PPT-Probabilistic Consensus Ali

Ghodsi UC BerkeleyKTH alig at csberkeleyedu 42413 Ali Ghodsi aligatcsberkeleyedu 2 Impossibility of Consensus We know there exists an infinite execution in any consensus algorithm for asynchronous networks that tolerates . (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. Ken Birman. Fall 2012. Consensus… a classic problem. Consensus abstraction underlies many distributed systems and protocols. N processes. They start execution with inputs.  {0,1}. Asynchronous, reliable network. 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. A Tool for Teams. Sister Singers Festival. July 25, 2014. Presenter: Mindy Taylor. Session Overview. Become familiar with a group process tool called Consensus Validation. Learn the steps in doing Consensus Validation. 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. A Tool for Teams. GALA Leadership Symposium. October 11, 2013. Presenter: Mindy Taylor. Session Overview. Become familiar with a group process tool called Consensus Validation. Learn the steps in doing Consensus Validation. When you can’t agree to disagree. Consensus. Why do applications need consensus?. What does it mean to have consensus?. Consensus = consistency. Assumptions. Processes can choose values. Run at arbitrary speeds. 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 . Terri Giordano MSN, CRNP, CORLN. Division of Otolaryngology. The Children’s Hospital of Philadelphia. Tracheosto. my Care. Systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances. 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 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. for Replicated Logs. Diego Ongaro and John Ousterhout. Stanford University. Replicated log => . replicated state . machine. All servers execute same commands in same . order. Consensus module ensures proper log replication. 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 .