Demo Data from SIGIR 2026 / reviewers in the wild / expert
Adrian Cryer
dblp:362/4414
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0007-7887-0603ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 77% Data stream processing · 23% | |
| Software engineering, system software, and programming languages
1 paper |
Services computing and microservices · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › process mining
event log analysis |
0.7 | 1 | 2023 | Cross-Instance Regulatory Compliance Checking of Business Process Event Logs · IEEE Trans. Software Eng. 2023 |
Services computing and microservices › business process management
business process compliance checking |
0.7 | 1 | 2023 | Cross-Instance Regulatory Compliance Checking of Business Process Event Logs · IEEE Trans. Software Eng. 2023 |
Methods — techniques the papers use, named apart from their topics
sequence attribute function evaluation · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Cross-Instance Regulatory Compliance Checking of Business Process Event LogsabstractEvent logs capture the execution of business processes, such that each task is represented by an event and each individual execution is a chronological sequence of events, called an event trace. Event logs allow after-the-act and runtime analysis of deployed business processes to verify whether their execution complies with regulations and business requirements. Checking the compliance of a single sequence of events in a trace is straightforward and a number of approaches have been proposed to address this. However, some regulations or business rules span multiple process instances, requiring a cross-instance analysis. In order to check whether such requirements are maintained at all times, multiple traces need to be analysed together, which can result in a combinatorial computational complexity. In this article, we present a novel approach that efficiently checks runtime regulatory compliance based on event logs, while supporting cross-instance rule evaluation and extensible function evaluation over sequences of attribute data values. The efficiency and applicability of the proposed method is tested in a two-pronged evaluation, showing a significant improvement over existing techniques with respect to capabilities as well as computational complexity. The approach presented in this paper is subject to a patent application, with patent number WO2021/248201. Nick R. T. P. van Beest, Heerko Groefsema, Adrian Cryer, Guido Governatori, Silvano Colombo Tosatto, Hannah M. S. Burke |
IEEE Trans. Software Eng. | 3 |