Indexing and Efficient Instance-Based Retrieval of Process Models Using Untanglings

Polyvyanyy, Artem; Rosa, Marcello La; Hofstede, Arthur H. M. ter

doi:10.1007/978-3-319-07881-6_30

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…As a result, ten 4C predicates were selected: one conflict, one co-occurrence, four causality, and four concurrency predicates. In addition, in line with our previous work, we included two unary predicates that can be used to check if a given task can occur or always occurs in the executions of a process model [58,71]. This led to the total of twelve predicates, which are reported with their names and text definitions in Table 2.…”

Section: Behavioral Predicate Definitionmentioning

confidence: 99%

Process Query Language: Design, Implementation, and Evaluation

Polyvyanyy¹,

Hofstede²,

Rosa³

et al. 2019

Preprint

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SELECT "ID","Title" FROM "⇑SAP-R3-EPC-Repo" WHERE AlwaysOccurs({"Continuous inventory WM","Annual inventory WM", "Print inventory list"},ANY) OR Cooccur("Storage type is blocked","Annual inventory WM to be performed");Note that this query utilizes a well-known mechanism of macros for combining results of two or more predicate checks into a result of a single statement. Concretely, in query Q2, three behavioral predicates connected via the logic OR operator: AlwaysOccurs(f 2 ) OR AlwaysOccurs(f 3 ) OR AlwaysOccurs(f 5 ), are combined into one marco AlwaysOccurs({f 2 ,f 3 ,f 5 },ANY).Example 3. The user's intent is to retrieve the process models, with all their attributes information (e.g., ID, title, version, author, etc.), which satisfy all the four conditions listed below: (C1) Function "Start inventory recount" (f 7 ), event "No variance is determined" (e 13 ), function or event having a label similar to "Clear differences" (f 8 or f 9 ), and function or event having a label similar to "Difference is posted" (e 16 or e 18 ), occur in at least one process instance; (C2) Inventory recount is optional, i.e., f 7 does not occur in every process instance; (C3) None of the tasks with a label similar to "Clear differences" (f 8 and f 9 ) occurs if no variance is determined (i.e., when e 13 takes place); and

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Section: Behavioral Predicate Definitionmentioning

confidence: 99%

Process Query Language: Design, Implementation, and Evaluation

Polyvyanyy¹,

Hofstede²,

Rosa³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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“…While scanning is efficient through in-memory database technology, the explosive growth of "big data" through event streaming applications, e.g., sensor data streams in Internet of Things applications, nonetheless warrants the use of indexing, e.g., B-Tree or R-Tree, to be created for process data, as alternative access paths to sequential process repository scans. Proposals for dedicated indexing strategies for processes involve indexing activity traces and subgraph isomorphism algorithms [24,25]. We include under the banner of "indexing" pragmatic considerations of pre-processing data, e.g., prejoining, to reduce computational latency and support rapid retrieval.…”

Section: Non-functional Requirements Of Process Queryingmentioning

confidence: 99%

“…○ Behavioral querying focuses on behaviors induced by activity models. Typical examples are querying techniques based on Petri nets [25,63], YAWL nets [64], temporal logics such as LTL [65], and a process query language [66,67] specifically designed to support behavioral querying. Another research stream studies behavioral similarity search [68][69][70][71] grounded in behavioral relations over activities and originates from the existing research on behavioral profile [72].…”

Section: Literature Analysis and Evaluationmentioning

confidence: 99%

Process querying: Enabling business intelligence through query-based process analytics

Polyvyanyy

Ouyang

Barros

et al. 2017

Decision Support Systems

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The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms.

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“…In [PLtH14], we introduced the total executability problem. Given a net system S and a subset of its transitions U , the total executability problem consists of deciding whether there exists an occurrence sequence in S of which every transition in U is an element.…”

Section: Proposition 73 (Executability)mentioning

confidence: 99%

Untanglings: a novel approach to analyzing concurrent systems

et al. 2015

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Abstract. Substantial research efforts have been expended to deal with the complexity of concurrent systems that is inherent to their analysis, e.g., works that tackle the well-known state space explosion problem. Approaches differ in the classes of properties that they are able to suitably check and this is largely a result of the way they balance the trade-off between analysis time and space employed to describe a concurrent system. One interesting class of properties is concerned with behavioral characteristics. These properties are conveniently expressed in terms of computations, or runs, in concurrent systems. This article introduces the theory of untanglings that exploits a particular representation of a collection of runs in a concurrent system. It is shown that a representative untangling of a bounded concurrent system can be constructed that captures all and only the behavior of the system. Representative untanglings strike a unique balance between time and space, yet provide a single model for the convenient extraction of various behavioral properties. Performance measurements in terms of construction time and size of representative untanglings with respect to the original specifications of concurrent systems, conducted on a collection of models from practice, confirm the scalability of the approach. Finally, this article demonstrates practical benefits of using representative untanglings when checking various behavioral properties of concurrent systems.

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Indexing and Efficient Instance-Based Retrieval of Process Models Using Untanglings

Cited by 11 publications

References 23 publications

Process Query Language: Design, Implementation, and Evaluation

Process Query Language: Design, Implementation, and Evaluation

Process querying: Enabling business intelligence through query-based process analytics

Untanglings: a novel approach to analyzing concurrent systems

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