A technique called user views has recently been proposed to focus user attention on relevant information in response to provenance queries over workflow executions [1, 2]: Given user input on what modules in the workflow specification are relevant to the user, a user view is a concise representation that clusters together modules to create a small number of composite modules (or clusters) such that (1) each composite module in a user view contains at most one relevant (atomic) module, thus assuming the "meaning" of that module; and (2) no control or data dependencies (either direct or indirect) are introduced (soundness) or removed (completeness) between relevant modules. The goal is to find a user view with a smallest number of composite modules.

Workflow systems have become increasingly popular for managing experiments where many bioinformatics tasks are chained together. Due to the large amount of data generated by these experiments and the need for reproducible results, provenance has become of paramount importance. Workflow systems are therefore starting to provide support for querying provenance. However, the amount of provenance information may be overwhelming, so there is a need for abstraction mechanisms to help users focus on the most relevant information. The technique we pursue is that of "user views". Since bioinformatics tasks may themselves be complex sub-workflows, a user view determines what level of sub-workflow the user can see, and thus what data and tasks are visible in provenance queries. In this paper, we formalize the notion of user views, demonstrate how they can be used in provenance queries, and give an algorithm for generating a user view based on which tasks are relevant for the user. We then describe our prototype and give performance results. Although presented in the context of scientific workflows, the technique applies to other data-oriented workflows.

Abstract ZOOM* UserViews presents a model of provenance for scientific workflows that is simple, generic, and yet sufficiently expressive to answer questions of data and step provenance that have been encountered in a large variety of scientific case studies. In addition, ZOOM builds on the concept of composite step‐classes—or sub‐workflows—which is present in many scientific workflow systems to develop a notion of user views. This paper discusses the design and implementation of ZOOM in the context of the queries posed by the provenance challenge, and shows how user views affect the level of granularity at which provenance information can be seen and reasoned about. Copyright © 2007 John Wiley & Sons, Ltd.

Article ORCHESTRA: facilitating collaborative data sharing Share on Authors: Todd J. Green University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile , Grigoris Karvounarakis University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile , Nicholas E. Taylor University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile , Olivier Biton University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile , Zachary G. Ives University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile , Val Tannen University of Pennsylvania, Philadelphia, PA University of Pennsylvania, Philadelphia, PAView Profile Authors Info & Claims SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of dataJune 2007 Pages 1131–1133https://doi.org/10.1145/1247480.1247631Published:11 June 2007 41citation70DownloadsMetricsTotal Citations41Total Downloads70Last 12 Months20Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

UNLABELLED: Biologists are frequently faced with the problem of integrating information from multiple heterogeneous sources with their own experimental data. Given the large number of public sources, it is difficult to choose which sources to integrate without assistance. When doing this manually, biologists differ in their preferences concerning the sources to be queried as well as the strategies, i.e. the querying process they follow for navigating through the sources. In response to these findings, we have developed BioGuide to assist scientists search for relevant data within external sources while taking their preferences and strategies into account. In this article, we present BioGuideSRS, a user-friendly system which automatically retrieves instances of data by using BioGuide on top of the sequence retrieval system (SRS). BioGuideSRS is an Applet that can be run from its web page on any system with Java 5.0. AVAILABILITY: http://www.bioguide-project.net.