Compliance Management in Business Processes

Saralaya, Sridevi; Saralaya, Vishwas; D’Souza, Rio

doi:10.1007/978-3-319-93940-7_3

Cited by 9 publications

(7 citation statements)

References 90 publications

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“…In many cases, it might be required to check deviations on a much finer granularity, e.g., on the level of activities, e.g., absence, existence, or pairs of activities, e.g., co-existence, mutual exclusion, and temporal and resource variants thereof. Such finer granularity checks are referred to as compliance checking, and compliance patterns are used for categorizing the type of compliance requirements [9,14,18]. Fig.…”

Section: Compliance Patternsmentioning

confidence: 99%

“…Within this field, a common operation consists in identifying process instances (cases) that violate one or more compliance rules, a.k.a. (business process) compliance checking [18]. 1 Previous studies have tackled the problem of compliance checking w.r.t.…”

Section: Introductionmentioning

confidence: 99%

“…1 Previous studies have tackled the problem of compliance checking w.r.t. various types of rules, categorized into compliance patterns in [9,18]. Within this framework, the goal of compliance checking is to detect and analyse deviations between the process executions recorded in an event log, and a set of compliance rules, each of which is an instance of a compliance pattern.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Checking of Temporal Compliance Rules Over Business Process Event Logs

Augusto¹,

Awad²,

Dumas³

2021

Preprint

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Verifying temporal compliance rules, such as a rule stating that an inquiry must be answered within a time limit, is a recurrent operation in the realm of business process compliance. In this setting, a typical use case is one where a manager seeks to retrieve all cases where a temporal rule is violated, given an event log recording the execution of a process over a time period. Existing approaches for checking temporal rules require a full scan of the log. Such approaches are unsuitable for interactive use when the log is large and the set of compliance rules is evolving. This paper proposes an approach to evaluate temporal compliance rules in sublinear time by pre-computing a data structure that summarizes the temporal relations between activities in a log. The approach caters for a wide range of temporal compliance patterns and supports incremental updates. Our evaluation on twenty real-life logs shows that our data structure allows for real-time checking of a large set of compliance rules.1 This operation is equivalent to that of detecting deviations between process executions and declarative process models, i.e. models that capture rule-sets diagrammatically [14,4].

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Section: Compliance Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Checking of Temporal Compliance Rules Over Business Process Event Logs

Augusto¹,

Awad²,

Dumas³

2021

Preprint

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“…Compliance checking [30] is a specialization of conformance checking in which event logs are checked against compliance rules that might restrict process behaviour w.r.t control flow, data, resources, and timing. Compliance rules represent interpretations of obligations either internal, e.g., policies, or external, e.g., legislation.…”

Section: Introductionmentioning

confidence: 99%

“…Compliance checking is an interactive and repetitive task by nature due to changes in the obligations. Compliance rules usually follow common patterns [15,30]. The objective of compliance checking is to identify process instances that violate the rules.…”

Section: Introductionmentioning

confidence: 99%

Efficient Checking of Timed Order Compliance Rules over Graph-encoded Event Logs

Zaki¹,

Helal²,

Awad³

et al. 2022

Preprint

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Validation of compliance rules against process data is a fundamental functionality for business process management. Over the years, the problem has been addressed for different types of process data, i.e., process models, process event data at runtime, and event logs representing historical execution. Several approaches have been proposed to tackle compliance checking over process logs. These approaches have been based on different data models and storage technologies including relational databases, graph databases, and proprietary formats. Graph-based encoding of event logs is a promising direction that turns several process analytics tasks into queries on the underlying graph. Compliance checking is one class of such analysis tasks. In this paper, we argue that encoding log data as graphs alone is not enough to guarantee efficient processing of queries on this data. Efficiency is important due to the interactive nature of compliance checking. Thus, compliance checking would benefit from sub-linear scanning of the data. Moreover, as more data are added, e.g., new batches of logs arrive, the data size should grow sub-linearly to optimize both the space of storage and time for querying. We propose two encoding methods using graph representation, realized in Neo4J, and show the benefits of these encoding on a special class of queries, namely timed order compliance rules. Compared to a baseline encoding, our experiments show up to 5x speed up in the querying time as well as a 3x reduction in the graph size.

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