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Processing Business Intelligence Inquiries in a Blink

Overview

The Blink project is developing a scalable query engine that consistently responds to Business Intelligence queries against a data warehouse in mere seconds, without the necessity for the complicated and human-intensive "performance layer" of indexes, materialized views, and pre-computation of today's data warehouse systems. It exploits many disruptive hardware technologies -- including large main memories, commodity multi-core processors, and fast interconnects -- together with innovative software developed by Almaden Research to highly compress and de-normalize data, apply query predicates and perform grouping on the compressed data, maximize parallelism, minimize L2 cache misses, and significantly simplify administration.

The project builds upon a great degree of published work:

  • Knut Stolze, Vijayshankar Raman, Richard Sidle, O. Draese: Bringing BLINK Closer to the Full Power of SQL. BTW 2009: 157-166
  • Peter J. Haas, Ihab F. Ilyas, Guy M. Lohman, Volker Markl: Discovering and Exploiting Statistical Properties for Query Optimization in Relational Databases: A Survey. Statistical Analysis and Data Mining 1(4): 223-250 (2009)
  • Debabrata Dash, Jun Rao, Nimrod Megiddo, Anastasia Ailamaki, Guy M. Lohman: Dynamic faceted search for discovery-driven analysis. CIKM 2008: 3-12
  • Wook-Shin Han, Wooseong Kwak, Jinsoo Lee, Guy M. Lohman, Volker Markl: Parallelizing query optimization. PVLDB 1(1): 188-200 (2008)
  • Vijayshankar Raman, Garret Swart, Lin Qiao, Frederick Reiss, Vijay Dialani, Donald Kossmann, Inderpal Narang, Richard Sidle: Constant-Time Query Processing. ICDE 2008: 60-69

Project Contact: Guy Lohman

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