Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processes

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

doi:10.1007/978-3-642-38709-8_4

Cited by 32 publications

(34 citation statements)

References 27 publications

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“…These metadata can be used for imbuing the process data with additional semantics and will manifest new challenges for process analysis. In our previous work, we formalized set of metadata queries to discover evolution (how the business artifact evolved over time? ), derivation (what are the ancestors of the business artifact?…”

Section: Analyzing Process Datamentioning

confidence: 99%

“…For each of these queries, we provide an analysis service. Scalable Process Query Services: We provide efficient mapping and execution of process‐level queries into graph‐level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. To achieve this and to support a scalable and efficient analysis over process data, we extend our previous work (a graph query engine for organizing and querying process data()) to store and query large process graphs using Apache Hadoop solution . We implement scalable process query services to translate domain‐specific queries into large‐scale MapReduce‐based queries to speed up data processing and to scale up with data volume.…”

Section: Introductionmentioning

confidence: 99%

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ProcessAtlas: A scalable and extensible platform for business process analytics

2018

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In today's knowledge-, service-, and cloud-based economy, an overwhelming amount of business-related data are being generated at a fast rate daily from a wide range of sources. These data increasingly show all the typical properties of big data: wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. In this context, there is a need for new scalable and process-aware services for querying, exploration, and analysis of process data in the enterprise because (1) process data analysis services should be capable of processing and querying large amount of data effectively and efficiently and, therefore, have to be able to scale well with the infrastructure's scale and (2) the querying services need to enable users to express their data analysis and querying needs using process-aware abstractions rather than other lower-level abstractions. In this paper, we introduce Proces-sAtlas, ie, an extensible large-scale process data querying and analysis platform for analyzing process data in the enterprise. The ProcessAtlas platform offers an extensible architecture by adopting a service-based model so that new analytical services can be plugged into the platform. In ProcessAtlas, we present a domain-specific model for representing process knowledge, ie, process-level entities, abstractions, and the relationships among them modeled as graphs. We provide services for discovering, extracting, and analyzing process data. We provide efficient mapping and execution of process-level queries into graph-level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. We have implemented ProcessAtlas as a MapReduce-based prototype and report on experiments performed on both synthetic and real-world datasets. goal is to understand how a BP is performed and to identify opportunities for improvement. 2 However, the wide-scale automation in enterprises has led to having BPs implemented over many systems. Therefore, answering questions such as "What are the dependencies between 2 different cases? What is the typical path for a case and how much time and resources can be spent in processing it? What is the story behind the file number #756.1: where is the origin of this file? How it evolved over time? Who was involved in updating this file?" becomes difficult at best.The main barrier for answering questions like those aforementioned is that, in today's knowledge-, service-, and cloud-based economy, the information about process execution is scattered across several systems and data sources. Consequently, process logs increasingly come to show all typical properties of the big data 3 : wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. We use the term Process Data to refer to such large hybrid collections of heterogeneous and partially unstructured process-related execution data. Digitalization of business artifacts (eg, doc...

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Section: Analyzing Process Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ProcessAtlas: A scalable and extensible platform for business process analytics

2018

Self Cite

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“…In the data extraction step, the original query is transformed into three different queries, in order to query the three different viewpoints in an isolated way (see (1) in Figure 8). The result of these specific queries (step 2) is a set of JSON files that will be combined in step (3). The interaction with the specific technology that supports each viewpoint (i.e., Neo4J, Bonita and Oracle) is in charge of drivers.…”

Section: Data Extractionmentioning

confidence: 99%

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Gómez-López

Quintero

Parody

et al. 2018

Business Process Management Workshops

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Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three different technologies.

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“…This capability is required for instance to monitor segregation of duties. These three capabililty types capture information about the provenance of processes [3], i.e. what manipulation have been performed in the process, when and by whom.…”

Section: Conceptual Modelmentioning

confidence: 99%

Aligning Monitoring and Compliance Requirements in Evolving Business Networks

Comuzzi

2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

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This is the submitted version of the paper.This version of the publication may differ from the final published version. Abstract. Dynamic business networks (BNs) are intrinsically characterised by change. Compliance requirements management, in this context, may become particularly challenging. Partners in the network may join and leave the collaboration dynamically and tasks over which compliance requirements are specified may be consequently delegated to new partners or backsourced by network participants. This paper considers the issue of aligning the compliance requirements in a BN with the monitoring requirements they induce on the BN participants when change (or evolution) occurs. We first provide a conceptual model of BNs and their compliance requirements, introducing the concept of monitoring capabilities induced by compliance requirements. Then, we present a set of mechanisms to ensure consistency between the monitoring and compliance requirements when BNs evolve, e.g. tasks are delegated or backsourced in-house. Eventually, we discuss a prototype implementation of our framework, which also implements a set of metrics to check the status of a BN in respect of compliance monitorability. Permanent repository link

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Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processes

Cited by 32 publications

References 27 publications

ProcessAtlas: A scalable and extensible platform for business process analytics

ProcessAtlas: A scalable and extensible platform for business process analytics

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Aligning Monitoring and Compliance Requirements in Evolving Business Networks

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