Jardim-Gonçalves, Ricardo

Zdravković, Milan

Enterprise Information Systems

English

Repositório da Universidade Nova de Lisboa

Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=teis20Enterprise Information SystemsISSN: 1751-7575 (Print) 1751-7583 (Online) Journal homepage: https://www.tandfonline.com/loi/teis20Model-driven data-intensive EnterpriseInformation SystemsMilan Zdravković & Ricardo Jardim-GonçalvesTo cite this article: Milan Zdravković & Ricardo Jardim-Gonçalves (2018) Model-driven data-intensive Enterprise Information Systems, Enterprise Information Systems, 12:8-9, 910-914, DOI:10.1080/17517575.2018.1526327To link to this article:  https://doi.org/10.1080/17517575.2018.1526327Published online: 04 Oct 2018.Submit your article to this journal Article views: 589View related articles View Crossmark dataCiting articles: 2 View citing articles EDITORIALModel-driven data-intensive Enterprise Information SystemsIntroductionThe special issue is motivated by the extensive growth of interest for embedding so-calledBig Data, Wireless Sensor Networks (WSN), Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) capabilities into traditional Enterprise Information Systems (EIS) architectures.In the EIS world, those new capabilities would significantly expand the scope of planningand monitoring to end-to-end processes, encompassing full supply chain, while alsoincluding new stakeholders or events having the impact to these processes.Networks of physical objects with sensing, data collection, transmission and actuationcapabilities, and vast endpoints in the cloud, offering large amounts of heterogeneousdata, become increasingly important enterprise resources and sources for fundamentalanalytics. The large number of identifiable devices, used also on a sharing basis, isexpected to become a commodity in the future, also providing a technology tool foremergence of so-called ‘sensing enterprise’ (Noran, Romero, and Zdravković 2014).Technology stack seem to be already there, with large number of cloud-based IoTplatforms, offering connectivity, storage, analytics and visualization in a secure way andat scale. However, critical issues still remain open.First, such diversity will pose tremendous challenges related to the interoperabilityissues. The new technological landscape, provided by the Future Internet systems willthus establish interoperability problems as critical and possibly consider the interoper-ability as an inherent capability of the future information systems.Second, while the interoperability is self-obvious challenge, implementation problemis inherited from the traditional EISs and even multiplied when considering significantlyincreased complexity of the EISs operating in IoT world. In order to capitalize on thetechnological trend of data sources commoditization and thus start evolving from digitalto sensing enterprises, existing EISs need to change. Such change may imply any of thefollowing: complete EIS re-design and re-deployment, full integration with off-the-shelftools or empowering existing systems with specific interoperability infrastructures.The objective of this Special Issue is to present state-of-the-art research in usingModel-Driven Engineering approaches, techniques, tools and practices to overcomeissues arising from transition from traditional process-focused to data-intensive EIS,where this transition is facilitated by any of the three above mentioned approaches.The Issue is also expected to highlight new challenges and opportunities, related to thenew role of EISs in the ‘datafied’ world we are living in, today.DiscussionModel Driven Engineering (MDE) is a software development approach in which abstractmodels of software systems are systematically transformed to their specificENTERPRISE INFORMATION SYSTEMS2018, VOL. 12, NOS. 8–9, 910–914https://doi.org/10.1080/17517575.2018.1526327© 2018 Informa UK Limited, trading as Taylor & Francis Groupimplementations. MDE is driven and motivated by the growing complexity of software,which additionally increases the gap between the problem-level software abstractions(e.g. requirements) and its implementation. Today, MDE approaches and practices arecommonly addressed by using a Model Driven Architecture (MDA).Different approaches of using MDE are notable at different levels of EIS implementa-tion, namely, system design (and requirements engineering as its vital part) and devel-opment. Furthermore, MDE has shown to be useful also for considering differentarchitectural viewpoints to the system, by designing it around different focal artefacts,such as interaction and process. While the role of MDE in systems based on relationaldata structures is quite clear, this is not the case in data-intensive systems which areoften based on loosely structured and unstructured data. Unveiling their hidden struc-tures and correlating those structures with existing data models are of crucial impor-tance for the effective implementation of those systems.System designAt the design level, widely accepted MDE standards – UML/SysML languages and toolsare often being used, although new modelling paradigms have started to emerge.Design-level use of MDE standards is important in development of very complexdistributed systems, which are difficult to maintain due to possible interoperabilityproblems. Examples of solutions to the design of such complex systems are providedin the domain of development healthcare systems (Dimovski et al. 2018) and manufac-turing systems (Liao et al. 2018). Liao et al. (2018) proposed the approach of combiningthe specific design (holonic) approach with using new modelling paradigm, convenientfor the analysis of interoperability issues, namely Notification-Oriented