Business process modeling extension and scheduling strategy compatible with big data business

Cloud computing and big data: Technologies and applications

Information System is one of the key domain in which lot of research has been done in the past few years. Applying Information systems to heterogeneous and distributed environments is one of the current research areas. Business Process Management Systems is the information systems which deal with the administration of tasks in the business processes, organizational structures, or in the related context. The Workflow Management system, the early idea of BPM, controls the workflow in an organization, data transfer, and integration of legacy information systems with existing programs and program modules, delegation of business tasks. Offering task management services especially modeling of business processes and underlying organizations, BPM serves it meaning and incorporates the WFM system. BPM supporting the use of knowledge concerning the awareness/unawareness of integrated software, and analysis of processes and organizational structure in terms of verification, modification, evaluation are key management in BPM system I.INTRODUCTION TO BPM: Combination of corporate restructuring and mergers urged the department and processes to be automated and linked together. Unfortunately it created largely disconnected spaghetti of systems and applications. Business Process Management (BPM) was born from a strong need to streamline internal processes and connections between both internal and external functions. It provides ability to model, monitor, manage and manipulate processes quickly in response to changes in strategy and market forces. BPM is an extension of classical Work Flow Management (WFM) systems and approaches. BPM delivers the maximum utility of legacy system. It is a method for revolutionizing the way the work moves throughout an organization. II.COMPONENTS OF BPM: The four key elements in Business Process Management are: Process modeling, Process rule, Process management, and Process performance analytics. Process modeling: Process modeling describes the flow of information (business process).It helps in building a model of process and mapping into an implementation frame work. Modeling requires a consistent analysis and representation of the processes. It forms the basis for the userspecific process environment. Process Rule: Process Rule is a heart of the BPM. It involves execution of discrete steps & integration of information. A process rules engine is used for this purpose. The engine is to monitor and manage the given steps by providing a flow control over the business process. If a process fails or delayed, a compensated action can be done to rectify. Process Management: Process Management is to correlate business/application events to fundamental business metrics .It enables the organization to monitor and optimize the business process. Once the process has been analyzed, modeled and the associated rules are formed. The next stage involves the visualization and management. Visualization means transaction data ranging from holistic to process-state or client state specific rules. Process Performance Analytics: Process Performance Analytics is a transition data source available for sophisticated management analytics .This data should be available on a retrospective or real-time basis. It should answer to one of the most difficult question like “how much does it cost to process a specific transaction?” By this way BPM mainly addresses four components. They are Work Flow Management, Content and records management Enterprise Application Integration (EAI) and Business Activity monitoring (BAM). III.RELEVENCE TO SYSTEM INTEGRATION: BPM can be fitted easily on the top of existing IT system. It is the Process rules that governs all the happenings and optimizes the process. BPM will not be able to work without the connectivity provided by the EAI layer. EAI layer provides BPM to gain and measure the value from the investment. On the other hand, BPM provides value to the EAI technology layer of Information technology. BPM layer offers business management the view of the message processing layer. It is used to unlock the value of the information held in the integration layer. It is also used to gather data of every transaction in the lifecycle. IV.APPLICATION OF BPM: BPM can be applied to any application which needs these advantages. BPM reduces unnecessary high losses in time critical transactions, helps to provide any critical information, practical application yield fast ROI ,better control over the information, identifying and maximizing the benefits faster, better analysis and tracking, provides enterprise-wide visibility of business processes, improve the effectiveness of the core operations, provides ability to coordinate interactions between information systems, business process and the people, flexible and easily changed by non-technical business people.

Purpose: This study aims to investigate the impact of Big Data Analytics Capabilities (BDAC) on Indonesian Firm Performance (FPER), addressing the lack of empirical research in this area despite Indonesia's significant Big Data expenditure growth. The study also proposes a conceptual model as a framework for developing BDAC. Method: This is a quantitative study conducted in Indonesia using purposive and snowball sampling techniques to collect data from 51 Indonesian companies. The research model is based on the IT Capability framework, Sociomaterialism Theory, and incorporates Business Process Agility (BPA) as a mediating variable, as suggested by previous research. Findings: The results demonstrate a significant positive impact of BDAC on FPER. Furthermore, the study confirms that BPA significantly mediates the relationship between BDAC and FPER, highlighting the crucial role of agile business processes in leveraging the benefits of Big Data Analytics for improved firm performance. Practical Implications: This research provides valuable insights for Indonesian companies by: 1) demonstrating the significant positive impact of investing in BDAC on firm performance; 2) highlighting the importance of developing agile business processes to maximize the return on Big Data Analytics investments; and 3) offering a conceptual framework for developing and enhancing BDAC within Indonesian organizations. Originality: This study contributes to the existing literature by: 1) providing empirical evidence on the impact of BDAC on FPER specifically within the Indonesian context; 2) proposing a novel conceptual model for developing BDAC based on a combination of IT Capability framework, Sociomaterialism Theory, and the mediating role of BPA; and 3) addressing the gap in research on the relationship between BDAC and FPER in the Indonesian market. Paper Type: This research can be classified as an empirical study within the field of Information Systems, specifically focusing on Big Data Analytics, firm performance, and business process management. Keywords: Big Data, Firm Performance, Business Process Agility

