An Event Processing Platform for Business Process Management

Herzberg, Nico; Meyer, Andreas; Weske, Mathias

doi:10.1109/edoc.2013.20

Cited by 31 publications

(24 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yet, they always focus on either log or model when it comes to the objectives and the output of those techniques. An exception is the work by Herzberg et al [62], which aims at a strong connection between events and process models.…”

Section: Related Workmentioning

confidence: 99%

“…So far, we could only identify one work in this specific category. Herzberg et al [62] present a framework to correlate events to predefined process event monitoring points (PEMP) in a process model. These PEMPs are attached to the life cycle of activities.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…They do not provide concrete mechanism for the relation of events to PEMPs. While the work in [62] conceptually considers many-to-many relations between activities and events, it assumes that event instances of event classes that are related to multiple activities, always reflect the execution of all related activities. Thus, it is not possible to have context-dependent assignment of events from the same event class to multiple activities.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…During the execution of a business process, many events -like the receiving of an order or the arrival of a package -occur and are automatically recorded by the supporting IT system or in some cases manually by staff in a log book. Unfortunately, these events are often not correlated to the modeled activities and thus, can hardly be used for analysis [62]. Looking at conformance checking and enhancement of process models, it is obvious that the events stemming from IT systems have to be mapped to activities defined in process models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

I like things matching. I have an upright bass, a drum kit and a grand piano that's the same color.-Penn Jillette A B S T R A C T Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during process execution. Aiming at a better process understanding and improvement, this event data can be used to analyze processes using process mining techniques. Process models can be automatically discovered and the execution can be checked for conformance to specified behavior. Moreover, existing process models can be enhanced and annotated with valuable information, for example for performance analysis. While the maturity of process mining algorithms is increasing and more tools are entering the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Mapping the recorded events to activities of a given process model is essential for conformance checking, annotation and understanding of process discovery results. Current approaches try to abstract from events in an automated way that does not capture the required domain knowledge to fit business activities. Such techniques can be a good way to quickly reduce complexity in process discovery. Yet, they fail to enable techniques like conformance checking or model annotation, and potentially create misleading process discovery results by not using the known business terminology.In this thesis, we develop approaches that abstract an event log to the same level that is needed by the business. Typically, this abstraction level is defined by a given process model. Thus, the goal of this thesis is to match events from an event log to activities in a given process model. To accomplish this goal, behavioral and linguistic aspects of process models and event logs as well as domain knowledge captured in existing process documentation are taken into account to build semiautomatic matching approaches. The approaches establish a pre-processing for every available process mining technique that produces or annotates a process model, thereby reducing the manual effort for process analysts. While each of the presented approaches can be used in isolation, we also introduce a general framework for the integration of different matching approaches.The approaches have been evaluated in case studies with industry and using a large industry process model collection and simulated event logs. The evaluation demonstrates the effectiveness and efficiency of the approaches and their robustness towards nonconforming execution logs. • Thomas Baier, Andreas Rogge-Solti, Mathias Weske, and Jan Mendling. Matching of events and activities -an approach based on behavioral constraint satisfaction. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 1225-1230. ACM, 2015.• Thomas Baier, Claudio Di Ciccio, Jan Mendling, and Mathias Weske. Matching of events and activities -an approach using declarative modeling constraints. Other important roles i...

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a business process, activities are executed automatically and manually, where manual execution may be supported by information systems. In this environment, the different systems used to execute business processes generate a large amount of events (e.g., Global Positioning System (GPS) signals of driving trucks) that can be used to enable the monitoring of business processes across enterprise boundaries [6,11,15].…”

Section: Introductionmentioning

confidence: 99%

Model-Driven Event Query Generation for Business Process Monitoring

Backmann

Baumgrass

Herzberg

et al. 2014

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. While executing business processes, a variety of events is produced that is valuable for getting insights about the process execution. Specifically, these events can be processed by Complex Event Processing (CEP) engines to deliver a base for business process monitoring. Mobile, flexible, and distributed business processes challenge existing process monitoring techniques, especially if process execution is partially done manually. Thus, it is not trivial to decide where the required business process execution information can be found, how this information can be extracted, and to which point in the process it belongs to. Tackling these challenges, we present a model-driven approach to support the automated creation of CEP queries for process monitoring. For this purpose, we decompose a process model that includes monitoring information into its structural components. Those are transformed to CEP queries to monitor business process execution based on events. For illustration, we show an implementation for Business Process Model and Notation (BPMN) and describe possible applications.

show abstract

Developing a Platform for Supporting Clinical Pathways

Kirchner¹,

Herzberg

2019

Empirical Studies on the Development of Executable Business Processes

View full text Add to dashboard Cite

An Event Processing Platform for Business Process Management

Cited by 31 publications

References 8 publications

Matching of events and activities

Matching of events and activities

Model-Driven Event Query Generation for Business Process Monitoring

Developing a Platform for Supporting Clinical Pathways

Contact Info

Product

Resources

About