Data Flow Perspective Research Articles

Multi-criteria decision analysis for non-conformance diagnosis: A priority-based strategy combining data and business rules

Business process analytics and verification have become a major challenge for companies, especially when process data is stored across different systems. It is important to ensure Business Process Compliance in both data-flow perspectives and business rules that govern the organisation. In the verification of data-flow accuracy, the conformance of data to business rules is a key element, since essential to fulfil policies and statements that govern corporate behaviour. The inclusion of business rules in an existing and already deployed process, which therefore already counts on stored data, requires the checking of business rules against data to guarantee compliance. If inconsistency is detected then the source of the problem should be determined, by discerning whether it is due to an erroneous rule or to erroneous data. To automate this, a diagnosis methodology following the incorporation of business rules is proposed, which simultaneously combines business rules and data produced during the execution of the company processes. Due to the high number of possible explanations of faults (data and/or business rules), the likelihood of faults has been included to propose an ordered list. In order to reduce these possibilities, we rely on the ranking calculated by means of an AHP (Analytic Hierarchy Process) and incorporate the experience described by users and/or experts. The methodology proposed is based on the Constraint Programming paradigm which is evaluated using a real example. .

OrgMiner: A Framework for Discovering User-Related Process Intelligence from Event Logs

Process Intelligence refers to the extraction and analysis of valuable knowledge nuggets embedded in business process instances/event logs or enterprise applications, for the purpose of supporting various decision-making processes. Researchers and practitioners mine such event logs using Process Mining and Analytics (PMA) techniques that help analyze business processes across three perspectives: control flow, organization, and data. While previous PMA studies have made advances toward the control flow and data flow perspectives, there is limited research toward the organizational perspective of process intelligence. In this study, we propose an organizational mining framework, OrgMiner, that supports constructing organizational models from event logs. The framework utilizes the notion of behavioral patterns, which rely on the weak order relations appearing in event logs. The various modules and knowledge elements in the framework are described in detail. The components of the framework support identifying, selecting, and applying behavioral patterns using different metrics for organizational mining purposes. The derived organizational models can be used to support decision making in scenarios such as task assignment, resource allocation, as well as role-based access control. Compared to extant studies, the proposed approach does not assume prior availability of explicit process models. Additionally, the process patterns presented in this study can be used as building blocks, so that researchers and practitioners can use them directly or extend them further to identify complex organizational processes. A case study is presented to evaluate the feasibility and effectiveness of the OrgMiner framework.

Transformation of the BPMN Design Model into a Colored Petri Net Using the Partitioning Approach

Formal verification is a process to ensure that the business process model and notation (BPMN) design model is free of deadlock, livelock, and other undesirable properties that can cause a system crash. Formal verification is a complicated procedure involving model abstraction and model checking tools used for property checking. There are several existing transformation techniques that yield an abstract model, but the data perspective is not considered. These techniques are also inappropriate for the large-scale BPMN design model. An automated transformation can reduce the flaws, time consumption, and complexity of the large-scale BPMN design model. This paper proposes a transformation technique and provides a framework for transforming the BPMN design models into colored Petri nets (CPNs). Our techniques cover both the control-flow and data-flow perspectives. The partitioning technique of the BPMN design model is applied to reduce the complexity and leads to a CPN model that can support the hierarchical and compositional verification techniques that are suitable for the large-scale BPMN design models. The proposed transformation has been implemented as a transformation framework and is able to consistently transform the BPMN design models into CPN models.

Multilevel Process Mining for Financial Audits

The relevance of business intelligence increases with the growing amount of recorded data. The research on business intelligence has led to a mature set of methods and tools that are used in many application areas, but they are almost absent in the auditing industry. Public accountants face the challenge to audit increasingly complex business processes that process huge amounts of transaction data. Process mining can be used as a business intelligence approach in the context of process audits to exploit this data. We introduce a process mining algorithm to improve such audits. Key requirements for this purpose are the reliability of the mining results, the integration of a data flow perspective and the ability to inspect data from the point of origin to the final output on the financial accounts. The presented algorithm integrates the control flow and data flow perspective. It operates on different abstraction levels to enable the auditor to follow the audit trail. The algorithm creates precise and fitting process models to prevent false negative and false positive audit results, accepts specific unlabeled event logs as input, and considers data relationships for inferring the control flow. It was evaluated by using extensive real world data.

A Dataflow Perspective for Business Process Integration

Business process integration has become prevalent as business increasingly crosses organizational boundaries. To address the issue of protecting organizations’ competitive knowledge and private information while also enabling business-to-business (B2B) collaboration, past research has focused mainly on customized public and private process design, as well as structural correctness of the integrated workflow. However, a dataflow perspective is important for business process integration. This article presents a data-flow perspective using workflow management and mathematical techniques to address data exchange problems in independent multistakeholder business process integration in dynamic circumstances. The research is conducted following a design science paradigm. We build artifacts that include interorganizational workflow concepts, a workflow model, and a public dataset calculation method. The use of the proposed artifacts is illustrated by applying them to a real-world case in the Shenzhen (Chaiwan) port. The utility of the artifacts is evaluated through interviews with practitioners in industry. We conclude that this research complements the control-flow perspective in the interorganizational workflow management area and also contributes to B2B information-sharing literature; further, the dataflow formalism can help practitioners to formally provide the right data at the right time in dynamic circumstances.