As the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency.

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.

Business processes are essential components of all enterprises and the use of models, languages and execution engines as components of a Business Process Management (BPM) deployment are now commonplace. By definition, these processes describe an enterprise in terms of its organizational knowledge, structure and activities, and are often essential to realizing an organization's competitive advantage. It thus follows that the design, execution and, critically, responsiveness to change in the system or environment they are affecting, is of prime significance toward establishing and maintaining efficient business operation. Deploying a high quality and effective Business Process Management System (BPMS) is thus utterly essential to many modern enterprises. Yet current trends toward flexible methods of working, just-in-time organizational reaction times, distributed intra-organization and interorganization collaboration and constantly changing markets are creating new and complex business landscapes. This brings about increased complexity, further motivating the need for real-time dynamic change throughout an enterprise's business processes; ever more dynamic environments require key business processes to be more flexible and automated in both their design and behaviour. Yet many companies are now discovering that investments in conventional BPMS often suffers from poor return on investment due to a common inability to create business process models that are both meaningful to business people and capable of offering the real-time process flexibility and rapid process adaptation required to cope effectively with the fluid business conditions typifying many modern enterprises. As evidenced by our work with customers in the manufacturing domain, there is very often a need to alter executing process structures, sometimes in real-time, without perturbing the integrity of running process instances. If a BPMS is not built to innately support change in this manner the result can be reductions in both dependability and visibility, especially from a management perspective. Our observation is that many of the current procedural approaches to BPM are too inflexible and unresponsive to change, especially in any automated fashion. In fact many BPMS solutions provide only design-time modelling, with neither support for run-time determination of process structure, nor direct execution of industry standard Business Process Modeling Notation (BPMN) process models without the need for first translating BPMN into intermediary formats, such as the Business Process Execution Language (BPEL), in preparation for execution. In practice, these issues imply that process models can tend to become overly complex and brittle through the necessity of coding-in all O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Big DataVol. 8, No. 1 Call for PapersFree AccessCall for Special Issue Papers: Big Data in BusinessDeadline for Manuscript Submission: March 20, 2020Guest Editor: Haitham NobaneeGuest Editor: Haitham NobaneeThe University of Oxford, The University of Liverpool, Abu Dhabi UniversitySearch for more papers by this authorPublished Online:14 Feb 2020https://doi.org/10.1089/big.2019.29032.cfp2AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Big data is a fast-growing field that describes and analyzes huge amounts of structured, semi-structured, or unstructured data that comes from various sources that are vast and complex. It requires advanced analytics applications and sophisticated data-processing software. Big data has revolutionized the way businesses and institutions operate. Business entities from several sizes and sectors are benefiting from big data applications. Business entities revealed such benefits could improve efficiency and increase profitability and growth. Many business entities have several plans for using and implementing big data tools to improve operations, customer satisfaction and loyalty, enhancing corporate governance practices, fighting against fraud, cyber-attacks, money laundry, and other financial crimes. Big data has many other applications in business. This special issue is devoted to publishing high-quality papers that involve theoretical and practical aspects related to big data in business. Reviews on this topic are also welcome.Topics of interest for this special issue include, but are not limited to:The use of big data in business, marketing, finance, and accountingBig data and machine learning in business decision-making and performance evaluationBig data and enterprise risk managementBig data and corporate governanceBig data in marketing, promotions, and social networkingBig data in supply chain managementBig data and predictive algorithms in business, marketing, finance, and accountingBig data and business ethicsBig data and investment decisionsThe use of big data in financial institutions and marketsThe uses of big data in anti-financial crimes such as fraud and money laundry detection.Big data and online shopping.Monetizing big data for dynamic advertisementBig data and customer engagementBig data, machine-to-machine (M2M) analytics to improve product life-cycle managementBig data, automation, and internal efficiencyThe use of big data to compete, capture, innovate, and improve competitive advantageThe use of big data in creating new revenue streamsBig data and credit analysisBig data and insuranceBig data, business lending, and FinTechAll manuscripts should be submitted via the online submission system of Big Data. The Instructions for Authors can be found at www.liebertpub.com/big. The reviewing and selection of papers will be carried out in accordance with the standards of the journal.Submit your paper for peer review online: https://mc.manuscriptcentral.com/bigFiguresReferencesRelatedDetails Volume 8Issue 1Feb 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editor: Haitham Nobanee.Call for Special Issue Papers: Big Data in Business.Big Data.Feb 2020.1-1.http://doi.org/10.1089/big.2019.29032.cfp2Published in Volume: 8 Issue 1: February 14, 2020PDF download

