A provenance framework for data-dependent process analysis

Abstract

A data-dependent process (DDP) models an application whose control flow is guided by a finite state machine, as well as by the state of an underlying database. DDPs are commonly found e.g., in e-commerce. In this paper we develop a framework supporting the use of provenance in static (temporal) analysis of possible DDP executions. Using provenance support, analysts can interactively test and explore the effect of hypothetical modifications to a DDP's state machine and/or to the underlying database. They can also extend the analysis to incorporate the propagation of annotations from meta-domains of interest, e.g., cost or access privileges.Toward this goal we note that the framework of semiringbased provenance was proven highly effective in fulfilling similar needs in the context of database queries. In this paper we consider novel constructions that generalize the semiring approach to the context of DDP analysis. These constructions address two interacting new challenges: (1) to combine provenance annotations for both information that resides in the database and information about external inputs (e.g., user choices), and (2) to finitely capture infinite process executions. We analyze our solution from theoretical and experimental perspectives, proving its effectiveness.

Approximated Summarization of Data Provenance

“…[10, 4], provenance is much more powerful than simply a log of the application execution. In particular, the algebraic model of provenance (based on semirings) has been shown to allow to correlate input data with output data; to track important details of the computational process that took place; and to further ([8]) provision the computation result with respect to hypothetical scenarios, namely to observe changes to the result based on changes to the input (without actually rerunning the process). Detailed tracking of provenance was thus proved to be a suitable (and necessary) vehicle for the applications that we have mentioned above.…”

An Innovative Online Process Mining Framework for Supporting Incremental GDPR Compliance of Business Processes

“…The problem of supporting GDPR compliance is also related to the well-understood provenance problem (e.g., [11], [12]). In fact, in order to support GDPR compliance in business process systems, very often suitable metadata are computed and/or derived, but, on the other hand, the problem of querying the origin of such metadata (i.e., their provenance) is relevant as well (e.g., [30], [31] ).…”

Debugging Distributed Systems with Why-Across-Time Provenance

“…A data-dependent process (DDP) is a finite state machine that evaluates queries against a relational database to determine some transitions and uses external requests to trigger other transitions [9,10]. In [9] and [10], Deutch et al extend provenance semirings [13] to linear temporal logic formulas issued against a DDP. Both DDP provenance and wat-provenance aim to extend traditional data provenance to state machines, but they do so in very different ways.…”