NDLI: Apply Block Index Technique to Scientific Data Analysis and I/O Systems

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Apply Block Index Technique to Scientific Data Analysis and I/O Systems

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Apply Block Index Technique to Scientific Data Analysis and I/O Systems

Content Provider	ACM Digital Library
Author	Wu, Kesheng Chou, Jerry Tian, Yuan Gu, Junmin Klasky, Scott Wu, Tzuhsien Podhorszki, Norbert
Abstract	Scientific discoveries are increasingly relying on analysis of massive amounts of data. The ability to directly access the most relevant data records through query, without shifting through all of them becomes essential. However, scientific datasets are commonly stored on parallel file systems and I/O systems that are optimized for reading/writing large chunks of data, and many scientific datasets have spatial-temporal data similarity, such that the records with similar values often locate in a close proximity of each other. Therefore, our previous work started to investigate the benefit of using block range index technique for scientific datasets, which only records the value range of all the records in a data block. In this paper, we extend our work in several aspects. First, we implement and integrate our block index technique with the ADIOS I/O system. Second, we show our proposed method can be significantly better than the existing minmax and bitmaps indexing methods supported in ADIOS, and can also have comparable performance in the worst case. Third, we propose several techniques that can take advantage of the block index information to greatly reduce data retrieval time from query results. Fourth, we evaluate our approach using several real scientific datasets, and analyze the spatial-temporal data similarity characteristics in them. Through our study, we believe block index can be an effective indexing technique for scientific datasets with little implementation and operating overhead. It's size is small enough for building the indexes on-the-fly, and yet its query information is sufficient for efficient data access.
Starting Page	865
Ending Page	871
Page Count	7
File Format	PDF
ISBN	9781509066100
DOI	10.1109/CCGRID.2017.37
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-14
Access Restriction	Subscribed
Subject Keyword	Scientific data Query analysis Io systems Indexing
Content Type	Text
Resource Type	Article

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