PPT-Failures Guide Probabilistic Search for

a HardtoFind Test Murali Venkatasubramanian Vishwani D Agrawal PhD Candidate James J Danaher Professor. Jørn Vatn. NTNU. jorn.vatn@ntnu.no. Definitions. Failure. In order to define the term ‘failure’, we need first to introduce the term ‘function’. A unit or system (entity) is designed for performing one or more functions. (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. Seventh Framework . Programme. FP7-ICT-2011-7. 2011-2014. http://www.parlance-project.eu. Partners. University of Cambridge. Coordinator: Helen Hastie h.hastie@hw.ac.uk. All of these . skills. will be learned or adapted using real data. 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 & Evaluation. 2. 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. em. or lose. János. . Tóth-Égetö. . / Software Consultant. 2015-04-03. www.softhouse.se. Softhouse Consulting. Are you scared of failures?. Who am I?. What I will talk about. The learnin. g process. 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. 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 . Florin . Dinu. . T. S. Eugene Ng. Computing in the Big Data Era. 2. 15PB. 20PB. 100PB. 120PB. Big Data – Challenging for previous systems. Big Data Frameworks . MapReduce. @ Google. Dryad @Microsoft. In Nontraditional Computing Environments. J. Alex Halderman. CD DRM. 2003, 2005. SDMI. 2001. CSS. 1999. AACS. 2007. Diebold. 2003, 2006. What’s the common “thread”?. Nontraditional Environments. 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 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. Strain Engineering. Mona . Yousofshahi, Prof. Soha Hassoun. Department of Computer Science. Prof. Kyongbum Lee. Chemical & Biological Engineering. Tufts University. 1. Production or overproduction by synthetic pathways . Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .