Infusing scientific foundations into Enterprise Interoperability

Computers in Industry 63 (2012) 858–866

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Computers in Industry journal homepage: www.elsevier.com/locate/compind

Infusing scientific foundations into Enterprise Interoperability Fenareti Lampathaki a,*, Sotiris Koussouris a, Carlos Agostinho b, Ricardo Jardim-Goncalves b, Yannis Charalabidis a, John Psarras a a b

Decision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou str., 15780 Athens, Greece CTS, UNINOVA, Dep. de Eng. Electrote´cnica, Faculdade de Cieˆncias e Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, Portugal

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 December 2011 Received in revised form 23 July 2012 Accepted 1 August 2012 Available online 24 August 2012

In a turbulent world where technological evolution has surpassed even the most imaginative scenarios predicted a few years ago, interoperability remains an intangible and elusive challenge. Associated with cost, risk and complexity reduction, Enterprise Interoperability can be defined as a capability of two or more enterprises, including all the systems within their boundaries and the external systems that they utilize or are affected by, to cooperate seamlessly, over a sustained period of time to pursue a common objective. During the last decade, substantial advancements at an application level have been made through EU- and national funded research. However, the lack of scientific foundations seems to hinder unlocking its full potential. In this context, the aim of this paper is to investigate the pathway towards establishing a science base and to provide an overview of the main milestones in the fascinating quest that shall eventually shape interoperability as a scientific discipline. ß 2012 Elsevier B.V. All rights reserved.

Keywords: Enterprise interoperability Science base Evolution waves Scientific foundations Taxonomy

1. Introduction Science is primarily an activity of extending perception into new contexts and into new forms, and as a means of obtaining what may be called reliable knowledge [1], whilst aiming at finding models that will account for as many observations as possible within a coherent framework [32]. The history of science has shown that ‘‘such puzzling concepts as force, energy, etc., are manmade and were evolved in an understandable sequence in response to acutely felt and very real problems’’. They were not handed down by some celestial textbook writer to whom they were immediately self-evident [2]. It is well acknowledged that an underlying scientific discipline typically evolves over several decades in incremental stages before being established as a science. There is actually no role model for showing that there is a successful way to initiate a new scientific discipline using a linear ‘‘push approach’’. It is indeed a matter of evolution over decades depending on complex dynamics, which are far beyond steering and control, and only accessible by intervention and policy instruments on the empirically well-researched micro level in helping to shape supportive environments in which what will happen anyway can happen in a better way. A theory or discipline

* Corresponding author. E-mail addresses: fl[email protected] (F. Lampathaki), [email protected] (S. Koussouris), [email protected] (C. Agostinho), [email protected] (R. Jardim-Goncalves), [email protected] (Y. Charalabidis), [email protected] (J. Psarras). 0166-3615/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.compind.2012.08.004

which actually purports to be scientific needs to present the following characteristics (adapted by [3]): (a) It has to be more progressive than alternative theories over a long period of time, and faces many unsolved problems, and (b) The community of practitioners should make great attempts to develop the theory towards solutions of the problems, show concern for attempts to evaluate the theory in relation to others, and should consider confirmations and disconfirmations in a continuous manner. However, over the 20th century, links between science and technology have grown increasingly strong with many technological domains claiming their position in the scientific status quo [36]. In the domain of Future Internet Enterprise Systems (FInES), establishing an Enterprise Interoperability Science Base (EISB) has been a long-sought challenge that was originally documented back in 2006 in the 4th version of the EI Research Roadmap [4] as mentioned by Charalabidis et al. [5]. According to the European Commission, such a Science Base comprises a new set of concepts, theories and principles derived from established and emerging sciences, with a view to long-term problem solving as opposed to short-term solution provisioning. The overall objective in establishing an EISB is to formulate and structure the knowledge gained through pragmatic research in the domain over the last decades and more, in order to avoid repeating research and missing opportunities for application [36].

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In general, interoperability is defined as ‘‘the ability of systems, units, or forces to provide services to and accept services from other systems, units, or forces and to use the services so exchanged to enable them to operate effectively together’’ [6]. IEEE defines interoperability as ‘‘the ability of two or more systems or components to exchange information and to use the information that has been exchanged’’. Through the years, however, interoperability tends to obtain a broader, allinclusive scope of a repetitive, well organized, and automated at ICT level feature of organizations, as indicated in the definition of the draft EIF 2.0 [7]: ‘‘Interoperability is the ability of disparate and diverse organizations to interact towards mutually beneficial and agreed common goals, involving the sharing of information and knowledge between the organizations via the business processes they support, by means of the exchange of data between their respective information and communication technology (ICT) systems’’ and the Enterprise Interoperability Research Roadmap [5] ‘‘Interoperability is a utility-like capability that enterprises can invoke on the fly in support of their business activities’’. Actually, during the last years the term Enterprise Interoperability has been coined by the Future Internet Enterprise Systems Cluster. In the Enterprise Interoperability Research Roadmap (EIRR), Enterprise Interoperability is defined «as a field of activity with the aim to improve the manner in which enterprises, by means of Information and Communications Technologies (ICT), interoperate with other enterprises, organizations, or with other business units of the same enterprise, in order to conduct their business. This enables enterprises to, for instance, build partnerships, deliver new products and services, and/or become more cost efficient». Enterprise Interoperability is also referred to as the ability of enterprises and entities within those enterprises to communicate and interact effectively [8]. In the present paper, Enterprise Interoperability is defined as the capability of two or more enterprises, including all the systems within their boundaries and the external systems that they utilize or are affected by, in order to cooperate seamlessly, over a sustained period of time to pursue a common objective. Along these lines, the aim of this paper is to investigate the pathway towards establishing a science base and to provide an overview of the main milestones in the fascinating quest that shall eventually shape interoperability as a scientific discipline. The present paper is structured as follows: Upon studying the methodological framework that needs to be adopted in Section 2, the Action Plan of related scientific disciplines is analyzed and designates the perspectives and lessons learnt from their paradigm in Section 3. In Section 4, the Enterprise Interoperability Science Base Action Plan is presented in detail giving way to Section 5, where the key achievements of the work carried out in this direction are presented. Finally, Section 6 that concludes this paper. 2. Methodological framework towards the defining an Action Plan for the EISB development Enterprise Interoperability is in its Pre-paradigm phase, according to Kuhn [9], due to its several incompatible and incomplete application theories, which are accompanied with the growing recognition that many aspects of interoperability cannot be completely understood from current disciplinary perspectives in the information society. In this context, an Action Plan shall outline a concrete set of activities that need to be collectively undertaken by stakeholders with different backgrounds in a logical time frame in order to eventually lead to the general recognition of the scientific

