Index-Accelerated Pattern Matching in Event Stores

Körber, Michael; Glombiewski, Nikolaus; Seeger, Bernhard

doi:10.1145/3448016.3457245

Cited by 5 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Still, this does not change the linearity of the match concerning the number of tuples in the table. A recent work speeds up MR by using indexes in relational databases [22] for strict contiguity patterns, i.e., patterns where events are in strict sequence. Order compliance patterns frequently refer to eventuality rather than strict order, limiting the use of indexes to accelerate the matching process.…”

Section: Related Workmentioning

confidence: 99%

Efficient Checking of Timed Order Compliance Rules over Graph-encoded Event Logs

Zaki¹,

Helal²,

Awad³

et al. 2022

Preprint

View full text Add to dashboard Cite

Validation of compliance rules against process data is a fundamental functionality for business process management. Over the years, the problem has been addressed for different types of process data, i.e., process models, process event data at runtime, and event logs representing historical execution. Several approaches have been proposed to tackle compliance checking over process logs. These approaches have been based on different data models and storage technologies including relational databases, graph databases, and proprietary formats. Graph-based encoding of event logs is a promising direction that turns several process analytics 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. Efficiency is important due to the interactive nature of compliance checking. Thus, compliance checking 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 representation, realized in Neo4J, and show the benefits of these encoding on a special class of queries, namely timed order compliance rules. Compared to a baseline encoding, our experiments show up to 5x speed up in the querying time as well as a 3x reduction in the graph size.

show abstract

Section: Related Workmentioning

confidence: 99%