Real-world Business Processes Research Articles

A deep reinforcement learning-based optimization approach for containerized microservice scheduling in Hybrid Fog/Cloud environments

The deployment of microservices in Hybrid Fog/Cloud (HFC) environments for Internet of Things (IoT) applications presents a significant challenge in efficiently scheduling containerized services across distributed resources. While existing studies have explored microservice scheduling, a comprehensive approach that considers resource constraints, workflow dependencies, and dynamic hybrid environments remains elusive. This paper introduces a novel Deep Reinforcement Learning-based Algorithm (DRLA) for containerized microservice scheduling in HFC environments. DRLA utilizes a multi-constrained Binary Quadratic Program (BQP) model to optimize execution time, resource consumption, and occupancy rates while considering microservice dependencies and resource capabilities. The algorithm leverages two Deep Reinforcement Learning (DRL) agents, DQN and REINFORCE, to learn and adapt to the dynamic nature of the HFC federation. Experimental evaluations using five real-world Business Process (BP) use cases demonstrate that DRLA outperforms existing scheduling approaches such as default Kubernetes, Reward Sharing Deep Q-Learning (RSDQL), and Deep Reinforcement Learning (DRL) schedulers. Compared to existing schedulers, ours delivers optimal or near-optimal solutions, demonstrating significant improvements in key performance values. Indeed, DRLA achieves average optimality gaps of just 0.16% and 0.21%, significantly outperforming the Kubernetes scheduler, which exhibits a gap of 2.88%. It is worth noting that other similar algorithms see a far higher increase in optimality gaps. This highlights DRLA’s excellent performance and ability to schedule containerized microservices in hybrid fog/cloud environments, resulting in near-optimal solutions for a variety of use cases.

Process mining embeddings: Learning vector representations for Petri nets

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

BPEL process defects prediction using multi-objective evolutionary search

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using Multi-Objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.

M-PoP: leveraging the systematic modeling of processes-of-business processes

PurposeThis paper presents M-PoP, a method to model large, complex, and dynamic business processes. These processes have sometimes resulted from alliances of organizations (i.e. joint ventures and mergers and acquisitions) and are referred to as Processes-of-Business Processes (PoP). Due to the difficulty of modeling these dynamic processes, alliances of organizations have often lost opportunities, competitiveness, and profitability, so requiring suitable modeling methods.Design/methodology/approachThe authors proposed M-PoP that can model PoP through three views in different abstraction levels and using well-known techniques in industry and academia, mainly those from Business Process Model and Notation (BPMN). For this, M-PoP presents three main steps: identification of PoP elements, modeling of PoP, and verification of PoP models. To evaluate M-PoP, we applied it in a real-world business process in the health domain.FindingsThe evaluation results point out the capability and viability of M-PoP to deal with dynamic business processes.Research limitations/implicationsM-PoP still needs to be applied in various real-world scenarios to gather evidence of its productivity, efficiency, and scalability.Practical implicationsThis novel method could change the way organizations model their business processes and, as a consequence, it could leverage strategic business opportunities.Originality/valueM-PoP is the first method that makes it possible to model large and complex business processes and, most importantly, dynamic processes.

Visibility Matrix: Efficient User Interface Modelling for Low-Code Development Platforms

In this paper, we introduce the idea of the ‘visibility matrix’ for automated data entry form generation in low-code development platforms. We then focus on the problem of software development productivity in the area of automated software generation as the main factor of the Industry 4.0 concept in the area of business information. In our study, two different approaches to user interface development in a business process management low-code platform were evaluated. The first, the multi-form model, assumes that input forms are prepared separately for each user task in the business process being automated. The second approach, the single-form model, assumes that there is one global input form for every task in the business process. Since users have access to different data in different process tasks, it is necessary to prepare the visibility matrix to define which data are relevant to which tasks. The experiments presented in this paper help to answer the following question: which approach yields better results in terms of productivity, which is measured as costs and time required to prepare the application? Several dozen real business processes were analysed to examine the properties of their visibility matrix. Additionally, the real project team members were evaluated to determine their productivity. Then, the productivity parameters were calculated for real business processes and real project teams. The results show which approach is better suited for real-world business process development.

Transformation of Health and Social Care Systems-An Interdisciplinary Approach Toward a Foundational Architecture.

ObjectiveFor realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome.MethodsUsing the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used.ResultsThe system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.

