Top-k entity units retrieval over big data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

During the past several years, data size has increased explosively. This data explosion tendency has impacted various fields ranging from biomedical engineering, business consulting to social media and mobile application. Big Data is a two sided sword. While it provides incredibly treasured insights in commercial scope and innovative discovery in the scientific field, Big Data also has many challenges, such as complication in data storage, data processing, data analysis and data visualization. Among all these challenges, keyword searching over a large volume of data prevails as one of the four tasks defined by Bizer et al. at the year of 2012. Keyword searching refers to retrieving the objects relevant to the entities of concern using scientific computational methods. Consequently, efficiently solving the problem of keyword searching can contribute as a foundation to diverse Big Data applications.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages1269-1272
Number of pages4
ISBN (Electronic)9781450349147
DOIs
StatePublished - Jan 1 2019
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Other

Other26th International World Wide Web Conference, WWW 2017 Companion
CountryAustralia
CityPerth
Period4/3/174/7/17

Keywords

  • Big Data
  • Information Retrieval
  • Searching

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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  • Cite this

    Zhang, D., & Kabuka, M. R. (2019). Top-k entity units retrieval over big data. In 26th International World Wide Web Conference 2017, WWW 2017 Companion (pp. 1269-1272). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3041021.3053063