Towards a process-oriented analysis of blockchain data

Blockchains sequentially store the history of transactional information, in a virtually immutable and distributed way. Moreover, second-generation blockchains such as Ethereum are programmable environments, and every operation invocation towards the smart contracts corresponds to a transaction sequentially collated in the ledgers. They thus allow for the controlled enactment of multi-party processes as well as the immutable recording of their distributed execution. Despite the verification, tracking, and monitoring of such blockchain-enabled processes appears paramount, a formal and implemented framework encompassing those aspects is still a mostly unexplored research avenue. The talk revolves around the current state of the art, as well as the opportunities and challenges that arise when it comes to conducting a process-oriented analysis on data stemming from blockchains, from a representation and modelling perspective

Semantics-aware event data transformation for process mining

Process mining comprises methods to analyze organizational processes based on event data recorded by information systems during process execution. These methods generate actionable insights into how processes are truly executed and thereby support their improvement. However, the characteristics of event data that is available in organizations often differs from the data needs of process mining analyses. Specifically, unavailable data limits process analysis options, overly fine-granular data leads to uninformative process mining results, and inaccurate data even leads to incorrect results that do not mirror reality. These problems severely impact the opportunities and outcomes of process mining analyses. The goal of this doctoral thesis is to support organizations in overcoming these problems so that they can analyze their processes effectively using the event data available to them. Its main contributions are five approaches that automatically transform event data so that its characteristics satisfy the data needs of particular process analysis purposes. Specifically, we propose approaches to (1) annotate event data with semantic components to enable semantics-aware process analysis, (2) abstract fine-granular event data while adhering to user-defined requirements to enable purpose-driven process analysis, (3) transform user interaction data to task-level events to enable process analysis, (4) extract object-related information from event data to enable object-centric process analysis, and (5) detect best-practice violations in event data to provide insights into data-quality and conformance issues. The common driver of these approaches is the consideration of the semantics, i.e., the meaning, of events. We demonstrate the efficacy of the proposed approaches through quantitative evaluations using data obtained in real-world settings. Furthermore, we present application scenarios that underscore the usefulness of our approaches and highlight the analysis opportunities they enable for organizations