EdgeMiner: Distributed Process Mining at the Data Sources

TL;DR

EdgeMiner is a distributed process mining algorithm that operates directly on sensor nodes in real-time, enhancing scalability and privacy by reducing data centralization and communication overhead.

Contribution

It introduces a novel distributed process mining approach that works on sensor nodes, significantly reducing communication and storage needs compared to centralized methods.

Findings

Reduces communication overhead by up to 96%.

Queries stabilize after few events, querying less than 2.5% nodes.

Proves correctness of the distributed algorithm.

Abstract

Process mining is moving beyond mining traditional event logs and nowadays includes, for example, data sourced from sensors in the Internet of Things (IoT). The volume and velocity of data generated by such sensors makes it increasingly challenging to efficiently process the data by traditional process discovery algorithms, which operate on a centralized event log. This paper presents EdgeMiner, an algorithm for distributed process mining operating directly on sensor nodes on a stream of real-time event data. In contrast to centralized algorithms, EdgeMiner tracks each event and its predecessor and successor events directly on the sensor node where the event is sensed and recorded. As EdgeMiner aggregates direct successions on the individual nodes, the raw data does not need to be stored centrally, thus improving both scalability and privacy. We analytically and experimentally show the correctness of EdgeMiner. In addition, our evaluation results show that EdgeMiner determines predecessors for each event efficiently, reducing the communication overhead by up to 96% compared to querying all nodes. Further, we show that the number of queried nodes stabilizes after relatively few events, and batching predecessor queries in groups reduces the average queried nodes per event to less than 2.5%.