Efficient Checking of Timed Ordered Anti-patterns over Graph-Encoded Event Log

M. Zaki, Nesma; M. A. Helal, Iman; E. Hassanein, Ehab; Awad, Ahmed

Efficient Checking of Timed Ordered Anti-patterns over Graph-Encoded Event Log

Date

2022

Authors

Publisher

Springer, Cham

Abstract

Event logs are used for a plethora of process analytics and mining techniques. A class of these mining activities is conformance (compliance) checking. The goal is to identify the violation of such patterns, i.e., anti-patterns. Several approaches have been proposed to tackle this analysis task. These approaches have been based on differ ent data models and storage technologies of the event log including rela tional databases, graph databases, and proprietary formats. Graph-based encoding of event logs is a promising direction that turns several process analytic tasks into queries on the underlying graph. Compliance checking is one class of such analysis tasks. In this paper, we argue that encoding log data as graphs alone is not enough to guarantee efficient processing of queries on this data. Effi ciency is important due to the interactive nature of compliance checking. Thus, anti-pattern detection would benefit from sub-linear scanning of the data. Moreover, as more data are added, e.g., new batches of logs arrive, the data size should grow sub-linearly to optimize both the space of storage and time for querying. We propose two encoding methods using graph representations, realized in Neo4J & SQL Graph Database, and show the benefits of these encoding on a special class of queries, namely timed ordered anti-patterns. Compared to several baseline encoding, our experiments show up to 5x speed up in the querying time as well as a 3x reduction in the graph size.

Keywords

Anti pattern detection · Process mining · Graph-encoded event logs

Citation

Zaki, N.M., Helal, I.M.A., Hassanein, E.E., Awad, A. (2023). Efficient Checking of Timed Ordered Anti-patterns over Graph-Encoded Event Logs. In: Fournier-Viger, P., Hassan, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2022. Lecture Notes in Computer Science, vol 13761. Springer, Cham.