Business processes are designed to execute strategies that aim at achieving organisational goals. During the last decade, several methods have been proposed that prescribe the use of goal-oriented requirements engineering techniques for supporting different business process management activities, in particular business process modelling. The integration of goal modelling and business process modelling aims at increasing the alignment between business strategies and the processes with their supporting IT systems. This new research area, which the authors call Goal-Oriented Requirements Engineering for Business Processes (GORE-for-BP), is developing rapidly, but without a clear conceptualization of the focus and scope of the proposed GORE-for-BP methods. Furthermore, an overview is lacking of which methods exist and what their level of maturity is. This paper therefore presents a research review of the GORE-for-BP area, with the aim of identifying relevant methods and assessing their focus, scope, and maturity. This study used Systematic Literature Review and Method Meta-Modelling as research methods to identify and evaluate the state of the GORE-for-BP research area and to propose a research agenda for directing future research in the area. Nineteen methods were identified, which is an indication of an active research area. Although some similarities were found with respect to how goal models are transformed into business process models (or vice-versa), there is also considerable divergence in modelling languages used and the extent of coverage of typical requirements engineering and business process management lifecycle phases. Furthermore, the exploitation of requirements engineering techniques in the full business process management lifecycle is currently under researched. Also, the maturity of the methods found in terms of the formalisation of the transformation activity, the elaboration of method guidelines, and the extent to which methods are validated, can be further improved.

The constant change and rising complexity of organizations, mainly due to the transforming nature of their business processes, has driven the increase of interest in business process management by organizations. It is recognized that knowing business processes can help to ensure that the software under development will meet the business needs. Some of software development processes (like unified process) already refer to business process modeling as a first effort in the software development process. A business process model usually is created under the supervision, clarification, approval, and validation of the business stakeholders. Thus, a business process model is a proper representation of the reality (as is or to be), having lots of useful information that can be used in the development of the software system that will support the business. The chapter uses the information existing in business process models to derive software models specially focused in generating a data model.

Business processes (BPs) are nowadays essential elements and key assets in any commercial organization. Additionally, BP matters are important issues in the context of enterprise computing. Business process management (BPM) is the main research area for process-aware systems involving methodologies, models, and supporting tools for process design, execution, and monitoring. BPM offers many challenges for software developers, including process specification and documentation. This paper is about (operational) BPs (e.g., procurement, hiring a new employee, supply chain management, request for leave), with a focus on modeling BPs in which all sub-processes, activities, data flows, inputs, and outputs, together with their relationships with each other, are identified and described. Typically, the focus of traditional process modeling is persistently on diagrammatic tools to design process notations (e.g., UML and BPMN). In this paper, we adopt a diagrammatic modeling methodology called the Thinging Machine (TM) and use it as a base to promote understanding of notions of BPs by offering a new perspective that captures a system’s dynamic behavior based on its events. The TM emphasizes a single unifying ontological element: the thing/machine concept (thimac), in contrast to object- or process-oriented methodologies. TM-based modeling can be a valuable tool in the general area of BPM. We partially demonstrate that by applying it to document two actual administrative systems. The resultant conceptual descriptions reflect a well-defined approach to the notions of processes and events.

In this study, we argue that important of strategy, business, design, and technology alignment affecting an organization’s ICT enterprise architecture is also reflected by business management perspective and business process model. Enterprise Architecture (EA) is an effective way to develop current and future views of the entire enterprise. EA does this primarily by integrating the processes for strategic, business and technology planning in a way that also integrates with other business and technology process. The seamless collaborate and synergize working between technology and non-technology executives and professionals are needed in the designing and formulating enterprise architecture. Therefore, in order to facilitate this analysis, we propose an approach to relate EA specified business architecture to business process innovation, modeled using Service blueprint and Business Process Model and Notation (BPMN). Our approach is accompanied by a method that supports the process automation of business process model and is illustrated by a Healthcare service practice as a case study.

