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Instant-On Scientific Data Warehouses

Lazy ETL for Data-Intensive Research
  • Yağız Kargın
  • Holger Pirk
  • Milena Ivanova
  • Stefan Manegold
  • Martin Kersten
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 154)

Abstract

In the dawn of the data intensive research era, scientific discovery deploys data analysis techniques similar to those that drive business intelligence. Similar to classical Extract, Transform and Load (ETL) processes, data is loaded entirely from external data sources (repositories) into a scientific data warehouse before it can be analyzed. This process is both, time and resource intensive and may not be entirely necessary if only a subset of the data is of interest to a particular user. To overcome this problem, we propose a novel technique to lower the costs for data loading: Lazy ETL. Data is extracted and loaded transparently on-the-fly only for the required data items. Extensive experiments demonstrate the significant reduction of the time from source data availability to query answer compared to state-of-the-art solutions. In addition to reducing the costs for bootstrapping a scientific data warehouse, our approach also reduces the costs for loading new incoming data.

Keywords

Query Processing Query Time Data Ingestion Query Evaluation Incremental Update 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yağız Kargın
    • 1
  • Holger Pirk
    • 1
  • Milena Ivanova
    • 1
  • Stefan Manegold
    • 1
  • Martin Kersten
    • 1
  1. 1.Centrum Wiskunde & Informatica (CWI)AmsterdamThe Netherlands

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