OXPath: A language for scalable data extraction, automation, and crawling on the deep web

  • Authors:

  • Tim Furche;Georg Gottlob;Giovanni Grasso;Christian Schallhart;Andrew Sellers

  • Affiliations:

  • Department of Computer Science, Oxford University, Oxford, UK OX1 3QD;Department of Computer Science, Oxford University, Oxford, UK OX1 3QD;Department of Computer Science, Oxford University, Oxford, UK OX1 3QD;Department of Computer Science, Oxford University, Oxford, UK OX1 3QD;Department of Computer Science, Oxford University, Oxford, UK OX1 3QD

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2013

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Abstract

The evolution of the web has outpaced itself: A growing wealth of information and increasingly sophisticated interfaces necessitate automated processing, yet existing automation and data extraction technologies have been overwhelmed by this very growth. To address this trend, we identify four key requirements for web data extraction, automation, and (focused) web crawling: (1) interact with sophisticated web application interfaces, (2) precisely capture the relevant data to be extracted, (3) scale with the number of visited pages, and (4) readily embed into existing web technologies. We introduce OXPath as an extension of XPath for interacting with web applications and extracting data thus revealed--matching all the above requirements. OXPath's page-at-a-time evaluation guarantees memory use independent of the number of visited pages, yet remains polynomial in time. We experimentally validate the theoretical complexity and demonstrate that OXPath's resource consumption is dominated by page rendering in the underlying browser. With an extensive study of sublanguages and properties of OXPath, we pinpoint the effect of specific features on evaluation performance. Our experiments show that OXPath outperforms existing commercial and academic data extraction tools by a wide margin.