BPFlexTemplate: A Business Process template generation tool based on similarity and flexibility

In large organizations with multiple organizational units, process variants emerge due to many aspects, including local management policies, resources or socio-technical limitations. Organizations then struggle to improve a business process which has no longer a single process model to redesign, implement and adjust. In this paper, we propose an approach to tackle these two challenges: decrease the proliferation of process variants in these organizations, and foresee, at the same time, the need of having flexible business processes that allow for a certain degree of adjustment. To validate our approach, we first conducted case studies where we collected six real-world business process variants from two organizational units of the same healthcare organization. We then proposed an algorithm to derive a template process model from all the variants, which includes common and flexible process elements. We implemented our approach in a software tool called BPFlexTemplate, and tested it with the elicited variants.

Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

Synthesis of Mathematical Programming models with one-class evolutionary strategies

Abstract We propose an Evolutionary Strategy-based One Class Constraint Synthesis ( ESOCCS ), a novel method for computer-assisted synthesis of constraints for Mathematical Programming models. ESOCCS synthesizes constraints of Linear Programming and Non-Linear Programming types using solely examples of feasible states of the modeled entity. This is a crucial feature from the viewpoint of modeling real-world business processes from data, as acquisition of examples of feasible states is straightforward for a normally operating process, while infeasible states corresponding to errors and faults are avoided in practice and thus uncommon. ESOCCS is verified on a suite of synthetic benchmarks having known model representations and synthesizes models with noticeable fidelity to the known representations in terms of syntax and semantics. We employ ESOCCS and an off-the-shelf solver in a fully automated setup for modeling and optimization of a real-world business process of rice production. The resulting optimal parameters of production are validated in the context of three exemplary rice farms. Likely increase in profit thanks to applying those parameters is concluded.

One-class synthesis of constraints for Mixed-Integer Linear Programming with C4.5 decision trees

We propose Constraint Synthesis with C4.5 (CSC4.5), a novel method for automated construction of constraints for Mixed-Integer Linear Programming (MILP) models from data. Given a sample of feasible states of a modeled entity, e.g., a business process or a system, CSC4.5 synthesizes a well-formed MILP model of that entity, suitable for simulation and optimization using an off-the-shelf solver. CSC4.5 operates by estimating the distribution of the feasible states, bounding that distribution with C4.5 decision tree and transforming that tree into a MILP model. We verify CSC4.5 experimentally using parameterized synthetic benchmarks, and conclude considerable fidelity of the synthesized constraints to the actual constraints in the benchmarks. Next, we apply CSC4.5 to synthesize from past observations two MILP models of a real-world business process of wine production, optimize the MILP models using an external solver and validate the optimal solutions with use of a competing modeling method.

Efficient Alignment Between Event Logs and Process Models

The aligning of event logs with process models is of great significance for process mining to enable conformance checking, process enhancement, performance analysis, and trace repairing. Since process models are increasingly complex and event logs may deviate from process models by exhibiting redundant, missing, and dislocated events, it is challenging to determine the optimal alignment for each event sequence in the log, as this problem is NP-hard. Existing approaches utilize the cost-based A* algorithm to address this problem. However, scalability is often not considered, which is especially important when dealing with industrial-sized problems. In this paper, by taking advantage of the structural and behavioral features of process models, we present an efficient approach which leverages effective heuristics and trace replaying to significantly reduce the overall search space for seeking the optimal alignment. We employ real-world business processes and their traces to evaluate the proposed approach. Experimental results demonstrate that our approach works well in most cases, and that it outperforms the state-of-the-art approach by up to 5 orders of magnitude in runtime efficiency.

Using logical decision trees to discover the cause of process delays from event logs

In real-world business processes it is often difficult to explain why some process instances take longer than usual to complete. With process mining techniques, it is possible to do an a posteriori analysis of a large number of process instances and detect the occurrence of delays, but discovering the actual cause of such delays is a different problem. For example, it may be the case that when a certain activity is performed or a certain user (or combination of users) participates in the process, the process suffers a delay. In this work, we show that it is possible to retrieve possible causes of delay based on the information recorded in an event log. The approach consists in translating the event log into a logical representation, and then applying decision tree induction to classify process instances according to duration. Besides splitting those instances into several subsets, each path in the tree yields a rule that explains why a given subset has an average duration that is higher or lower than other subsets of instances. The approach is applied in two case studies involving real-world event logs, where it succeeds in discovering meaningful causes of delay, some of which having been pointed out by domain experts.

