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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2012
Publication statusPublished - 2012
EventVisualization and Data Analysis 2012 - Burlingame, CA, United States
Duration: 2012 Jan 232012 Jan 25

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherVisualization and Data Analysis 2012
Country/TerritoryUnited States
CityBurlingame, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'SDSS Log Viewer: Visual exploratory analysis of large-volume SQL log data'. Together they form a unique fingerprint.

Cite this