PurposeThe purpose of this paper is to address a theoretical gap in the business process management (BPM) literature on factors that influence the acceptance and use of business process modelling (PM) in organisations. The paper seeks to contribute to theory building and practice in BPM through better understanding of important determinants of PM adoption and use.Design/methodology/approachA combination of literature review and 34 interviews in context was used to develop a PM acceptance model that was subsequently empirically tested using survey data from 74 companies.FindingsThe paper provides empirical insights about how business PM can be influenced by many factors in the organisational context. It suggests that PM is a complex activity mandated by management, but influenced by individual and socio‐political factors.Research limitations/implicationsThere is a need for future research to focus on the many opposing forces that influence business PM in organisations. Future studies should analyse influence from different stakeholder groups separately to reveal their relative influence on PM activity and its outcomes.Practical implicationsThe paper identifies important forces in the organisational context that managers should focus on in their efforts to successfully implement business PM in their organisations.Originality/valueThis paper uses a triangulation of sources of information to better understand the less‐studied process of PM adoption and use in an organisational context. It contributes to theory building within BPM and to more successful BPM in organisations.

Purpose. The aim of the article is to identify the key features of the implementation of digital solutions in the business process management system of modern enterprises. Methodology of research. The following research methods were used to achieve the set goal: methods of system analysis (to analyse problems during the planning of the implementation of digital solutions in the business process management system of modern enterprises); methods of scientific abstraction, deduction and induction (for formulating a scientific hypothesis of work, laws and principles), analysis and synthesis – to analyse the state of development of the business process management system of modern enterprises; abstract and logical (for making theoretical generalizations, substantiating research methodology, formulating conclusions). The tabular method clearly reflects the current state of the investigated problem. Findings. It has been established that the transition to a digital economy is based on technologies that define the Fourth Industrial Revolution, which consists of digitization and includes the saturation of the physical world with electronic and digital devices. The study also found that the process of digital transformation of business processes consists of elements such as customer interaction, operational business processes and enterprise models. It is determined that the implementation of digital solutions in each of these parts of the model opens up new opportunities for the development of enterprises. It is substantiated that the introduction of digital solutions in the management of business processes of enterprises has a positive impact on efficiency, flexibility and adaptability to changes in the external environment. It is noted that in order to maintain competitiveness, it is important not only to implement technology, but also to change the corporate culture and adapt employees to new tools. The study also highlights the most common digital solutions that are often implemented by modern enterprises: artificial intelligence, big data, blockchain, Internet of Things and cloud computing, which provide transparency, security and integration in business process management. Originality. A modern systematic overview of the most common digital solutions used to manage business processes at the enterprise has been further developed. Practical value. The results of the study can be useful for business managers and business process management specialists, as they provide a clear idea of the advantages and peculiarities of using digital solutions in the practice of enterprises. Key words: artificial intelligence, blockchain, big data, cloud technologies, fourth industrial revolution, digitalization.

The application of Big Data and time series models is currently at an early stage. This paper examines the relevance and use of time series analyses for Big Data and business analytics by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how time series models can be efficiently and effectively applied in accounting and auditing using Big Data. Using sophisticated Big Data and time series models, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The time series model and Big Data analysis presented in this paper provide policy, practical, educational, and research implications. Businesses and management can use our suggested time series model and Big Data analysis in their predictive models of managerial strategies, decisions, and actions. Business schools and accounting programs can integrate the time series model, Big Data, and data analytics into business and accounting education.

Nowadays, the problem in business process management (BPM) is that BPM systems should both be easy to use for business process developers and be based on a sound formal method. Business process management systems are often based on semiformal modeling techniques such as event-driven process chains (EPC) or UML. Unlike semiformal modeling techniques, the process calculus CSP comes with mature verification sup- port. Surprisingly little work has been done on using CSP for business process modeling and management. In this paper, we present our approach to business process management, which is based on the observation that CSP is well suited not only for specifying business processes and verifying work???ows but also for executing work???ows using our CSP4J framework. We report on a work???ow server, which is specified in CSP and implemented using our CSP4J framework. The server accepts work???ows that are modeled in CSP and also implemented using CSP4J. This allows us to integrate the mature verification support of CSP into both the management system itself and the development process of the end users’ business process definitions.