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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20190201T110000
DTEND;TZID=America/Los_Angeles:20190201T120000
DTSTAMP:20261015T000552
CREATED:20190125T020959Z
LAST-MODIFIED:20190125T211117Z
UID:1045-1549018800-1549022400@isg.ics.uci.edu
SUMMARY:Prof. Jeff Ullman: Data Science:  Is it Real?
DESCRIPTION:ABSTRACT: \nWe shall discuss the various ways in which data science is approached by different communities\, including the Statistics\, Machine-Learning\, and Database communities.  Each presents a different viewpoint and values different outcomes. Some consequences of these approaches will be discussed. As an example\, of why data science is not machine learning\, we shall sketch two important algorithms not in this class: locality-sensitive hashing and approximate counting. \nBIOGRAPHY: \nJeffrey David Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp.  He received the B.S. degree in Engineering Mathematics from Columbia in 1963 and the PhD in Electrical Engineering from Princeton in 1966. Prior to his appointment at Stanford in 1979\, he was a member of the technical staff of Bell Laboratories from 1966-1969\, and on the faculty of Princeton University between 1969 and 1979.  From 1990-1994\, he was chair of the Stanford Computer Science Department. Prof. Ullman was elected to the US National Academy of Engineering in 1989\, the American Academy of Arts and Sciences in 2012\, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996)\, the ACM Karl V. Karlstrom Outstanding Educator Award (1998)\, the Knuth Prize (2000)\, the Sigmod E. F. Codd Innovations award (2006)\, the IEEE von Neumann medal (2010)\, and the NEC C&C Foundation Prize (2017).  He is the author of 16 books\, including books on database systems\, data mining\, compilers\, automata theory\, and algorithms. \nURL:  i.stanford.edu/~ullman
URL:https://isg.ics.uci.edu/event/prof-jeff-ullman-visit/
LOCATION:DBH 6011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190125T150000
DTEND;TZID=America/Los_Angeles:20190125T160000
DTSTAMP:20261015T000552
CREATED:20181221T022339Z
LAST-MODIFIED:20190131T004734Z
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SUMMARY:David Lomet (Microsoft Research): How Data Caching Systems Succeed
DESCRIPTION:Data in traditional “caching” data systems resides on secondary storage\, and is read into main memory only when operated on. This limits system performance. Main memory data stores with data always in main memory are much faster. But this performance comes at a cost. In this paper\, we analyze the costs of both in-memory operations and secondary storage operations where data is not “in cache”. We study the performance impact of cache misses on caching system performance. The analysis considers both execution and storage costs. Based on our analysis\, we derive cost/performance results for a data caching system [Deuteronomy and its Bw-tree] and a main memory system [MassTree] to understand where each demonstrates the best cost per operation\, what is driving the cost differences\, and the scale of the differences. This analysis (1) provides insight into why data caching systems continue to dominate the market; (2) points to higher performance that does not rely on simply increasing main memory cache size; and (3) suggests a path to lower costs and hence better cost/performance. \n  \nSpeaker Bio: \nDavid Lomet founded the Database Group at Microsoft Research Redmond in 1995 and managed it for 20 years. His research career began at IBM where\, while on a 1975-76 sabbatical at the University of Newcastle-on-Tyne\, he invented atomic actions (a form of transactions). He later worked at Wang Institute as a faculty member\, and at Digital Equipment Corporation as a software architect and research staff member. He received a