PPT-Map-Reduce and the New Software Stack

Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users of these slides We would be delighted if you found this our material useful in giving your own lectures Feel free to use these slides verbatim or to modify them to fit your own needs. for Security Bugs. AppSecUSA. New York City 2013. ME?. Simón Roses Femerling. Founder & CEO, VULNEX . www.vulnex.com. Blog. : . www.simonroses.com. Twitter: . @. simonroses. Former Microsoft, PwC, @Stake. ABHISHEK REDDY PAM (11cs30002). date . : . 07/10/2013. Viable Prefix. The set of prefixes of right sentential forms that can appear on the stack of a shift-reduce parser are called viable prefixes.. Approaches to Preserve Computational Capabilities. Todd King. Why Archive Software. Developed software is . a . large investment . and . worthy of preservation. . But…. The . long term preservation of software . Prof. Dr. Bertrand Meyer. Lecture 2: Modularity and Abstract Data Types. Reading assignment for this week. OOSC, chapters. 3: Modularity. 6: Abstract data types. In particular pp.153-159,. . Contracts, Trusted Components. and . Patterns. Bertrand Meyer. Manuel . Oriol. Till Bay. ETH, Fall . 2008. Chair of Software Engineering. 2. Today & next lecture. Aims of the course. Introduction to issues of software quality. Thilina Gunarathne (. tgunarat@indiana.edu. ). Judy . Qiu (. xqiu@indiana.edu. ). Dennis . Gannon (. dennis.gannon@microsoft.com). Introduction. Three disruptions. Big Data. MapReduce. Cloud Computing. Top-down vs. bottom-up parsing. Top-down . vs. bottom-up . parsing. Ex. Ex. Ex. Ex. +. Nat. *. Nat. Nat. Ex. Ex. . . . Nat. | . (. Ex. ). | . Ex. . +. . Ex. | . Ex. . *. . Ex. Matched input string. David Brumley. dbrumley@cmu.edu. Carnegie Mellon University. Basic Execution Model. 2. Process. Memory. Stack. Heap. Processor. Fetch, decode, execute. read and write. Code . Data. .... cdecl. . –. CSCI-GA.2590. Ralph . Grishman. NYU. Taking Stock. For information extraction, we now have. POS tagger. name tagger. NP . chunker. with semantic classifier. we can now write semantic patterns to find particular relationships. Thilina Gunarathne (. tgunarat@indiana.edu. ). Judy . Qiu (. xqiu@indiana.edu. ). Dennis . Gannon (. dennis.gannon@microsoft.com). Introduction. Three disruptions. Big Data. MapReduce. Cloud Computing. Top-down versus Bottom-up Parsing. Top down:. Recursive descent parsing. LL(k) parsing. Top to down and leftmost derivation . Expanding from starting symbol (top) to gradually derive the input string. Data-Driven Dependency Parsing Kenji Sagae CSCI-544 Background: Natural Language Parsing Syntactic analysis String to (tree) structure He likes fish PARSER S NP VP NP V N Prn He likes fish Data-Driven Dependency Parsing Kenji Sagae CSCI-544 Background: Natural Language Parsing Syntactic analysis String to (tree) structure He likes fish PARSER S NP VP NP V N Prn He likes fish Team Members:. Vinti (vv2236). Garvit. Singh (gs2731). Pramod. . Nayak. (ppn2106). Vidhatre. . Gathey. (vvg2111). Contents. Introduction: Go back N protocol. Algorithm. Software Modules. Terminating Conditions.