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Interactive data exploration using semantic windows

Published:18 June 2014Publication History

ABSTRACT

We present a new interactive data exploration approach, called Semantic Windows (SW), in which users query for multidimensional "windows" of interest via standard DBMS-style queries enhanced with exploration constructs. Users can specify SWs using (i) shape-based properties, e.g., "identify all 3-by-3 windows", as well as (ii) content-based properties, e.g., "identify all windows in which the average brightness of stars exceeds 0.8". This SW approach enables the interactive processing of a host of useful exploratory queries that are difficult to express and optimize using standard DBMS techniques. SW uses a sampling-guided, data-driven search strategy to explore the underlying data set and quickly identify windows of interest. To facilitate human-in-the-loop style interactive processing, SW is optimized to produce online results during query execution. To control the tension between online performance and query completion time, it uses a tunable, adaptive prefetching technique. To enable exploration of big data, the framework supports distributed computation.

We describe the semantics and implementation of SW as a distributed layer on top of PostgreSQL. The experimental results with real astronomical and artificial data reveal that SW can offer online results quickly and continuously with little or no degradation in query completion times.

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      • Published in

        cover image ACM Conferences
        SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
        June 2014
        1645 pages
        ISBN:9781450323765
        DOI:10.1145/2588555

        Copyright © 2014 ACM

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        New York, NY, United States

        Publication History

        • Published: 18 June 2014

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        SIGMOD '14 Paper Acceptance Rate107of421submissions,25%Overall Acceptance Rate785of4,003submissions,20%

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