Paradigm (NOP).Requirements engineering plays important role and has large impact to the effective-ness of data-intensive systems design. Requirements elicitation is considered as one of thecritical factors for successful systems design as it is expected to facilitate reconciliation ofthe functional and implementation visions of one system, from two quite different andsometimes contradicting viewpoints – customers and developers. Formal representation ofthose requirements reduces or completely removes bias and ambiguity from their defini-tion and even facilitates reasoning and effective mining over the systems representation,thus enabling detection of incomplete or contradicting definitions (Diamantopoulos andSymeonidis 2018). However, the effectiveness of well-formed requirements can be pre-served only if they are continuously maintained throughout whole system engineeringprocess. This is particularly difficult in data-intensive systems due to the dynamics of theirenvironment, often unstructured data, etc. One of the proposed solutions to maintenancerelated problems is based on data-driven software systems, associated with user interfacescorresponding to used data structures, a software change request service and initialchange impact set (Stojanov, Dobrilović, and Stojanov 2018).Transition to online business models by enterprises assumes a significant shift inconsidering the requirements related to security issues. Security-by-design is a novelsystems engineering paradigm which aims to address security issues in a full engineer-ing lifecycle (Zolotas, Chatzdimitriou, and Symeonidis 2018).ENTERPRISE INFORMATION SYSTEMS 911System developmentAt the development level, researchers and practitioners use more implicit formalisms,namely the Domain Specific Languages (DSL), to enable even domain experts to developspecific components of data-intensive EISs.For example, DSLs can be used for development of intelligent agents in Multi-AgentSystems (MAS) and the development of micro-services. Both development paradigmsare quite relevant for the design and development of data-intensive EIS.Micro-services play an important role in data-intensive EIS and especially in IoT, asimplementation of the servitization and componentization principles in its designand development. Using DSL in their development can significantly increase thereliability and quality of micro-services, as well as reduce the redundancies in theirdesign (Terzić et al. 2018).Centralized architectures are proven inefficient in very complex data-intensive (espe-cially IoT) environments, due to a lack of flexibility, especially from the scaling perspec-tive. Peer-to-peer architectures aim to resolve this problem and Multi-Agent Systemparadigm is often used as a design approach. However, interoperability and platformdependence still remain difficult issues in implementing MAS. It is often addressed byimplementing the specific DSLs (Sredojević, Vidaković, and Ivanović 2018).While interoperability is the most crucial functional requirement in a conceptualsystems design, security is major non-functional one. Most often, security-relatedrequirements are considered only at infrastructural or design level, while their actualimplementation remains in a black box. RESTsec facilitates rapid security requirementsmodelling, based on the desired access control policy and generation of the enter-prise services, security infrastructure and actual code (Zolotas, Chatzdimitriou, andSymeonidis 2018).Interaction modellingTo facilitate interaction modelling in a heterogeneous environment and thus, to addressthe resolution of interoperability problem, many researches rely on the formal models,namely ontologies at different levels of system abstraction (Dimovski et al. 2018) (Liaoet al. 2018) (Diamantopoulos and Symeonidis 2018). The expressivity of formal models,combined with a range of available domain ontologies has shown useful also indesigning experiments and respective automatic code generation for test-beds, as apowerful alternative to the relevant DSL languages and especially complex experimentconfiguration tools (Jelenković, Tošić, and Nejković 2018).Modelling interactions have tremendous impact to design of peer-to-peer envir-onments, such as MAS. Use of MAS in enterprise is significantly constrained by thelevel of autonomy of the intelligent agents. Reliability of such enterprise MASinfrastructures is questionable due to the fact that intelligent agents often interactand/or make decisions based on incomplete knowledge. In order to increase thereliability, negotiation model is being developed and implemented in a platformwhich aims to increase the quality of interactions in collaborative working environ-ments (Cretan et al. 2018).912 EDITORIALProcess modellingA processmodel perspective is widely used discourse to understand the inner workings of thecomplex EIS, for the purpose of its analysis, optimization and/or re-engineering (Dimovskiet al. 2018). However, this perspective is not commonly used in WSN, CPS and IoT systems. Insuch systems, the processes are defined in ad-hocmanner, which could create different issues,for example in systems re-engineering. The first of the research problems related to this issueis lack of understanding of underlying process structures and it could be addressed byimplementing process discovery and mining from data streams (Moisescu et al. 2018).New architectural trends in designing EISs, especially in big data world are posing newchallenges. With appearance of peer-to-peer interoperability approaches and increasingtrend of servitization of IT functionalities, the design of EIS is more difficult to conform tothe unique process model. Thus, this model needs