Full Citation

Title: ViewSeeker: An Interactive View Recommendation Framework

Citation Type: Journal Article

Publication Year: 2021

ISBN:

ISSN: 2214-5796

DOI: 10.1016/J.BDR.2021.100238

NSFID:

PMCID:

PMID:

Abstract: View recommendations have emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. However, the suitability of the utility functions and their tunable parameters for an analysis is usually dependent on the analysis context, such as the user, the data and the analysis task. In order to provide context-aware view recommendation, we formulate a new Interactive View Recommendation (IVR) paradigm, where the system interacts with the user to discover the utility functions that are most suitable in the current analysis context. We further develop an IVR framework, coined ViewSeeker, which leverages user feedback on intelligently selected example views to discover the most suitable utility functions. Finally, we implemented a prototype of ViewSeeker and verified its efficiency and effectiveness using two real-world datasets.

Url: https://www.sciencedirect.com/science/article/abs/pii/S2214579621000551

User Submitted?: No

Authors: Zhang, Xiaozhong; Ge, Xiaoyu; Chrysanthis, Panos K.; Sharaf, Mohamed A.

Periodical (Full): Big Data Research

Issue:

Volume: 25

Pages: 1-30

Data Collections: IPUMS CPS

Topics: Population Data Science

Countries: