Lookup NU author(s): Dr Paolo Missier,
Dr Jacek Cala
This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by Usenix, 2016.
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The cost of deriving actionable knowledge from large datasets has been decreasing thanks to a convergence of positive fac- tors: low cost data generation, inexpensively scalable stor- age and processing infrastructure (cloud), software frame- works and tools for massively distributed data processing, and parallelisable data analytics algorithms. One observa- tion that is often overlooked, however, is that each of these elements is not immutable, rather they all evolve over time. This suggests that the value of such derivative knowledge may decay over time, unless it is preserved by reacting to those changes. Our broad research goal is to develop mod- els, methods, and tools for selectively reacting to changes by balancing costs and benefits, i.e. through complete or partial re-computation of some of the underlying processes. In this paper we present an initial model for reasoning about change and re-computations, and show how analysis of detailed provenance of derived knowledge informs re-computation decisions. We illustrate the main ideas through a real-world case study in genomics, namely on the interpretation of hu- man variants in support of genetic diagnosis.
Author(s): Missier P, Cala J, Wijaya E
Editor(s): Sarah Cohen Boulakia
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP '16)
Year of Conference: 2016
Print publication date: 09/06/2016
Online publication date: 09/06/2016
Acceptance date: 23/04/2016