QuRVe: Query Refinement for View Recommendation in Visual Data Exploration

Humaira Ehsan, Mohamed A. Sharaf, Gianluca Demartini

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

4 Citations (Scopus)

Abstract

The need for efficient and effective data exploration has resulted in several solutions that automatically recommend interesting visualizations. The main idea underlying those solutions is to automatically generate all possible views of data, and recommend the top-k interesting views. However, those solutions assume that the analyst is able to formulate a well-defined query that selects a subset of data, which contains insights. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. To address that challenge, this paper proposes to automatically refine the analyst’s input query to discover such valuable insights. However, a naive query refinement, in addition to generating a prohibitively large search space, also raises other problems such as deviating from the user’s preference and recommending statistically insignificant views. In this paper, we address those problems and propose the novel QuRVe scheme, which efficiently navigates the refined queries search space to recommend the top-k insights that meet all of the analysts’s pre-specified criteria.

Original languageEnglish
Title of host publicationNew Trends in Databases and Information Systems, ADBIS 2020 Short Papers, Proceedings
EditorsJérôme Darmont, Boris Novikov, Robert Wrembel
PublisherSpringer
Pages154-165
Number of pages12
ISBN (Print)9783030546229
DOIs
Publication statusPublished - 2020
Event24th European Conference on Advances in Databases and Information Systems, ADBIS 2020 - Lyon, France
Duration: Aug 25 2020Aug 27 2020

Publication series

NameCommunications in Computer and Information Science
Volume1259 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference24th European Conference on Advances in Databases and Information Systems, ADBIS 2020
Country/TerritoryFrance
CityLyon
Period8/25/208/27/20

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Fingerprint

Dive into the research topics of 'QuRVe: Query Refinement for View Recommendation in Visual Data Exploration'. Together they form a unique fingerprint.

Cite this