Ph.D. in computer science from the University of Pennsylvania. Lomet’s primary focus has been the engineering of database systems\, with a focus on database system kernels. His work on concurrency control and recovery contributed to making DEC’s Rdb and Microsoft’s SQL Server database management systems leaders in cost/performance. His Deuteronomy research project’s latch-free Bw-tree index and log structure store are key elements in Microsoft’s Hekaton main memory database and Azure Cosmos DB cloud data service. Deuteronomy won the Microsoft Research Redmond “2017 Best Research Project” Award. Lomet is an author of over 120 papers and over 60 patents. Lomet has won IEEE awards as well as the ACM SIGMOD Contributions Award for his 25 year tenure as EIC of the IEEE Data Engineering Bulletin. HE has also served as editor of ACM TODS\, VLDB Journal and others\, and has been a member of the VLDB Board. He has been a PC co-chair for ICDE and VLDB. He is a member of the IEEE Computer Society Board of Governors and society Secretary\, and has been First Vice President and Treasurer. He is a fellow of IEEE\, ACM\, and AAAS\, and a member of the National Academy of Engineering. \n  \nDownload slides for this talk \nWatch the video for this talk
URL:https://isg.ics.uci.edu/event/speaker-david-lomet-microsoft-research-cost-performance-in-modern-data-stores-how-data-caching-systems-succeed/
LOCATION:DBH 4011
ATTACH;FMTTYPE=image/png:https://isg.ics.uci.edu/wp-content/uploads/2018/10/Microsoft-logo_rgb_wht.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181207T150000
DTEND;TZID=America/Los_Angeles:20181207T160000
DTSTAMP:20261015T000552
CREATED:20181222T021251Z
LAST-MODIFIED:20260401T210254Z
UID:838-1544194800-1544198400@isg.ics.uci.edu
SUMMARY:Vinayak Borkar (FireEye Inc.): The X15 Machine Data Management Platform
DESCRIPTION:ABSTRACT: Machine Data (aka Log Data) is continuously produced by applications and devices as a result of human-computer and computer-computer interactions. Although most of this data was initially generated for ad-hoc human consumption to aid with debugging and troubleshooting systems and deployments\, their systematic treatment using well-known data processing techniques can unlock valuable insight about operations within organizations. Log Data may sometimes be the only feasible source of some of this insight. Processing Log Data poses new challenges as compared to traditional data management. Firstly\, Log Data is mostly textual requiring ad-hoc parsing to surface its meaning. Secondly\, Log Data tends to be extremely voluminous and is generated with high velocity posing a unique set of challenges in its acquisition and storage in a cost-effective manner. Lastly\, Log Data requires new query languages and techniques to unlock its full potential. X15 is a scalable purpose-built platform to acquire\, store\, and manage Machine Data. In this talk we will look at X15’s architecture\, some of its design decisions and use cases. \nBIOS: Vinayak Borkar is Vice President of Engineering at FireEye\, Inc. Prior to FireEye’s acquisition of X15\, he was a co-founder and the CTO of X15 Software. Prior to X15\, Vinayak was the CTO and head of engineering at DVO Software and the inventor and lead contributor of Hyracks\, an open-source parallel data processing platform built at UC Irvine. He began his career at BEA Systems\, where he led the development of the first XQuery-based data integration platform. Vinayak was the recipient of the Facebook Fellowship award for his contribution in the area of large-scale data management and holds a BS in Computer Science and Engineering from University of Mumbai and an MS in Computer Science and Engineering from Indian Institute of Technology Bombay\, and a PhD. in Information and Computer Science from the University of California at Irvine.