Effective application of process improvement patterns to business processes

Improving the operational effectiveness and efficiency of processes is a fundamental task of business process management (BPM). There exist many proposals of process improvement patterns (PIPs) as practices that aim at supporting this goal. Selecting and implementing relevant PIPs are therefore an important prerequisite for establishing process-aware information systems in enterprises. Nevertheless, there is still a gap regarding the validation of PIPs with respect to their actual business value for a specific application scenario before implementation investments are incurred. Based on empirical research as well as experiences from BPM projects, this paper proposes a method to tackle this challenge. Our approach toward the assessment of process improvement patterns considers real-world constraints such as the role of senior stakeholders or the cost of adapting available IT systems. In addition, it outlines process improvement potentials that arise from the information technology infrastructure available to organizations, particularly regarding the combination of enterprise resource planning with business process intelligence. Our approach is illustrated along a real-world business process from human resource management. The latter covers a transactional volume of about 29,000 process instances over a period of 1 year. Overall, our approach enables both practitioners and researchers to reasonably assess PIPs before taking any process implementation decision.

Modeling Support for Role-Based Delegation in Process-Aware Information Systems

In the paper, an integrated approach for the modeling and enforcement of delegation policies in process-aware information systems is presented. In particular, a delegation extension for process-related role-based access control (RBAC) models is specified. The extension is generic in the sense that it can be used to extend process-aware information systems or process modeling languages with support for process-related RBAC delegation models. Moreover, the detection of delegation-related conflicts is discussed and a set of pre-defined resolution strategies for each potential conflict is provided. Thereby, the design-time and runtime consistency of corresponding RBAC delegation models can be ensured. Based on a formal metamodel, UML2 modeling support for the delegation of roles, tasks, and duties is provided. A corresponding case study evaluates the practical applicability of the approach with real-world business processes. Moreover, the approach is implemented as an extension to the BusinessActivity library and runtime engine.

Gamification

Gamification is about understanding and influencing human behaviours that organizations want to encourage amongst their workforce or customers. Gamification seeks to take enjoyable aspects of games – fun, play and challenge – and apply them to real-world business processes. Analysts are predicting massive growth of gamification over the next few years, but is there any substance to the benefits being touted? This article takes a critical look at the potential of gamification as a business change agent that can deliver a more motivated and engaged workforce.

CONCEPT LOCATION MODELING THROUGH BUSINESS PROCESS VIEWS

Concept location is a key activity during software modernization since it allows maintainers to exactly determine what pieces of source code support a specific concept. Real-world business processes and information systems providing operational IT support for respective processes can be misaligned as a consequence of uncontrolled maintenance over time. When concepts supported by an information system are getting outdated or misaligned, concept location becomes a time-consuming and error-prone task. Moreover, enterprise information systems (which implement business processes) embed significant business knowledge over time that is neither present nor documented anywhere else. To support the evolution of existing information systems, the embedded knowledge must first be retrieved and depicted in up-to-date business process models and then be mapped to the source code. This paper addresses this issue through a concept location approach that considers business activities as the key concept to be located and discovers different partial business process views for each piece of source code. Thus, the concept location problem becomes the problem of extracting such views. This approach follows model-driven development principles and an automatic model transformation is implemented to facilitate its adoption. Moreover, a case study involving two real-life information system demonstrates its feasibility.

