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

TL;DR

This paper introduces two graph encoding methods for event logs in Neo4J to efficiently check timed order compliance rules, achieving significant speedups and storage reductions over baseline methods.

Contribution

It proposes novel graph encoding techniques for event logs that enable faster compliance rule queries and reduced storage, enhancing process analytics efficiency.

Findings

Up to 5x faster query performance

3x reduction in graph size

Effective encoding for timed order compliance rules

Abstract

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.