Interoperative end-user process modelling for process collaborative manufacturing

As business environments change rapidly, the ability to quickly set up a collaborative automated business processes is desirable. Collaborative business processes are increasingly driven by business agility, adaptability and flexibility, particularly in a modern manufacturing enterprise environment. Traditionally, collaborative business processes are used among big organisations. Many of collaborative business process systems are designed for a long-term use with central control. It is a great challenge for enterprises to adapt processes to the today's business pace. Today's enterprise users demand many situational collaborative business process applications which handle business needs within a short period and which normally do not need to support too many users. The traditional centrally controlled systems are not designed for such situational applications. In this paper, we explore how business process mashups can be used for manufacturing enterprise collaboration. We highlight the modelling of end-users process collaboration from both a control flow and a data flow perspective. Based on our analyses, an end-user process modelling approach is proposed for process enterprise mashup applications. Our approach, illustrated by reference to an example pharmaceutical collaborative manufacturing case, will support collaboration among users with different levels of modelling skills and expertise in a pharmaceutical manufacturing enterprise environment.

Diagnosing correctness of semantic workflow models

To model operational business processes in an accurate way, workflow models need to reference both the control flow and dataflow perspectives. Checking the correctness of such workflow models and giving precise feedback in case of errors is challenging due to the interplay between these different perspectives. In this paper, we propose a fully automated approach for diagnosing correctness of semantic workflow models in which the semantics of activities are specified with pre and postconditions. The control flow and dataflow perspectives of a semantic workflow are modeled in an integrated way using Artificial Intelligence techniques (Integer Programming and Constraint Programming). The approach has been implemented in the DiagFlow tool, which reads and diagnoses annotated XPDL models, using a state-of-the-art constraint solver as back end. Using this novel approach, complex semantic workflow models can be verified and diagnosed in an efficient way.

Testing criteria for object oriented visual dataflow languages

Commercial and research Visual Dataflow Languages (VDFLs) with object oriented features have had a substantial impact on end-user programming. Like any other object oriented programming languages, be they visual or textual, VDFLs often contain faults. In this article we investigate, from a data-flow testing perspective, the various side effects introduced by objects behavior in VDFLs. Our findings helped us establish the theoretical and practical foundations needed to adapt code-based data-flow test adequacy criteria to VDFLs, in the context of Prograph [7], and provide users of the latter with some of the error detection benefits of traditional object oriented testing.

Formulating the Data-Flow Perspective for Business Process Management

Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.

Integrating Process Mapping Into The AIS Students Toolset

Over the past decade, the impact of technology on accounting practice has been profound. The professional landscape is rapidly changing, and todays accounting students have more career opportunities than ever before. While many accounting majors are still choosing the traditional routes of public or corporate accounting, others are tailoring their education toward management or systems consulting. As AIS educators it is imperative that we maintain currency in our curriculum for both the traditional majors and those students seeking a consulting career path.Although the AIS curriculum is applicable to both types of accounting majors, certain content is especially pertinent to those students interested in consulting. One subject that consultants draw upon extensively is systems documentation. Current AIS curriculum focuses on analysis, design, and documentation of systems from an internal control and data flow perspective, and students learn to use traditional accounting documentation tools. These techniques include document and systems flowcharts and data flow diagrams. While these tools may be sufficient for their intended purposes, they are not the primary documentation techniques used in the consulting profession. The objective of this paper is to advocate the inclusion of a popular management consulting documentation technique, process mapping, into the accounting curriculum. Process maps are used to better understand a business entity's current business processes and communicate these in a non-technical way. Additionally, process mapping can provide an initial foundation to successful process management.

Database Generated Software Requirements Specification

AbstractThe goal of software requirements management at Grumman is to provide Systems Engineering's internal and external customers with the right information to produce and evaluate a computer program that meets the system's needs. A valuable approach to requirements management is to model one or more aspects of a system and communicate and control the system and software requirements via a database of the model elements.Engineering models can range from abstract models such as a series of partial differential equations for the solution of harmonic vibrations to isomorphic models such as a data flow diagrams or perspective drawings. The modeling method chosen uses a graphical notation and an Entity‐Relationship‐Attribute (ERA) database to create four isomorphic models to define a system and is currently implemented using RDD‐100® from Ascent Logic Corporation.The models used to specify the system are:1) Design Knowledge Capture (DKC) model‐an ERA model of the requirements flowdown from the perceived need to the requirements specification. 2) Behavior model – built using entities that represent functions and items, along with temporal notations of the functional flow. 3) Physical model –sometimes called the system architecture, includes the hierarchical structure of the components that build the system, the interfaces between the components, and the physical links that comprise the interfaces. 4) Resource model‐allows the engineer to evaluate the amount of resources needed when specified behavior is exhibited by a system architecture and to determine the behavior of the system when resources are depleted.The customization of the RDD language and the augmentation of RDD with other tools to add the required capability is described. The elements of the model are mapped from the RDD database to the an expansive interpretation of the Data Item Description for a Software Requirements Specification (DI‐MCCR‐80025A). The interpretation is based on interchanges between the requirements provider and the requirements implementor.The paper describes the aspects of systems specified using the database of models method. The method is heavily driven by the need to meet the MIL‐STD 2167A requirements for a Software Requirements Specification. However; the main value of this paper may be in the identification of the information produced while engineering the system that is not in the Systems Requirements Specification (SRS).