Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and

Analysing Quality of Resilience in Fish4Knowledge Video Analysis Workflows

The Fish4Knowledge (F4K) project involves analysing video generated from multiple camera feeds to support environmental and ecological assessment. A workflow engine is utilised in the project which deals with on-demand user queries and batch queries, selection of a suitable computing platform on which to enact the workflow along with a selection of suitable software modules to use to support analysis. A workflow monitor is also made use of, which handles the seamless execution and error monitoring of jobs on a heterogeneous computing platform. End users of such workflow generally include marine biologists, who are often primarily interested in the accuracy, performance and resilience of the workflows they execute. We describe how such users can be provided with possible workflow alternatives that trade off these three characteristics, based on previously recorded (historical) data. We describe a Quality of Resilience (QoR) metric that can be associated with multiple workflow alternatives, that enable such users to make more informed decisions about which alternative to choose