PPT-ICICLES: Self-tuning Samples for Approximate Query Answerin

By Venkatesh Ganti Mong Li Lee and Raghu Ramakrishnan CSE6339 Data exploration Raghavendra Madala In this presentation Introduction Icicles Icicle Maintenance IcicleBased Estimators. 49 BelAir 103 Levis Qc Canada G6V 6K9 mrueltopcontrolcom Keywords Process Control Optimization Oscilla tion Model PID Control Closed L oop Open Loop Stiction Backlash Non linearity Pseudorandombinary Sequence Generalized Binary Noise ABSTRACT Tradit Every SQL Programmer Should Know. Kevin Kline. . Director of Engineering Services at SQL . Sentry. . Microsoft MVP since 2003. . Facebook, LinkedIn, Twitter at KEKLINE. . KEKline@sqlsentry.com. . compound subjects and compound predicates #2. A compound predicate (or compound verb) is made up of two or more verbs or verb phrases that are joined by a conjunction and have the same subject. The diagram has a fork in the baseline at the right (verb) side of the vertical line.. Sulabha Balan. ICICLES: Self-tuning Samples for Approximate Query Answering. By . Venkatesh. . Ganti. , . Mong. Li Lee, and . Raghu. . Ramakrishnan. CSE 6339 . Introduction. Icicles. Icicle Maintenance. Hardware: Challenges and Opportunities. Author. : Bingsheng He. (Nanyang Technological University, Singapore) . Speaker. : . Jiong . He . (Nanyang Technological University, Singapore. ). 1. What is Approximate Hardware?. By . Venkatesh. . Ganti. , . Mong. Li Lee, and . Raghu. . Ramakrishnan. Shruti. P. . Gopinath. CSE 6339 . Outline. Introduction . and background. Icicles. Icicle Maintenance. Icicle-Based Estimators. Davide Mottin, Alice . Marascu. , . Senjuti. . Basu. Roy. Gautam. Das, Themis . Palpanas. , . Yannis. . Velegrakis. Talk by Davide Mottin at Yahoo! Research Barcelona. Who am I?. Born. in Marostica. Mehrzad Samadi. 1. , Janghaeng Lee. 1. , D. Anoushe Jamshidi. 1. , Amir Hormati. 2. , and Scott Mahlke. 1. University of Michigan. 1. , Google . Inc.. 2. December 2013. Compilers creating custom processors. Ad-hoc. BigData Queries. Srikanth Kandula. , Anil Shanbhag, Aleksandar Vitorovic, Matthaios Olma,. Robert Grandl, Surajit Chaudhuri, Bolin Ding. Motivation: Approximating Big-Data Queries. Data-Analysis Clusters. Chairs. IETF 79. 1. IGMP/MLD tuning milestone. Are we ready to adopt one of these drafts?. draft-asaeda-multimob-igmp-mld-optimization-04.txt. . draft-wu-multimob-igmp-mld-tuning-03.txt . More specific questions follow. The Tuning Process. Benefits of Tuning. Why Tuning is Different. What is Tuning?. A collaborative, faculty-driven . process that . “harmonizes” curricula around defining what a . student should know and be able to do in a chosen . B. Gibbons . Viswanath. . Poosala. . Sridhar . Ramaswamy. . Presented By . Vinay. . Hoskere. Join Synopses for Approximate Query Answering. Introduction. AQUA. Problem . with joins. Join . synopses. Barzan. . Mozafari. (presented by Ivo D. Dinov). University of Michigan, Ann Arbor. Research Goals. Using statistics to build better data-intensive systems. Faster. How to query petabytes of data in seconds?. John Casey. Senior Solutions Advisor jcasey@rocketsoftware.com. What’s the cost of application downtime to your business?. Minutes of Downtime. x Average revenue per minute . = Downtime Losses. Performance tuning challenges across the organization.