Sync your data: update propagation for heterogeneous protein databases

Sync your data: update propagation for heterogeneous protein databases The traditional model of bench (wet) chemistry in many life sciences domain is today actively complimented by computer-based discoveries utilizing the growing number of online data sources. A typical computer-based discovery scenario for many life scientists includes the creation of local caches of pertinent information from multiple online resources such as Swissprot (Nucleic Acid Res. 1 (28), 45–48 (2000)), PIR (Nucleic Acids Res. 28 (1), 41–44 (2000)), PDB (The Protein DataBank. Wiley, New York (2003)), to enable efficient data analysis. This local caching of data, however, exposes their research and eventual results to the problems of data staleness, that is, cached data may quickly be obsolete or incorrect, dependent on the updates that are made to the source data. This represents a significant challenge to the scientific community, forcing scientists to be continuously aware of the frequent changes made to public data sources, and more importantly aware of the potential effects on their own derived data sets during the course of their research. To address this significant challenge, in this paper we present an approach for handling update propagation between heterogeneous databases, guaranteeing data freshness for scientists irrespective of their choice of data source and its underlying data model or interface. We propose a middle-layer –based solution wherein first the change in the online data source is translated to a sequence of changes in the middle-layer; next each change in the middle-layer is propagated through an algebraic representation of the translation between the source and the target; and finally the net-change is translated to a set of changes that are then applied to the local cache. In this paper, we present our algebraic model that represents the mapping of the online resource to the local cache, as well as our adaptive propagation algorithm that can incrementally propagate both schema and data changes from the source to the cache in a data model independent manner. We present a case study based on a joint ongoing project with our collaborators in the Chemistry Department at UMass-Lowell to explicate our approach. The VLDB Journal Springer Journals

Sync your data: update propagation for heterogeneous protein databases

Loading next page...
Copyright © 2005 by Springer-Verlag
Computer Science; Database Management
Publisher site
See Article on Publisher Site


You’re reading a free preview. Subscribe to read the entire article.

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches


Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.



billed annually
Start Free Trial

14-day Free Trial