to be continuously validated andrepaired in cases where it does not correspond anymore to the realities of EIS operation.Process repair is a specialization of process mining approach in which the process model ischanged based on the analysis of the event logs from EIS or actual recorded activities thattook place. Choice structures method can be used to repair Petri-net process representa-tions, while taking into account both precision and simplicity (Qi et al. 2018).Data structuresWith introducing big data in enterprise, EIS landscape now needs to consider sources ofloosely structured or unstructured data, residing in distributed, big data repositories, such asHadoop. In order to unlock the potential for using this data, especially for analytical purposes,those loose structures need to bemapped to relationalmodels of the core EISs. In fact, in a bigdata world, data is typically unstructured andmodels have descriptive rather than prescriptiverole. NoSQL databases addresses challenges related to non-functional requirements, such asperformance, availability and scalability, while SQL models consider information design.Relational data can be migrated to NoSQL storage or relational and non-relationaldatabases can be integrated, but both approaches imply significant effort and time.Schema on read modelling approach combines read modelling and data virtualiza-tion techniques to achieve integration of structured and unstructured data (Jankovićet al. 2018). Instead of integrating both sources, Bjeladinović (2018) suggest that some-times there is a strong need for simultaneous use of SQL and NoSQL databases. HybridSQL/NoSQL databases are strong candidate for solution of the above problems. Theapproach in their development is proposed (Bjeladinović 2018) and it also encompassesrequirements analysis and design and redesign processes. Zečević et al. (2018) take onestep further and besides extracting data structures from data streams, they considerusing this structure as a formal, executable specification for a specific target platform.ReferencesBjeladinović, S. 2018. “A Fresh Approach for Hybrid SQL/NoSQL Database Design Based on DataStructuredness.” This SI. doi:10.1080/17517575.2018.1446102.Cretan, A., R. Jardim-Goncalves., C. Coutinho., C. Agostinho., and B. Bratu. 2018. “CoordinatingNegotiations in Data-Intensive Collaborative Working Environments Using an Agent-BasedModel-Driven Platform.” This SI. doi: 10.1080/17517575.2018.1521998ENTERPRISE INFORMATION SYSTEMS 913Diamantopoulos, T., and A. Symeonidis. 2018. “Enhancing Requirements Reusability throughSemantic Modeling and Data Mining Techniques.” This SI. doi:10.1080/17517575.2017.1416177.Dimovski, A., B. Atanasovski., M. Bogdanović., G. Velinov., L. Stoimenov., B. Koteska., D. Janković., I.Skrčeska., M. Kon-Popovska., and B. Jakimovski. 2018. “On Defining a Model Driven Architecturefor an Enterprise E-Health System.” This SI. doi: 10.1080/17517575.2018.1521996Janković, S., S. Mladenović, D. Mladenović, S. Vesković, and D. Glavić. 2018. “Schema on ReadModeling Approach as a Basis of Big Data Analytics Integration in EIS.” This SI. doi:10.1080/17517575.2018.1462404.Jelenković, F., M. Tošić, and V. Nejković. 2018. “Semantic Driven Code Generation for NetworkingTestbed Experimentation.” This SI. doi:10.1080/17517575.2018.1509135.Liao, Y., H. Panetto, P. C. Stadzisz, and J. M. Simão. 2018. “A Notification-Oriented Solution for Data-Intensive Enterprise Information Systems – A Cloud Manufacturing Case.” This SI. doi:10.1080/17517575.2018.1470258.Moisescu, M., D. Repta., I. Dumitrache., I. Sacala., A. Stanescu., and S. Caramihai. 2018. “AutomatedProcess Recognition Architecture for Cyber-Physical Systems.” This SI. doi: 10.1080/17517575.2018.1521997Noran, O., D. Romero, and M. Zdravković. 2014. “The Sensing Enterprise: Towards the NextGeneration Dynamic Virtual Organizations.” IFIP Advances in Information and CommunicationTechnology 434: 209–216.Qi, H., Y. Du, L. Qi, and L. Wang. 2018. “An Approach to Repair Petri Net-Based Process Models withChoice Structures.” This SI. doi:10.1080/17517575.2018.1432768.Sredojević, D., M. Vidaković, and M. Ivanović. 2018. “ALAS: Agent-Oriented Domain-SpecificLanguage for the Development of Intelligent Distributed Non-Axiomatic Reasoning Agents.”This SI. doi:10.1080/17517575.2018.1482567.Stojanov, Ž., D. Dobrilović, and J. Stojanov. 2018. “Extending Data-Driven Model of Software withSoftware Change Request Service.” This SI. doi:10.1080/17517575.2018.1445296.Terzić, B., V. Dimitrieski, S. Kordić, G. Milosavljević, and I. Luković. 2018. “Development andEvaluation of MicroBuilder: A Model-Driven Tool for the Specification of REST MicroserviceSoftware Architectures.” This SI. doi:10.1080/17517575.2018.1460766.Zečević, I., P. Bjeljac, B. Perišić, S. Stankovski, D. Venus, and G. Ostojić. 2018. “Model DrivenDevelopment of Hybrid Databases Using Lightweight Meta-Model Extensions.” This SI.doi:10.1080/17517575.2018.1445295.Zolotas, C., K. C. Chatzdimitriou, and A. L. Symeonidis. 2018. “RESTsec: A Low-Code Platform forGenerating Secure by Design Enterprise Services.” This SI. doi:10.1080/17517575.2018.1462403.Milan ZdravkovićFaculty of Mechanical Engineering in Niš, Unviersity of Niš, Niš, Serbiamilan.zdravkovic@gmail.comRicardo Jardim-GonçalvesUNINOVA – Instituto de Desenvolvimento de Novas Tecnologia, New University ofLisbon, Lisbon, Portugalhttp://orcid.org/0000-0002-3703-6854Received 11 September 2018; accepted 17 September 2018914 EDITORIAL

Model-driven data-intensive Enterprise Information Systems

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Model-driven data-intensive Enterprise Information Systems

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