Multi-Dimensional Event Data in Graph Databases

Abstract

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as “directly/eventually-follows,” it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data, but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on labeled property graphs that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that interact synchronously and asynchronously. The queries allow for efficiently converting large real-life event data sets into our data model, and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multi-dimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically, aggregation queries on our data model enable process mining over multiple inter-related entities using off-the-shelf technology.

Causal Process Mining from Relational Databases with Domain Knowledge

“…Pursuing a slightly different direction, Fahland [25] presents formal semantics for processes with N:M interactions considering unbounded dynamic synchronization of transitions, cardinality constraints, and history-based correlation of token identities. In [26] and [15], the cardinality is captured by the concept of one event being part of multiple cases by using labeled property graphs. Esser and Fahland [15,26] transform event logs into graphs to store structural and temporal relationships between events.…”

“…In [26] and [15], the cardinality is captured by the concept of one event being part of multiple cases by using labeled property graphs. Esser and Fahland [15,26] transform event logs into graphs to store structural and temporal relationships between events. They discuss how edges between events define a causal relationship, based on the assumption that events are related to each other if there is an underlying entity to which both events belong.…”

“…Here, interacting objects cannot be traced using a single case identifier. Recent works on artifact-related PM [9] or object-centric PM [10,11] as well as approaches that consider relational databases [12,13,14] or multi-dimensional event data [15] address the problem by associating a single event with one or more objects. However, they do not exploit causal domain knowledge such as visible in the foreign key relationships of the databases storing event data.…”

AI-Augmented Business Process Management Systems: A Research Manifesto

“…Complemented by contemporary advances in causal reasoning (e.g., [71,72]), such infrastructure could be developed to determine plausible justifications for process decisions and results (intermediary or eventual) in real-time, to allow for valid establishment of reasoning in retrospect. Specifically, event knowledge graphs [23] which encode behavioral and causal inter-dependencies of objects and actors over time in the context of process flows and process knowledge allow to symbolically represent situations of all kinds for situation-aware reasoning. Such techniques may be used to facilitate the (automatic or by humans) tracking of execution consistency, for better understanding of process flows and process outcomes, and to drive ongoing process improvements (at either design-or retraction at run-time).…”

“…What emerges is the need to explicitly distinguish different kinds of objects as well as their "trajectories" over time. We envision that a simple, but flexibly extensible graph-based model (e.g., [23]) are capable of encoding the concepts and phenomena so far studied separately in a uniform format.…”