URL:https://isg.ics.uci.edu/event/vinayak-borkar-fireeye-inc-the-x15-machine-data-management-platform/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181116T150000
DTEND;TZID=America/Los_Angeles:20181116T160000
DTSTAMP:20261015T000552
CREATED:20181222T021505Z
LAST-MODIFIED:20181222T021505Z
UID:842-1542380400-1542384000@isg.ics.uci.edu
SUMMARY:Dr. Andrey Balmin and Mayank Pradhan (Workday): Workday Prism Analytics: Unifying Interactive and Batch Data Processing Using Apache Spark
DESCRIPTION:Abstract: Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. To prepare data for analysis\, business users can setup data transformation pipelines in an interactive\, self-service\, modern data prep environment. Thus\, Workday Prism Analytics needs to run three types of scalable data processing applications: “always on” query engine and data prep applications\, and on-demand batch execution of transformation pipelines. We standardized on Apache Spark and Spark SQL for all three applications\, due to its scalability\, as well as\, flexibility and extensibility of the Spark’s Catalyst compiler. All applications share much of the compilation and execution code\, except for sampling\, caching\, and result extraction. \n  \nIn this talk we will\, first\, introduce Workday and then Prism Analytics. We will then zoom into Spark-based interactive and batch data processing components of Prism Analytics. We will then describe the data prep transformations\, and their compilation into Spark DataFrames\, through Spark-SQL Catalyst plans\, in both interactive and batch mode. We will focus on some challenges we encountered while compiling and executing complex pipelines and queries. For example\, Spark SQL compilation times exceeded execution time for some low-latency queries. And compiled plans grew dangerously for data prep pipelines with multiple self-joins and self-unions. We will describe caching\, sampling\, and query compilation techniques that allow us to support interactive user experience. This includes a join co-sampling component that improves system usability when joining large datasets. Finally\, we will conclude with an overview of the open challenges that we plan to tackle in the future. \n  \nBios: \nDr. Andrey Balmin is a Sr. Principal Engineer at Workday\, where he is building the self-service Prism Analytics platform\, continuing the work he began at Platfora (which was acquired by Workday in 2016). Prior to this\, he was a Research Staff Member at IBM Almaden Research Center where he focused on search and query processing of semi-structured and graph-structured data in Data Warehousing and\, later\, Big Data platforms. He holds a Ph.D. degree in Computer Science from UC San Diego. \n  \nMayank Pradhan is a Senior Engineering Manager at Workday focused on building Analytics Platform for Workday’s customers. His team develops the backend engines in cloud that run a variety of workloads including\, data ingestion\, interactive data preparation\, and OLAP cubes for large scale\, high volume interactive querying. Prior to Workday\, Mayank worked on industry shifting data processing products like Platfora\, ParAccel (the columnar database technology behind Redshift)\, IBM DB2. He has 18 years of industry experience building distributed databases. He did his M.S in Computer Science from Santa Clara University and BS Computer Science from Pune University India.
URL:https://isg.ics.uci.edu/event/dr-andrey-balmin-and-mayank-pradhan-workday-workday-prism-analytics-unifying-interactive-and-batch-data-processing-using-apache-spark/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181102T110000
DTEND;TZID=America/Los_Angeles:20181102T120000
DTSTAMP:20261015T000552
CREATED:20181024T025814Z
LAST-MODIFIED:20190128T173206Z
UID:793-1541156400-1541160000@isg.ics.uci.edu
SUMMARY:Sushant Jain : Large scale and low latency data distribution from database to servers
DESCRIPTION:Many applications at Google are structured with data stored in a transactional database (source of truth) and same data being required by servers distributed worldwide. For efficient and fast computation servers store this data in memory. Further\, the database is changing continuously and we need to update the in-memory view of these large number of servers in real-time. For example\, in Google Search Ads application we have Advertisers configuration stored in a database and this data is loaded in the memory of various servers to compute Ads in a scalable and fast way. In this talk\, we describe our solution to this data distribution problem and the challenges that we encountered in providing a highly reliable and low latency service. \n  \nSpeaker Bio:  \nSushant Jain is a member of the Ads Quality Infrastructure group at Google Inc. His research interests include distributed systems and network routing. He is currently an engineering director and a lead engineer for multiple products in Ads Quality Infrastructure with a focus on data management and distribution. Many of the systems developed as part of his work at Google (such as WhyHigh\, Bandwidth Enforcer\, B4 Network) are actively deployed at Google and have had significant research impact as well. He is a recipient of the Best Paper Award at ACM SIGCOMM IMC 2011. He received his Ph.D. from the University of Washington in 2005 and a B.Tech. from IIT Delhi in 1999.
URL:https://isg.ics.uci.edu/event/large-scale-and-low-latency-data-distribution-from-database-to-servers/
LOCATION:DBH 6011
ATTACH;FMTTYPE=image/jpeg:https://isg.ics.uci.edu/wp-content/uploads/2018/10/google-datacenter-tech-13.jpg
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