Querying web polystores

Abstract : The database, semantic web, and linked data communities have proposed solutions that federate queries over multiple data sources using a single data model. Nowadays, the data retrieval requirements originating from versatile and broad domains like healthcare and life sciences (HCLS) are changing this conventional trend - of federating query over a single data model - primarily due to the simultaneous use of different data models (CSV, JSON, RDB, RDF, XML, etc.) in a real-life scenario. It's now impractical to assume that the variety (graph, key-value, stream, text, table, tree, etc.) of high volume data residing in specialised storage engines will first be converted to a common data model, stored in a general-purpose data storage engine, and finally be queried over the Web. Nevertheless, in this era where genomics datasets are growing from petascale to exascale, it is now important to exploit such vast domain resources in their native data models. The key approach is to query the vast data resources from their native data models and specialised storage engines. In this paper, we propose a Web-based query federation mechanism - called PolyWeb - that unifies query answering over multiple native data models (CSV, RDB, and RDF). We demonstrate PolyWeb on a cancer genomics use-case where it is often the case that a description of biological and chemical entities (e.g., gene, disease, drug, pathways) span across multiple data models. In order to assess the benefits and limitations of evaluating queries over native data models, we evaluate PolyWeb with state-of-the-art query federation engine in terms of result completeness, source selection, and overall query execution time.
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Conference papers
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https://hal-emse.ccsd.cnrs.fr/emse-01879779
Contributor : Florent Breuil <>
Submitted on : Monday, September 24, 2018 - 12:02:01 PM
Last modification on : Wednesday, November 20, 2019 - 3:04:00 AM

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Yasar Khan, Antoine Zimmermann, Alokkumar Jha, Dietrich Rebholz-Schuhmann, Ratnesh Sahay. Querying web polystores. 2017 IEEE International Conference on Big Data (Big Data), Dec 2017, Boston, France. pp.3190-3195, ⟨10.1109/BigData.2017.8258299⟩. ⟨emse-01879779⟩

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