Number of co-authors:32
Number of publications with 3 favourite co-authors:Sean Kandel:2Chris Olston:2Jeffrey Heer:2
Joseph M. Hellerstein's 3 most productive colleagues in number of publications:W. Bruce Croft:124Hector Garcia-Moli..:47Andreas Paepcke:43
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Publications by Joseph M. Hellerstein (bibliography)
Kandel, Sean, Parikh, Ravi, Paepcke, Andreas, Hellerstein, Joseph M. and Heer, Jeffrey (2012): Profiler: integrated statistical analysis and visualization for data quality assessment. In: Proceedings of the 2012 International Conference on Advanced Visual Interfaces 2012. pp. 547-554.
Data quality issues such as missing, erroneous, extreme and duplicate values undermine analysis and are time-consuming to find and fix. Automated methods can help identify anomalies, but determining what constitutes an error is context-dependent and so requires human judgment. While visualization tools can facilitate this process, analysts must often manually construct the necessary views, requiring significant expertise. We present Profiler, a visual analysis tool for assessing quality issues in tabular data. Profiler applies data mining methods to automatically flag problematic data and suggests coordinated summary visualizations for assessing the data in context. The system contributes novel methods for integrated statistical and visual analysis, automatic view suggestion, and scalable visual summaries that support real-time interaction with millions of data points. We present Profiler's architecture -- including modular components for custom data types, anomaly detection routines and summary visualizations -- and describe its application to motion picture, natural disaster and water quality data sets.
© All rights reserved Kandel et al. and/or ACM Press
Guo, Philip J., Kandel, Sean, Hellerstein, Joseph M. and Heer, Jeffrey (2011): Proactive wrangling: mixed-initiative end-user programming of data transformation scripts. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 65-74.
Analysts regularly wrangle data into a form suitable for computational tools through a tedious process that delays more substantive analysis. While interactive tools can assist data transformation, analysts must still conceptualize the desired output state, formulate a transformation strategy, and specify complex transforms. We present a model to proactively suggest data transforms which map input data to a relational format expected by analysis tools. To guide search through the space of transforms, we propose a metric that scores tables according to type homogeneity, sparsity and the presence of delimiters. When compared to "ideal" hand-crafted transformations, our model suggests over half of the needed steps; in these cases the top-ranked suggestion is preferred 77% of the time. User study results indicate that suggestions produced by our model can assist analysts' transformation tasks, but that users do not always value proactive assistance, instead preferring to maintain the initiative. We discuss some implications of these results for mixed-initiative interfaces.
© All rights reserved Guo et al. and/or ACM Press
Chen, Kuang, Hellerstein, Joseph M. and Parikh, Tapan S. (2010): Designing adaptive feedback for improving data entry accuracy. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 239-248.
Data quality is critical for many information-intensive applications. One of the best opportunities to improve data quality is during entry. Usher provides a theoretical, data-driven foundation for improving data quality during entry. Based on prior data, Usher learns a probabilistic model of the dependencies between form questions and values. Using this information, Usher maximizes information gain. By asking the most unpredictable questions first, Usher is better able to predict answers for the remaining questions. In this paper, we use Usher's predictive ability to design a number of intelligent user interface adaptations that improve data entry accuracy and efficiency. Based on an underlying cognitive model of data entry, we apply these modifications before, during and after committing an answer. We evaluated these mechanisms with professional data entry clerks working with real patient data from six clinics in rural Uganda. The results show that our adaptations have the potential to reduce error (by up to 78%), with limited effect on entry time (varying between -14% and +6%). We believe this approach has wide applicability for improving the quality and availability of data, which is increasingly important for decision-making and resource allocation.
© All rights reserved Chen et al. and/or their publisher
Abiteboul, Serge, Agrawal, Rakesh, Bernstein, Philip A., Carey, Michael J., Ceri, Stefano, Croft, W. Bruce, DeWitt, David J., Franklin, Michael J., Garcia-Molina, Hector, Gawlick, Dieter, Gray, Jim, Haas, Laura M., Halevy, Alon Y., Hellerstein, Joseph M., Ioannidis, Yannis E., Kersten, Martin L. and Pazzani, Michael J. (2005): The Lowell database research self-assessment. In Communications of the ACM, 48 (5) pp. 111-118.
Hellerstein, Joseph M., Avnur, Ron, Chou, Andy, Hidber, Christian, Olston, Chris, Raman, Vijayshankar, Roth, Tali and Haas, Peter J. (1999): Interactive data Analysis: The Control Project. In IEEE Computer, 32 (8) pp. 51-59.
Olston, Chris, Stonebraker, Michael, Aiken, Alexander and Hellerstein, Joseph M. (1998): VIQING: Visual Interactive Querying. In: VL 1998 1998. pp. 162-169.
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