TaPP 2018 Workshop Program

When computational experiments include only datasets, they could be shared through the Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs) which point to these resources. However, experiments seldom include only datasets, but most often also include software, execution results, provenance, and other associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While an entire Research Object may be citable using a URI or a DOI, it is often desirable to cite specific sub-components of a research object to help identify, authorize, date, and retrieve the published sub-components of these objects. In this paper, we present an approach to automatically generate citations for sub-components of research objects by using the object's recorded provenance traces. The generated citations can be used "as is" or taken as suggestions that can be grouped and combined to produce higher level citations.

Intrusion detection is an arms race; attackers evade intrusion detection systems by developing new attack vectors to sidestep known defense mechanisms. Provenance provides a detailed, structured history of the interactions of digital objects within a system. It is ideal for intrusion detection, because it offers a holistic, attack-vector-agnostic view of system execution. As such, provenance graph analysis fundamentally strengthens detection robustness.We discuss the opportunities and challenges associated with provenance-based intrusion detection and provide insights based on our experience building such systems.

We review the basics of data provenance in relational databases. We describe different provenance formalisms, from Boolean provenance to provenance semirings and beyond, that can be used for a wide variety of purposes, to obtain additional information on the output of a query. We discuss representation systems for data provenance, circuits in particular, with a focus on practical implementation. Finally, we explain how provenance is practically used for probabilistic query evaluation in probabilistic databases.

Data provenance is a valuable tool for protecting and troubleshooting distributed systems. Careful design of the provenance components reduces the impact on the design, implementation, and operation of the distributed system. In this paper, we present Curator, a provenance management toolkit that can be easily integrated with microservice-based systems and other modern distributed systems. This paper describes the design of Curator and discusses how we have used Curator to add provenance to distributed systems. We find that our approach results in no changes to the design of these distributed systems and minimal additional code and dependencies to manage. In addition, Curator uses the same scalable infrastructure as the distributed system and can therefore scale with the distributed system.