A Lightweight RDF Data Model for Business Process Analysis

Leida, Marcello; Majeed, Basim; Colombo, Maurizio; Chu, Andrej

doi:10.1007/978-3-642-40919-6_1

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…A general limitation of flat log formats is their redundancy, as entities (e.g., involved resources) recurring in multiple events are recorded in all corresponding event tuples. Relational [19] or graph-based [20] databases for storing event log data have been proposed as a solution to address this limitation. Besides handling process data, using a database supports computing views on them.…”

Section: Event-logs Standardsmentioning

confidence: 99%

“…In [20], an RDF serialization of graphs is adopted to model and analyze process logs. In this paper, we rely on a model we already used for semantic lifting [35], model reconciliation [29] and knowledge discovery [25].…”

Section: Semantics-aware Process Log Representationsmentioning

confidence: 99%

“…Today's PMon tools have to be flexible enough to handle process information that was unknown at the time of process model definition [20]. Our approach relies on two standards: the Resource Description Framework (RDF) [10] and RDF Schema (RDFS) [36].…”

Section: The Ebtic-bpm Process Vocabularymentioning

confidence: 99%

“…Three more relations apply to the ebtic-bpm:Task concept: ebtic-bpm: followedBy, ebticbpm:precededBy and ebticbpm:hasSubTask, which respectively indicate which tasks precede and follow a given one and which tasks are subtasks of a given one. These relations allow layering our model with specific ontologies describing the business activities addressed by an organization or additional domain knowledge originated from normative or contractual regulations [20].…”

Section: The Ebtic-bpm Process Vocabularymentioning

confidence: 99%

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Dynamic Access Control to Semantics-Aware Streamed Process Logs

et al. 2019

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Business process logs are composed of event records generated, collected and analyzed at different locations, asynchronously and under the responsibility of different authorities. Their analysis is often delegated to auditors who have a mandate for monitoring processes and computing metrics but do not always have the rights to access the individual events used to compute them. A major challenge of this scenario is reconciling the requirements of privacy and access control with the need to continuously monitor and assess the business process. In this paper, we present a model, a language and a software toolkit for controlling access to process data where logs are made available as streams of RDF triples referring to some company-specific business ontology. Our approach is based on the novel idea of dynamic enforcement: we incrementally build dynamic filters for each process instance, based on the applicable access control policy and on the current prefix of the event stream. The implementation and performance validation of our solution is also presented.

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Section: Event-logs Standardsmentioning

confidence: 99%

Section: Semantics-aware Process Log Representationsmentioning

confidence: 99%

Section: The Ebtic-bpm Process Vocabularymentioning

confidence: 99%

Section: The Ebtic-bpm Process Vocabularymentioning

confidence: 99%

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Dynamic Access Control to Semantics-Aware Streamed Process Logs

et al. 2019

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“…Moreover, as reported in [13], RDF tripe stores are more and more faced with very large graphs that contain hundreds of millions of RDF triples. The ZEUS framework represents one of the most advanced solutions specifically designed for integrating multiple source and supporting fast and continuous execution of SPARQL queries [14].…”

Section: Rdf Graphs In the Big Data Contextmentioning

confidence: 99%

Consistent Process Mining over Big Data Triple Stores

Azzini

Ceravolo

2013

2013 IEEE International Congress on Big Data

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Big Data' techniques are often adopted in crossorganization scenarios for integrating multiple data sources to extract statistics or other latent information. Even if these techniques do not require the support of a schema for processing data, a common conceptual model is typically defined to address name resolution. This implies that each local source is tasked of applying a semantic lifting procedure for expressing the local data in term of the common model. Semantic heterogeneity is then potentially introduced in data. In this paper we illustrate a methodology designed to the implementation of consistent process mining algorithms in a 'Big Data' context. In particular, we exploit two different procedures. The first one is aimed at computing the mismatch among the data sources to be integrated. The second uses mismatch values to extend data to be processed with a traditional map reduce algorithm.

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Finding Non-compliances with Declarative Process Constraints Through Semantic Technologies

Ciccio

Ekaputra

Cecconi

et al. 2019

Lecture Notes in Business Information Processing

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Business process compliance checking enables organisations to assess whether their processes fulfil a given set of constraints, such as regulations, laws, or guidelines. Whilst many process analysts still rely on ad-hoc, often handcrafted percase checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is DE-CLARE, a well-established declarative process specification language based on temporal logics. DECLARE specifies the behaviour of processes through temporal rules that constrain the execution of tasks. So far, however, automated compliance checking approaches typically report compliance only at the aggregate level, using binary evaluations of constraints on execution traces. Consequently, their results lack granular information on violations and their context, which hampers auditability of process data for analytic and forensic purposes. To address this challenge, we propose a novel approach that leverages semantic technologies for compliance checking. Our approach proceeds in two stages. First, we translate DECLARE templates into statements in SHACL, a graph-based constraint language. Then, we evaluate the resulting constraints on the graph-based, semantic representation of process execution logs. We demonstrate the feasibility of our approach by testing its implementation on realworld event logs. Finally, we discuss its implications and future research directions.

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A Lightweight RDF Data Model for Business Process Analysis

Cited by 7 publications

References 11 publications

Dynamic Access Control to Semantics-Aware Streamed Process Logs

Dynamic Access Control to Semantics-Aware Streamed Process Logs

Consistent Process Mining over Big Data Triple Stores

Finding Non-compliances with Declarative Process Constraints Through Semantic Technologies

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