Database Seminar Series (2012-2013)

Problems of what-if analysis (such as hypothetical deletions, insertions, and modifications) over complex analysis queries are increasingly commonplace, e.g., in forming a business strategy or looking for causal relationships in science. Here, data analysts are typically interested in only task-specific views of the data, and they expect to be able to interactively manipulate the data in a natural and seamless way - possibly on a phone or tablet, and possibly via a spreadsheet or similar interface without having to carry the full machinery of a DBMS.

The Caravan system enables what-if analysis, i.e., fast, lightweight, interactive exploration of alternative answers, within views computed over large-scale, possibly distributed data sources. Our novel approach is based on creating dedicated provisioned autonomous representations, or PARs. PARs are compiled out of the data, initial analysis queries and user-specified what-if scenarios and are based on c-tables and their recent generalization to aggregation. They allow rapid evaluation of what-if scenarios without accessing the original data or performing complex query operations. Importantly, the size of PARs is governed by the parameters of the what-if analysis and is proportional to the size of the initial query answer rather than the typically much larger source data. Consequently, many what-if analysis tasks performed through PAR evaluations can be done autonomously, on limited-resource devices. We describe our model and architecture, demonstrate preliminary performance results, and present several open implementation and optimization issues.

Joint work with Daniel Deutch (Ben-Gurion U), Zack Ives (UPenn), and Tova Milo (Tel Aviv U).

Prior to IBM, Shivakumar was part of the newly formed Altavista Group at Digital. Shivakumar was an invited keynote speaker at the 2011 German Database Conference and 2011 ACM SiGIR Industrial Track. He is also an Associate Editor of Journal of Statistical Analysis and Data Mining.

Dr. Arthur Zimek is a postdoctoral fellow at the University of Alberta, Edmonton, AB, working with Prof. Dr. Joerg Sander. Formerly, he worked as scientific assistant in the database systems and data mining group of Hans-Peter Kriegel at Ludwig-Maximilians-University Munich, Germany. He finished his PhD thesis in computing science on ''Correlation Clustering'' in summer 2008.

He received the ''SIGKDD Doctoral Dissertation Award (runner-up)'' in 2009. He received the ''Best Paper Honorable Mention Award'' at SDM 2008 and the ''Best Demonstration Paper Award'' at SSTD 2011 together with his co-authors. His research interests include unsupervised data mining (clustering and outlier detection), especially for high dimensional data, and evaluation of unsupervised data mining methods. He serves as a reviewer for several top database and data mining journals (e.g. VLDB Journal, IEEE TKDE, ACM TKDD, Data Min. Knowl. Disc., Stat. Anal. Data Min.) and as a member of program committees in data mining conferences (e.g. ACM SIGKDD 2011, 2012, ECML PKDD 2012).

This talk describes write-optimization techniques used in the TokuDB database, developed at Tokutek. TokuDB uses Fractal-Tree indexes, which support asymptotically optimal point-queries and inserts that run one to two orders of magnitude faster than traditional indexes.

I first explain how to build write-optimized data structures, addressing theoretical and engineering issues. Write-optimized storage systems can ingest and query data orders of magnitude faster than a traditional file systems and databases. For example, a prototype file system based on this technology can support over 20,000 file creates per second on a single disk.

Bender has coauthored over 90 articles. He won an R&D 100 Award for scheduling in parallel computers. He has also won three awards for both graduate and undergraduate teaching.

Bender co-founded Tokutek in 2006. He has held Visiting Scientist positions at both MIT and King's College London.

Bender received his B.A. in Applied Mathematics from Harvard University in 1992 and obtained a D.E.A. in Computer Science from the Ecole Normale Superieure de Lyon, France in 1993. He completed a Ph.D. on Scheduling Algorithms from Harvard University in 1998.

Franz Faerber is Senior Vice President of SAP HANA – Technology and Innovation Platform. In this role he is responsible for SAP's database development. His team develops the SAP HANA platform (database and development platform components). SAP HANA platform integrates database and development platform components based on innovative technology, optimized for SAP solutions while fully leveraging all talents and IP. This database platform will inhibit the fastest database for SAP applications with the lowest TCO while serving as the best platform for developing new business applications - both SAP and non-SAP applications.

Faerber reports to Vishal Sikka, member of the SAP executive board leading Technology and Innovation Platform. Faerber joined SAP in 1994 as a developer and acquired a wide and deep understanding of SAP's business and technology with a clear focus on data management. Prior to joining SAP, Faerber was working at IBM in Boeblingen as a developer. Faerber is an engineer (BA) in computer science.

Nick Koudas if a professor of computer science at the University of Toronto. He conducts research in social media analytics and big data analytics.