An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
A survey of data provenance in e-science
ACM SIGMOD Record
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
VEIL: A System for Certifying Video Provenance
ISM '07 Proceedings of the Ninth IEEE International Symposium on Multimedia
Annotation and provenance tracking in semantic web photo libraries
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
Summary abstract for the 2nd ACM international workshop on multimedia analysis for ecological data
Proceedings of the 21st ACM international conference on Multimedia
A video processing and data retrieval framework for fish population monitoring
Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
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In-situ video recording of underwater ecosystems is able to provide valuable information for biology research and natural resources management, e.g. changes in species abundance. Searching the videos manually, however, requires costly human effort. Our video analysis tool supports the key task of counting different species of fish, allowing marine biologists to query the video collection without watching the videos. To be suitable for scientific research on changes in species abundance, the video data must include data provenance information that reflects the potential biases introduced through the video processing.In order to trust the analyses made by the system, we need to provide expert users with sufficient information to allow them to interpret these potential biases. We conducted two user studies to design a user interface that includes data provenance information. Our qualitative analysis discusses the support for understanding the reliability of video analysis, and trusting the results it produces. Our main finding is that disclosing details about the video processing and provenance data allows biologists to compare the results with their traditional statistical methods, thus increasing their trust in the results.