified Real Estate Opportunities. Risk Disclosures. Risk Disclosures. Why Real Estate? . Accessing Institutional Real Estate. Quarterly Distributions…. …With Low to Moderate Volatility. 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 . ISPMNA 2019. Mike Martin, Engineering Associate Fellow – Structural Integrity. 22. nd. October 2019. Introduction and Context. 01. Target Reliability Approach. 02. Working Principles and Methods. Summary and Next Steps. 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.