SDSS Log Viewer: visual exploratory analysis of large-volume SQL log data

Jian Zhang; Chaomei Chen; Michael S. Vogeley; Danny Pan; Ani Thakar; Jordan Raddick

doi:10.1117/12.907097

24 January 2012 SDSS Log Viewer: visual exploratory analysis of large-volume SQL log data

Jian Zhang, Chaomei Chen, Michael S. Vogeley, Danny Pan, Ani Thakar, Jordan Raddick

Proceedings Volume 8294, Visualization and Data Analysis 2012; 82940D (2012) https://doi.org/10.1117/12.907097
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

User-generated Structured Query Language (SQL) queries are a rich source of information for database analysts, information scientists, and the end users of databases. In this study a group of scientists in astronomy and computer and information scientists work together to analyze a large volume of SQL log data generated by users of the Sloan Digital Sky Survey (SDSS) data archive in order to better understand users' data seeking behavior. While statistical analysis of such logs is useful at aggregated levels, efficiently exploring specific patterns of queries is often a challenging task due to the typically large volume of the data, multivariate features, and data requirements specified in SQL queries. To enable and facilitate effective and efficient exploration of the SDSS log data, we designed an interactive visualization tool, called the SDSS Log Viewer, which integrates time series visualization, text visualization, and dynamic query techniques. We describe two analysis scenarios of visual exploration of SDSS log data, including understanding unusually high daily query traffic and modeling the types of data seeking behaviors of massive query generators. The two scenarios demonstrate that the SDSS Log Viewer provides a novel and potentially valuable approach to support these targeted tasks.

Citation Download Citation

Jian Zhang, Chaomei Chen, Michael S. Vogeley, Danny Pan, Ani Thakar, and Jordan Raddick "SDSS Log Viewer: visual exploratory analysis of large-volume SQL log data", Proc. SPIE 8294, Visualization and Data Analysis 2012, 82940D (24 January 2012); https://doi.org/10.1117/12.907097

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