Dynamic reduction of query result sets for interactive visualizaton

Modern database management systems (DBMS) have been designed to efficiently store, manage and perform computations on massive amounts of data. In contrast, many existing visualization systems do not scale seamlessly from small data sets to enormous ones. We have designed a three-tiered visualization system called ScalaR to deal with this issue. ScalaR dynamically performs resolution reduction when the expected result of a DBMS query is too large to be effectively rendered on existing screen real estate. Instead of running the original query, ScalaR inserts aggregation, sampling or filtering operations to reduce the size of the result. This paper presents the design and implementation of ScalaR, and shows results for an example application, displaying satellite imagery data stored in SciDB as the back-end DBMS.

[1]  Pat Hanrahan,et al.  Polaris: a system for query, analysis and visualization of multi-dimensional relational databases , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[2]  Alekh Jindal,et al.  Hadoop++ , 2010 .

[3]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[4]  Robert Spence,et al.  The Attribute Explorer: information synthesis via exploration , 1998, Interact. Comput..

[5]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[6]  Michael Stonebraker,et al.  A Demonstration of SciDB: A Science-Oriented DBMS , 2009, Proc. VLDB Endow..

[7]  Peter J. Haas,et al.  Ripple joins for online aggregation , 1999, SIGMOD '99.

[8]  Steven F. Roth,et al.  An Interactive Visualization Environment for Data Exploration , 1997, KDD.

[9]  Steven F. Roth,et al.  Visage: a user interface environment for exploring information , 1996, Proceedings IEEE Symposium on Information Visualization '96.

[10]  Monica M. C. Schraefel,et al.  Trust me, i'm partially right: incremental visualization lets analysts explore large datasets faster , 2012, CHI.

[11]  John T. Stasko,et al.  The information mural: a technique for displaying and navigating large information spaces , 1995, Proceedings of Visualization 1995 Conference.

[12]  Christopher Williamson,et al.  Dynamic queries for information exploration: an implementation and evaluation , 1992, CHI.

[13]  John T. Stasko,et al.  The Information Mural: A Technique for Displaying and Navigating Large Information Spaces , 1998, IEEE Trans. Vis. Comput. Graph..

[14]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[15]  C. Weaver.  Building Highly-Coordinated Visualizations in Improvise , 2004, IEEE Symposium on Information Visualization.

[16]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[17]  Ramana Rao,et al.  The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information , 1994, CHI '94.

[18]  Peter J. Haas,et al.  Interactive data Analysis: The Control Project , 1999, Computer.

[19]  Hans-Peter Kriegel,et al.  VisDB: database exploration using multidimensional visualization , 1994, IEEE Computer Graphics and Applications.

[20]  Ion Stoica,et al.  BlinkDB: queries with bounded errors and bounded response times on very large data , 2012, EuroSys '13.

[21]  Valerio Pascucci,et al.  Parallel visualization on large clusters using MapReduce , 2011, 2011 IEEE Symposium on Large Data Analysis and Visualization.

[22]  Jean-Daniel Fekete,et al.  Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines , 2010, IEEE Transactions on Visualization and Computer Graphics.

[23]  Helen J. Wang,et al.  Online aggregation , 1997, SIGMOD '97.