NDLI: Efficient range distribution query in large-scale scientific data

Content Provider	IEEE Xplore Digital Library
Author	Chaudhuri, A. Teng-Yok Lee Han-Wei Shen Peterka, T.
Copyright Year	2013
Description	Author affiliation: Argonne Nat. Lab., Argonne, IL, USA (Peterka, T.) \|\| Ohio State Univ., Columbus, OH, USA (Chaudhuri, A.; Teng-Yok Lee; Han-Wei Shen)
Abstract	Frequent access to raw data is no longer practical, if possible at all, for answering queries on large-scale data. This has led to the use of distribution-based data summaries, which can substitute for raw data to answer statistical queries of different kinds. Our work is concerned with range distribution query, which returns the distribution of an axis-aligned region of any size. We address the challenge of maintaining the interactivity and accuracy of such query results in the presence of large data. This work presents a novel and efficient framework for pre-computing and storing a set of distributions which can be used to query any arbitrary region during post-processing. We adapt an integral image based data structure to answer such queries in constant time, and propose a similarity-based encoding technique to reduce the storage cost of the data structure. Our scheme utilizes the similarity present among different regions in the data, and hence, their respective distributions. We demonstrate the use our technique in various applications, which directly or indirectly require distributions.
Starting Page	125
Ending Page	126
File Size	1095717
Page Count	2
File Format	PDF
ISBN	9781479916597
DOI	10.1109/LDAV.2013.6675171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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