Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74265
Title: Extracting top-k insights from multi-dimensional data
Authors: Tang, B 
Han, S
Yiu, ML 
Ding, R
Zhang, D
Issue Date: 2017
Publisher: Association for Computing Machinary
Source: Proceedings of the ACM SIGMOD International Conference on Management of Data, 2017, pt. F127746, p. 1509-1524 How to cite?
Abstract: OLAP tools have been extensively used by enterprises to make better and faster decisions. Nevertheless, they require users to specify group-by attributes and know precisely what they are looking for. This paper takes the first attempt towards automatically extracting top-k insights from multi-dimensional data. This is useful not only for non-expert users, but also reduces the manual effort of data analysts. In particular, we propose the concept of insight which captures interesting observation derived from aggregation results in multiple steps (e.g., rank by a dimension, compute the percentage of measure by a dimension). An example insight is: ""Brand B's rank (across brands) falls along the year, in terms of the increase in sales"". Our problem is to compute the top-k insights by a score function. It poses challenges on (i) the effectiveness of the result and (ii) the efficiency of computation. We propose a meaningful scoring function for insights to address (i). Then, we contribute a computation framework for top-k insights, together with a suite of optimization techniques (i.e., pruning, ordering, specialized cube, and computation sharing) to address (ii). Our experimental study on both real data and synthetic data verifies the effectiveness and efficiency of our proposed solution.
URI: http://hdl.handle.net/10397/74265
ISBN: 9781450341974
ISSN: 0730-8078
DOI: 10.1145/3035918.3035922
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