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Featured researches published by Oliver Draese.


business intelligence for the real time enterprises | 2011

Blink: Not Your Father's Database!

Ronald J. Barber; Peter Bendel; Marco Czech; Oliver Draese; Frederick Ho; Namik Hrle; Stratos Idreos; Min-Soo Kim; Oliver Koeth; Jae-Gil Lee; Tianchao Tim Li; Guy M. Lohman; Konstantinos Morfonios; Rene Mueller; Keshava Murthy; Ippokratis Pandis; Lin Qiao; Vijayshankar Raman; Sandor Szabo; Richard S. Sidle; Knut Stolze

The Blink project’s ambitious goals are to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. It takes a very innovative and counter-intuitive approach to processing BI queries, one that exploits several disruptive hardware and software technology trends. Specifically, it is a new, workload-optimized DBMS aimed primarily at BI query processing, and exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Ignoring the general wisdom of the last three decades that the only way to scalably search large databases is with indexes, Blink always performs simple, “brute force” scans of the entire data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two products: (1) an accelerator appliance product for DB2 for z/OS (on the “mainframe”), called the IBM Smart Analytics Optimizer for DB2 for z/OS, V1.1, which was generally available in November 2010; and (2) the Informix Warehouse Accelerator (IWA), a software-only version that was generally available in March 2011. We are now working on the next generation of Blink, called BLink Ultra, or BLU, which will significantly expand the “sweet spot” of Blink technology to much larger, disk-based warehouses and allow BLU to “own” the data, rather than copies of it.


IEEE Data(base) Engineering Bulletin | 2012

Business Analytics in (a) Blink

Ronald J. Barber; Peter Bendel; Marco Czech; Oliver Draese; Frederick Ho; Namik Hrle; Stratos Idreos; Min-Soo Kim; Oliver Koeth; Jae-Gil Lee; Tianchao Tim Li; Guy M. Lohman; Konstantinos Morfonios; René Müller; Keshava Murthy; Ippokratis Pandis; Lin Qiao; Vijayshankar Raman; Richard S. Sidle; Knut Stolze; Sandor Szabo


Archive | 2010

Adaptive cell-specific dictionaries for frequency-partitioned multi-dimensional data

Oliver Draese; Namik Hrle; Oliver Koeth; Tianchao Li; Vijayshankar Raman; Knut Stolze


Archive | 2009

METHOD AND SYSTEM FOR OPTIMIZING DATA ACCESS IN A DATABASE USING MULTI-CLASS OBJECTS

Oliver Draese; Benno Staebler; Torsten Steinbach; Knut Stolze


Archive | 2007

Workload Aware Checking of Database Reorganization

Oliver Draese; Namik Hrle; Torsten Steinbach; Michael J. Winer


Archive | 2009

System and method for parallel computation of frequency histograms on joined tables

Peter Bendel; Oliver Draese; Vijayshankar Raman; Knut Stolze


Archive | 2009

System and method for aviding three-valued logic in predicates on dictionary-encoded data

Peter Bendel; Oliver Draese; Vijayshankar Raman; Knut Stolze


BTW | 2009

Bringing BLINK Closer to the Full Power of SQL.

Knut Stolze; Vijayshankar Raman; Richard S. Sidle; Oliver Draese


Archive | 2009

System and method for providing information in a virtual world

Oliver Draese; Benno Staebler; Torsten Steinbach; Knut Stolze


very large data bases | 2014

Joins on encoded and partitioned data

Jae-Gil Lee; Gopi K. Attaluri; Ronald J. Barber; Naresh K. Chainani; Oliver Draese; Frederick Ho; Stratos Idreos; Min-Soo Kim; Sam Lightstone; Guy M. Lohman; Konstantinos Morfonios; Keshava Murthy; Ippokratis Pandis; Lin Qiao; Vijayshankar Raman; Vincent Kulandai Samy; Richard S. Sidle; Knut Stolze; Liping Zhang

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