# Multi-Dimensional Event Data in Graph Databases

arXiv: https://arxiv.org/abs/2005.14552 · HTML: https://mlsanity.com/paper/2005.14552/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 14 of 14)

- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Defining Cases and Variants for Object-Centric Event Data](https://mlsanity.com/paper/2208.03235/) (2022)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [OPerA: Object-Centric Performance Analysis](https://mlsanity.com/paper/2204.10662/) (2022)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [A Framework for Extracting and Encoding Features from Object-Centric Event Data](https://mlsanity.com/paper/2209.01219/) (2022)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs](https://mlsanity.com/paper/2210.12080/) (2022)
- [Inferring Unobserved Events in Systems With Shared Resources and Queues](https://mlsanity.com/paper/2103.00167/) (2021)
- [Towards Knowledge-Centric Process Mining](https://mlsanity.com/paper/2301.10927/) (2023)
- [Posets and Bounded Probabilities for Discovering Order-inducing Features in Event Knowledge Graphs](https://mlsanity.com/paper/2410.06065/) (2024)
- [OCPM2: Extending the Process Mining Methodology for Object-Centric Event Data Extraction](https://mlsanity.com/paper/2503.10735/) (2025)
- [Theory: Multidimensional Space of Events](https://mlsanity.com/paper/2505.11566/) (2025)

## Abstract (arXiv)

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 multidimensional 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 interrelated entities using off-the-shelf technology.