Workshop Program

Most information is now published in complex, structured, evolving datasets or databases. As such, there is increasing demand that this digital information should be treated in the same way as conventional publications and cited appropriately. While principles and standards have been developed for data citation, they are unlikely to be used unless we can couple the process of extracting information with that of providing a citation for it. I will discuss the problem of automatically generating citations for data in a database given how the data was obtained (the query) as well as the content (the data), and show how the problem of generating a citation is related to a well-understood problem in databases. I will also discuss the connection between data citation and provenance: are they different versions of the same problem or different problems entirely?

Provenance is well-understood for relational query operators. Increasingly, however, data analytics is incorporating operations expressed through linear algebra: machine learning operations, network centrality measures, and so on. In this paper, we study provenance information for matrix data and linear algebra operations. Our core technique builds upon provenance for aggregate queries and constructs a K semialgebra. This approach tracks provenance by annotating matrix data and propagating these annotations through linear algebra operations. We investigate applications in matrix inversion and graph analysis.

Explaining why some data are not part of a query result has recently gained significant interest. One use of why-not explanations is adapting queries to meet user expectations. We propose an algorithm to automatically generate changes to a query, by using Why-Not polynomials, one form of why-not explanations based on query operators.We improve on the state of the art in three aspects: (i) we refine both selection and join predicates, (ii) we guarantee a maximum similarity to the original query, and (iii) we cover all possible cases of why the desired data was missing. A prototype implementation shows the applicability of our approach in practice.

Using pervasive provenance to secure mainstream systems has recently attracted interest from industry and government. Recording, storing and managing all of the provenance associated with a system is a considerable challenge. Analyzing the resulting noisy, heterogeneous, continuously-growing provenance graph adds to this challenge, and apparently necessitates segmentation, that is, approximating, compressing or summarizing part or all of the graph in order to identify patterns or features. In this paper, we describe this new problem space for provenance data management, contrast it with related problem spaces addressed by prior work on provenance abstraction and sanitization, and highlight challenges and future directions toward solutions to the provenance segmentation problem.

Provenance middleware (such as SPADE) lets individuals and applications use a common framework for reporting, storing, and querying records that characterize the history of computational processes and resulting data artifacts. Previous efforts have addressed a range of issues, from instrumentation techniques to applications in the domains of scientific reproducibility and data security. Here we report on our experience adapting SPADE to handle large provenance data sets. In particular, we describe two motivating case studies, several challenges that arose from managing provenance at scale, and our approach to address each concern.

In recent years the need to simplify or to hide sensitive information in provenance has given way to research on provenance abstraction. In the context of scientific workflows, existing research provides techniques to semi-automatically create abstractions of a given workflow description, which is in turn used as filters over the workflow’s provenance traces. An alternative approach that is commonly adopted by scientists is to build workflows with abstractions embedded into the workflow’s design, such as using subworkflows. This paper reports on the comparison of manual versus semi-automated approaches in a context where result abstractions are used to filter report-worthy results of computational scientific analyses. Specifically; we take a real-world workflow containing user-created design abstractions and compare these with abstractions created by ZOOM*UserViews andWorkflow Summaries systems. Our comparison shows that semi-automatic and manual approaches largely overlap from a process perspective, meanwhile, there is a dramatic mismatch in terms of data artefacts retained in an abstracted account of derivation.We discuss reasons and suggest future research directions.

Scientific Workflow management systems have been largely adopted by data-intensive science communities. Many efforts have been dedicated to the representation and exploitation of provenance to improve reproducibility in data-intensive sciences. However, few works address the mining of provenance graphs to annotate the produced data with domain-specific context for better interpretation and sharing of results. In this paper, we propose PoeM, a lightweight framework for mining provenance in scientific workflows. PoeM allows to produce linked in silico experiment reports based on workflow runs. PoeM leverages semantic web technologies and reference vocabularies ((PROV-O, P-Plan) to generate provenance mining rules and finally assemble linked scientific experiment reports (Micropublications, Experimental Factor Ontology). Preliminary experiments demonstrate that PoeM enables the querying and sharing of Galaxy-processed genomic data as 5-star linked datasets.

The cost of deriving actionable knowledge from large datasets has been decreasing thanks to a convergence of positive factors: low cost data generation, inexpensively scalable storage and processing infrastructure (cloud), software frameworks and tools for massively distributed data processing, and parallelisable data analytics algorithms. One observation that is often overlooked, however, is that each of these elements is not immutable, rather they all evolve over time. As those datasets change over time, the value of their derivative knowledge may decay, unless it is preserved by reacting to those changes. Our broad research goal is to develop models, 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 human variants in support of genetic diagnosis.