30 research outputs found
The WHY in Business Processes: Discovery of Causal Execution Dependencies
A crucial element in predicting the outcomes of process interventions and
making informed decisions about the process is unraveling the genuine
relationships between the execution of process activities. Contemporary process
discovery algorithms exploit time precedence as their main source of model
derivation. Such reliance can sometimes be deceiving from a causal perspective.
This calls for faithful new techniques to discover the true execution
dependencies among the tasks in the process. To this end, our work offers a
systematic approach to the unveiling of the true causal business process by
leveraging an existing causal discovery algorithm over activity timing. In
addition, this work delves into a set of conditions under which process mining
discovery algorithms generate a model that is incongruent with the causal
business process model, and shows how the latter model can be methodologically
employed for a sound analysis of the process. Our methodology searches for such
discrepancies between the two models in the context of three causal patterns,
and derives a new view in which these inconsistencies are annotated over the
mined process model. We demonstrate our methodology employing two open process
mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery
technique. We apply it on a synthesized dataset and on two open benchmark data
sets.Comment: 20 pages, 19 figure
Extending Event-Driven Architecture for Proactive Systems
ABSTRACT Proactive Event-Driven Computing is a new paradigm, in which a decision is not made due to explicit users' requests nor is it made as a response to past events. Rather, the decision is autonomously triggered by forecasting future states. Proactive event-driven computing requires a departure from current event-driven architectures to ones capable of handling uncertainty and future events, and real-time decision making. We present a proactive event-driven architecture for Scalable Proactive Event-Driven Decision-making (SPEEDD), which combines these capabilities. The proposed architecture is composed of three main components: complex event processing, real-time decision making, and visualization. This architecture is instantiated by a real use case from the traffic management domain. In the future, the results of actual implementations of the use case will help us revise and refine the proposed architecture
The Boost 4.0 Experience
In the last few years, the potential impact of big data on the manufacturing industry has received enormous attention. This chapter details two large-scale trials that have been implemented in the context of the lighthouse project Boost 4.0. The chapter introduces the Boost 4.0 Reference Model, which adapts the more generic BDVA big data reference architectures to the needs of Industry 4.0. The Boost 4.0 reference model includes a reference architecture for the design and implementation of advanced big data pipelines and the digital factory service development reference architecture. The engineering and management of business network track and trace processes in high-end textile supply are explored with a focus on the assurance of Preferential Certification of Origin (PCO). Finally, the main findings from these two large-scale piloting activities in the area of service engineering are discussed.publishersversionpublishe
Augmented Business Process Management Systems: A Research Manifesto
Augmented Business Process Management Systems (ABPMSs) are an emerging class
of process-aware information systems that draws upon trustworthy AI technology.
An ABPMS enhances the execution of business processes with the aim of making
these processes more adaptable, proactive, explainable, and context-sensitive.
This manifesto presents a vision for ABPMSs and discusses research challenges
that need to be surmounted to realize this vision. To this end, we define the
concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we
discuss core characteristics of an ABPMS, and we derive a set of challenges to
realize systems with these characteristics.Comment: 19 pages, 1 figur
Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020
We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2
A Framework for Verifiable and Auditable Collaborative Anomaly Detection
Collaborative and Federated Leaning are emerging approaches to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent’s performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification of rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the development of accurate classifiers with an effective insight-sharing mechanism that does not break the data integrity. Moreover, we explain how the new architecture can be readily integrated in a blockchain infrastructure to ensure the verifiable and auditable execution of the algorithm. Furthermore, we discuss how this work may set the basis for a more general approach for the design of collaborative ensemble-learning methods beyond the specific task and architecture discussed in this paper