EdbA'20 -
First International Workshop on Event Data and
Behavioral Analytics

Padua, Italy, 5 October 2020

Co-located at ICPM 2020

Due to the exceptional circumstances of the COVID-19 outbreak, both EdbA 2020 and the main conference ICPM 2020 were organized as a fully virtual conference, with no travel involved.
Call for Papers

Over the past decades, capturing, storing and analyzing event data has gained attention in various domains such as process mining, clickstream analytics, IoT analytics, e-commerce and retail analytics, online gaming analytics, security analytics, website traffic analytics and preventive maintenance to name a few. It even resulted in the birth of new research domains such as behavioral informatics, behavioral analytics and behavioral operations research. The interest in event data lies in its analytical potential as it captures the dynamic behavior of people, objects and/or systems at a fine-grained level.

While each of these domains have their own applications and idiosyncrasies, they share the common denominator of event data and the objective to analyze behavior. Yet, these domains also differ in underlying assumptions and techniques used. Therefore, the objective of this workshop is to provide a forum to practitioners and researchers for studying a quintessential, minimal notion of events as the common denominator for records of discrete behavior in all its forms, and to study, develop and discuss techniques and methods for behavioral analytics based on all kinds of events. 

The Event Data & Behavioral Analytics (edba) workshop considers as its starting point how to gather event data being recorded at various sources and contexts, how to store event data in various forms, and how to exploit event data for analysis of behavior of various kinds. Event data at different levels of granularity are considered, ranging from frequent sensor-based events in IoT settings to recordings of aggregate or long-running behavior involving time intervals and rich information. Behavior often involves multiple entities, objects, and actors to which events can be correlated in various ways. In these situations, a unique explicit process notion does either not exist, is unclear or different processes or dynamics could be recorded in the same dataset.

The workshop aims to further the development of new (or the novel application of existing) techniques, algorithms and data structures for recording, storing, managing, processing, analyzing, and visualizing event data in various forms. The workshop welcomes different types of submissions, i.e. original research papers, case study reports, position papers, idea papers, challenge papers and WiP papers on event data and behavioral analytics.

The topics considered in the workshop consist of, but are not limited to:

  • Augmentation of fine-grained event data to higher order activities or behavior
  • Storage, integration, and querying of behavioral event data
  • Representation and analysis of event data without a unique case identifier (without case identifier or with multiple case identifiers present)
  • Monitoring and detection of complex behavior
  • Diagnosis of behavior, including root-cause analysis, variance analysis, cluster analysis and many other exploratory analysis techniques
  • Visual analytics of (complex) behavior
  • Behavior Pattern detection, e.g., in real-time location data or other types of context-rich data
  • Outlier Behavior Detection
  • Behavior Prediction
  • Prescriptive analytics which predicts behavior and prescribes which action could steer behavior in a specific direction
  1. Manuel Wetzel, Agnes Koschmider and Thomas Wilke. Visually Representing History Dependencies in Event Logs.
  2. Philip Waibel, Christian Novak, Saimir Bala, Kate Revoredo and Jan Mendling. Analysis of Business Process Batching using Causal Event Models.
  3. Gerry Murphy. Process “Procespecting” to Improve Renewable Energy Interconnection Queues: A Case Study.
  1. Adriano Augusto, Marlon Dumas and Marcello La Rosa. Automated Discovery of Process Models with True Concurrency and Inclusive Choices.
  2. Yang Lu, Qifan Chen and Simon Poon. A Novel Approach to Discover Switch Behaviours in Process Mining.
  1. Dominik Janssen, Felix Mannhardt, Agnes Koschmider and Sebastiaan J. van Zelst. Process Model Discovery from Sensor Event Data.
  2. Greg Van Houdt, Benoît Depaire and Niels Martin. Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study.

All accepted papers are published by Springer in the Lecture Notes in Business Information Processing (LNBIP) series. 


Organizing Committee

Program Committee