A Query Language for Analyzing Business Processes Execution

Beheshti, Amin; Benatallah, Boualem; Motahari-Nezhad, Hamid Reza; Sakr, Sherif

doi:10.1007/978-3-642-23059-2_22

Cited by 76 publications

(73 citation statements)

References 29 publications

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“…BP-SPARQL [33,34], which also focuses on trace analysis, is a declarative query language extended from SPARQL [35], a query language for Resource Description Framework (RDF) graphs. BP-SPARQL takes the process execution log of a system (with multiple process instances) and uses a data model-a graph with typed nodes representing entities, data objects with attributes and edges representing transitive relationship between two entities.…”

Section: Related Workmentioning

confidence: 99%

Querying Workflow Logs

Tang¹,

Mackey²,

Su³

2018

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A business process or workflow is an assembly of tasks that accomplishes a business goal. Business process management is the study of the design, configuration/implementation, enactment and monitoring, analysis, and re-design of workflows. The traditional methodology for the re-design and improvement of workflows relies on the well-known sequence of extract, transform, and load (ETL), data/process warehousing, and online analytical processing (OLAP) tools. In this paper, we study the ad hoc queryiny of process enactments for (data-centric) business processes, bypassing the traditional methodology for more flexibility in querying. We develop an algebraic query language based on "incident patterns" with four operators inspired from Business Process Model and Notation (BPMN) representation, allowing the user to formulate ad hoc queries directly over workflow logs. A formal semantics of this query language, a preliminary query evaluation algorithm, and a group of elementary properties of the operators are provided.

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Section: Related Workmentioning

confidence: 99%

Querying Workflow Logs

Tang¹,

Mackey²,

Su³

2018

Information

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“…All found tuples for an event instanceê are added to the LTÊ relation (see line 9). For each of these tuples, the event instanceê is copied and inserted into the transformed trace t as new event instanceê (see line [12][13][14][15][16][17][18]. The event class ofê is al-Algorithmus 1 : Transform event log with LTÊ.…”

Section: Transformation Of the Event Logmentioning

confidence: 99%

“…The constraint introduced in formula (11) ensures that events for which an Init rule exists, are only mapped to activities for which an Init rule exists. Formula (12) and (13) work in the same manner for End and Participation rules.…”

Section: Approaches Based On Behavioral Relationsmentioning

confidence: 99%

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Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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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...

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“…Secondly, more features besides sequences are evaluated in this paper. Besides the work on querying business process models, there also exists work on querying executions of business processes [4,5]. The technique in this paper relates to the topic of business process similarity search.…”

Section: Related Workmentioning

confidence: 99%

FNet: An Index for Advanced Business Process Querying

Yan

Dijkman

Grefen

2012

Lecture Notes in Computer Science

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Abstract. Nowadays, more and more organizations describe their operations in terms of business processes. Consequently, it is common for organizations to have collections of hundreds or even thousands of business process models. This calls for techniques to quickly retrieve business process models that satisfy a given query. Some advanced techniques for querying a collection business process models exist. However, these techniques focus on the expressive power of the query language, rather than the efficiency of retrieval of models that satisfy the query. Consequently, querying a collection of models can take considerable time. To solve this problem, this paper proposes an efficient technique, using feature nets. Experiments show that on average the technique performs two orders of magnitude faster than existing techniques.

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A Query Language for Analyzing Business Processes Execution

Cited by 76 publications

References 29 publications

Querying Workflow Logs

Querying Workflow Logs

Matching of events and activities

FNet: An Index for Advanced Business Process Querying

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