TY - JOUR
T1 - ViewSeeker
T2 - 2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019
AU - Zhang, Xiaozhong
AU - Ge, Xiaoyu
AU - Chrysanthis, Panos K.
AU - Sharaf, Mohamed A.
N1 - Funding Information:
Acknowledgments We would like to thank the anonymous reviewers for their helpful comments. This work was supported, in part, by NIH under award U01HL137159. This paper does not represent the views of NIH.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019
Y1 - 2019
N2 - View recommendation has 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. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones along with their tunable parameters, which represent the user’s intention during an exploration, is a challenge for both expert and non-expert users. This paper presents the first attempt towards interactive view recommendation by automatically discovering the most appropriate view utility functions in an exploration based on the user’s preferences. In particular, our proposed ViewSeeker uses a novel active learning method to discover the view utility function by interactively refining the set of k view recommendations. The effectiveness and efficiency of ViewSeeker was experimentally evaluated using both synthetic and real data sets.
AB - View recommendation has 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. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones along with their tunable parameters, which represent the user’s intention during an exploration, is a challenge for both expert and non-expert users. This paper presents the first attempt towards interactive view recommendation by automatically discovering the most appropriate view utility functions in an exploration based on the user’s preferences. In particular, our proposed ViewSeeker uses a novel active learning method to discover the view utility function by interactively refining the set of k view recommendations. The effectiveness and efficiency of ViewSeeker was experimentally evaluated using both synthetic and real data sets.
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M3 - Conference article
AN - SCOPUS:85062654468
SN - 1613-0073
VL - 2322
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 26 March 2019
ER -