Information systems frontiers editorial December 2012

Information Systems Frontiers (ISF) has crossed its first decade. We would like to thank all the Advisory Editors, the Executive Editors, the special issue editors and the various referees from across the world and of course Springer, the publishers, who have made this journal a success. In this last issue of 2012, ISF brings to you, nine articles which span the various topics at the frontiers of information systems. The lead article by Nobel Laureate, Arrow (2012) is a thought piece on economic theory and the financial crisis. In this article, Profesor Arrrow probes the role of the market in allocating resources. He suggests that the failure of markets for various kinds of derivative securities to perform properly is an essential element of the current financial crisis. Financial crises are not a new phenomenon and have been recognized by economists from John Stuart Mill (1848) onwards. Professor Arrow starts with the neoclassical general equilibrium framework, and seeks to identify a possible point at which it fails to supply a coherent theory of securities markets and so might possibly lead to some understanding of the repeated crises of the financial system underlying the development of capitalism. The article also touches upon one of the most important innovations in economic theory in the last 50 years–asymmetric information and points out that the presence of asymmetric information creates difficulties with the allocation of risks through the market. Professor Arrow argues that information is of the utmost importance in making economic decisions, especially when it comes to securities or other assets whose value depends on events that are yet unknown. Finally Professor Arrow discusses the issues of inefficient incentives and their impact on the financial crisis and concludes with some policy implications. The second article by Professors Zo et al. (2012) focuses on end-to-end reliability of service oriented applications. The paper focuses on reliability issues associated with applications developed within service oriented architecture. It develops a measure for deriving end-to-end application reliability. It further develops a model to help select appropriate services for tasks in the business process which accommodate the redundant and overlapping functionality of available services and planned redundancy in task support. A genetic algorithm approach is adopted to select promising services to assemble the application using endto-end reliability as the criterion of interest. The paper concludes with an application to a real-world business process. Professors Chunlin Li and Layuan Li develop an efficient resource allocation for maximizing benefit of users and resource providers in ad hoc grid environment (2012). The paper proposes an efficient resource allocation scheme for grid computing marketplace where ad hoc grid user agents can buy usage of memory and CPU from grid resource providers. The agents goal is to obtain optimized quality of service to accomplish their tasks on time within a given budget, and achieve profit maximization. The paper combines perspectives of adhoc grid users as well as resource providers. Simulations are conducted to compare the performance of the algorithms with related work. The fourth article, FAST: Fundamental Analysis Support for Financial Statements: Using semantics for trading recommendations (2012) is by Professors Alejandro Rodriguez-Gonzalez, Ricardo Colomo-Palacios & Fernando Guldris-Iglesias, Juan Miguel Gomez-Berbis and Angel Garcia-Crespo. The authors present a real trading system that has been developed using semantic technologies. These enable the definition of schemes that can be used for storing financial information, which, in turn, can be easily accessed and queried. Additionally, they incorporate R. Ramesh :H. R. Rao (*) SUNY Buffalo, Buffalo, NY, USA e-mail: mgmtrao@gmail.com

Impact of Enterprise Resource Planning Tools & Techniques in Management Education

Historically, business education is fragmented with different bodies of knowledge taught in separate functional courses. Although students become specialists in specified area, they many times miss the complete picture where interdependencies and interconnectedness among various business processes create efficient synergies in achieving business goals. The purpose of this research paper is to understand deeper nuances of an integrated educational environment & its impact on student communities, Faculty or teaching community & administration, society at large. Impact of Enterprise Resource Planning (ERP) in management education can be analyzed basis three major phases, namely: Phase I: Teaching ERP based Courses & provide guidance to educators on ways to effectively teach, in order to accomplish the synergistic goals of hands on exercises by students using the ERP package, illustrating real-world business processes & integration, re-designing the curricula in different functional courses. Phase II: Implementation of ERP based integrated Learning Management Systems (LMS) to carry on with day to day activities of the management of education. Phase III: Improving pedagogical tools, techniques & processes in an integrated manner to improve brand equity of the institute.

Generating event logs from non-process-aware systems enabling business process mining

As information systems age they become legacy information systems (LISs), embedding business knowledge not present in other artefacts. LISs must be modernised when their maintainability falls below acceptable limits but the embedded business knowledge is valuable information that must be preserved to align the modernised versions of LISs with organisations' real-world business processes. Business process mining permits the discovery and preservation of all meaningful embedded business knowledge by using event logs, which represent the business activities executed by an information system. Event logs can be easily obtained through the execution of process-aware information systems (PAISs). However, several non-process-aware information systems also implicitly support organisations' business processes. This article presents a technique for obtaining event logs from traditional information systems (without any in-built logging functionality) by statically analysing and modifying LISs. The technique allows the modified systems to dynamically record event logs. The approach is validated with a case study involving a healthcare information system used in Austrian hospitals, which shows the technique obtains event logs that effectively and efficiently enable the discovery of embedded business processes. This implies the techniques provided within the process mining field, which are based on event logs, may also be applied to traditional information systems.

End-to-end reliability of service oriented applications

As organizations move towards adopting a service oriented architecture that permits the coexistence of multiple technology environments, an increasing number of applications will be developed through the assembly of existing software components with standard web service interfaces. These components with web service interfaces may be available in-house, or may be supplied or hosted by external vendors. The use of multiple services, possibly utilizing different technologies, providers, locations, and sources, has implications for the end-to-end reliability of these applications to support a business process. Selecting the best service for individual tasks in a business process does not guarantee the most effective overall solution, particularly if criteria other than functional characteristics are employed. This paper examines reliability issues associated with applications developed within service oriented architecture. It develops a measure for deriving end-to-end application reliability, and develops a model to help select appropriate services for tasks in the business process which accommodate the redundant and overlapping functionality of available services and planned redundancy in task support to satisfy the reliability requirement of the resulting application. A genetic algorithm approach is adopted to select promising services to assemble the application using end-to-end reliability as the criterion of interest. An application to a real-world business process illustrates the effectiveness of the approach.