Abstract
We present a vision of next-generation visual analytics services. We argue that these services should have three related capabilities: support visual and interactive data exploration as they do today, but also suggest relevant data to enrich visualizations, and facilitate the integration and cleaning of that data. Most importantly, they should provide all these capabilities seamlessly in the context of an uninterrupted data analysis cycle. We present the challenges and opportunities in building next-generation visual analytics services.
- Tableau Public. http://www.tableaupublic.com/, 2012.Google Scholar
- K. Bellare et al. Active sampling for entity matching. In SIGKDD, 2012. Google ScholarDigital Library
- S. K. Card et al. Using Vision to Think. In Readings in Information Visualization. Morgan Kaufmann, 1999. Google ScholarDigital Library
- T. Dasu et al. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, New York, NY, 2003. Google ScholarDigital Library
- H. Gonzalez et al. Google Fusion Tables: Data Management, Integration and Collaboration in the Cloud. In SOCC, 2010. Google ScholarDigital Library
- A. Graves et al. Visualization tools for open government data. In Proc. of the 14th International Conf. on Digital Government Research, 2013. Google ScholarDigital Library
- A. Halevy et al. Principles of Data Integration. Morgan Kaufmann, 2012. Google ScholarDigital Library
- P. Hanrahan. Analytic database technologies for a new kind of user: the data enthusiast. In SIGMOD, 2012. Google ScholarDigital Library
- D. Huynh et al. Piggy bank: Experience the semantic web inside your web browser. In Proc. of ISWC, 2005. Google ScholarDigital Library
- Z. G. Ives et al. The orchestra collaborative data sharing system. ACM SIGMOD Record, 37(3):26--32, 2008. Google ScholarDigital Library
- Z. G. Ives et al. Interactive data integration through smart copy & paste. In CIDR, 2009.Google Scholar
- S. Kandel et al. Wrangler: Interactive Visual Specification of Data Transformation Scripts. In CHI, 2011. Google ScholarDigital Library
- E. Kandogan. Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. In VAST, 2012. Google ScholarDigital Library
- Z. Liu et al. immens: Real-time visual querying of big data. In EuroVis, 2013. Google ScholarDigital Library
- R. Miller. Response Time in Man-Computer Conversational Transactions. In AFIPS Fall Joint Computer Conf., 1968. Google ScholarDigital Library
- K. Morton et al. A Measurement Study of Two Web-based Collaborative Visual Analytics Systems. Technical Report UW-CSE-12-08-01, U. of Washington, Aug 2012.Google Scholar
- K. Morton et al. Dynamic Workload Driven Data Integration in Tableau. In SIGMOD, 2012. Google ScholarDigital Library
- A. Raffio et al. Clip: a Visual Language for Explicit Schema Mappings. In ICDE, 2008. Google ScholarDigital Library
- A. D. Sarma et al. Finding Related Tables. In SIGMOD, 2012. Google ScholarDigital Library
- M. Stonebraker et al. Data curation at scale: The data tamer system. In CIDR, 2013.Google Scholar
- R. Tuchinda et al. Building mashups by example. In IUI, 2008. Google ScholarDigital Library
- F. B. Viegas et al. Many eyes: A site for visualization at internet scale. IEEE TVCG, 13(6), 2007. Google ScholarDigital Library
- W. Willett et al. Commentspace: Structured support for collaborative visual analytics. In CHI, 2011. Google ScholarDigital Library
- G. Wolf et al. The Quantified Self. TED, 2010.Google Scholar
- E. Wu et al. Scorpion: Explaining Away Outliers in Aggregate Queries. In VLDB, 2013. Google ScholarDigital Library
Index Terms
- Support the data enthusiast: challenges for next-generation data-analysis systems
Recommendations
Adding data provenance support to Apache Spark
Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours ...
Comments