TY - JOUR
T1 - ViewSeeker
T2 - An Interactive View Recommendation Framework
AU - Zhang, Xiaozhong
AU - Ge, Xiaoyu
AU - Chrysanthis, Panos K.
AU - Sharaf, Mohamed A.
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/7/15
Y1 - 2021/7/15
N2 - 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.
AB - 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.
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U2 - 10.1016/j.bdr.2021.100238
DO - 10.1016/j.bdr.2021.100238
M3 - Article
AN - SCOPUS:85130510926
SN - 2214-5796
VL - 25
JO - Big Data Research
JF - Big Data Research
M1 - 100238
ER -