Managing dynamic mixed workloads for operational business intelligence

  • Authors:

  • Harumi Kuno;Umeshwar Dayal;Janet L. Wiener;Kevin Wilkinson;Archana Ganapathi;Stefan Krompass

  • Affiliations:

  • Hewlett-Packard Laboratories, Palo Alto, CA;Hewlett-Packard Laboratories, Palo Alto, CA;Hewlett-Packard Laboratories, Palo Alto, CA;Hewlett-Packard Laboratories, Palo Alto, CA;UC Berkeley, Berkeley, CA;Technische Universität München, Munich, Germany

  • Venue:
  • DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
  • Year:
  • 2010

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Abstract

As data warehousing technology gains a ubiquitous presence in business today, companies are becoming increasingly reliant upon the information contained in their data warehouses to inform their operational decisions. This information, known as business intelligence (BI), traditionally has taken the form of nightly or monthly reports and batched analytical queries that are run at specific times of day. However, as the time needed for data to migrate into data warehouses has decreased, and as the amount of data stored has increased, business intelligence has come to include metrics, streaming analysis, and reports with expected delivery times that are measured in hours, minutes, or seconds. The challenge is that in order to meet the necessary response times for these operational business intelligence queries, a given warehouse must be able to support at any given time multiple types of queries, possibly with different sets of performance objectives for each type. In this paper, we discuss why these dynamic mixed workloads make workload management for operational business intelligence (BI) databases so challenging, review current and proposed attempts to address these challenges, and describe our own approach. We have carried out an extensive set of experiments, and report on a few of our results.