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rigorousness of the domain. Particular emphasis needs to be also laid on the key challenges and the perspectives, which are opened up through its scientific establishment. In order to develop such a meticulous and concise Action Plan, it is necessary to pave the way by analyzing the already developed content of the domain and put the plans into public consultation amongst all interested stakeholders, to minimize the risk of following the wrong path and ending up with a non-applicable and unrealistic perception of how things should be done. For these reasons, the following steps have been followed: I. Step I: Study which are the key ingredients of a science and a science base. The fundamentals of the philosophy of sciences and epistemology provide the core principles, commonalities and structure of sciences at a conceptual level that provide significant input on the perspective under which the EISB Action Plan should be developed. II. Step II: Absorb lessons learnt from the neighbouring scientific domains paradigm. A rational selection of scientific disciplines which can be characterized as neighbouring to EI is performed taking into account specific criteria. In each discipline, the stages of development are analyzed in order to gain insight into the process of cultivating a fertile ground for a new scientific base. III. Step III: Adapt to the context of the Enterprise Interoperability Science Base. Since the Action Plan is applied to EI, specific needs and requirements emerging from the state of the art shall be taken into account in order to ensure its appropriateness. IV. Step IV: Formulate the EISB Action Plan. A generic, yet focused Action Plan for EISB is defined based on the valuable outcomes of the previous steps. V. Step V: Deliberate on the EISB Action Plan. A public consultation with key experts and scientists takes place with the help of online mechanisms and face-to-face validation workshops. VI. Step VI: Redefine the EISB Action Plan. During the process, whenever necessary, appropriate corrective actions addressing the experts’ valuable feedback on the Action Plan are taken. VII. Step VII: Deliver the EISB Results on the Waves that actually start implementing the Action Plan foreseen. Particular care has been taken in order to ensure that the design of the prospective EISB is inclusive and neutral, not biased towards or against any existing discipline or approach. 3. Neighbouring scientific disciplines paradigms Recognizing the omnipresent interdependencies amongst scientific disciplines, it cannot be expected that EI will be completely independent from other disciplines. In this direction, EI needs to be analyzed together with a selection of established and emerging sciences amongst the neighbouring scientific domains that can inspire the development of a scientific base for EI. Such neighbouring sciences are selected based on the following criterion: ‘‘Scientific disciplines whose research activities target new sets of concepts, theories, methods, techniques and practices for solving interoperability related problems in their own context, and that can somehow contribute to answering the formulated EI open research questions on a scientific basis’’. To develop the Action Plan for the development of the EISB, the authors studied the Action Plans that today resulted in established science-based neighbouring domains. The next paragraphs describe the Action Plans of 2 scientific areas selected as closest to EI.

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3.1. Action Plan: computer science The computing field has grown enormously since its inception in the 1930s. It began with the marriage of mathematical logic and digital electronics and has matured into a complex of fields gathered under the large umbrella called computing, informatics, and sometimes information technology (IT). According to Denning et al. [10], the advance of the computer science field fitted in four stages of development mirroring the human development phases: 1. Infancy: Algorithms devised by Pascal, Leibniz, and Gauss were used extensively to create tables of trigonometric, logarithmic, and exponential functions. During this stage the first calculating machines were build, which culminated with an all-digital computer (‘‘Z4’’). This way, computer science took digital electronic computer and married it with three historical lines: mathematical logic, engineering, and science. Digital electronic brought the infrastructure, mathematical logic brought notations for algorithms, universal machines, and mapping from logic formulas to physical switching circuits, whilst engineering brought know-how for mechanical calculation and much expertise in electronics and electro-mechanical systems. Finally, science brought a wealth of applications and methods for predicting the behaviour of physical systems from their computational models. 2. Childhood: The first computer building projects succeeded and stimulated interest in the new technology. Over the next years, the computing industry invented many technologies in programming languages, computer architectures, storage systems, time sharing, virtual memory, remote access, databases, graphics, and robotics. In parallel, the academic world started creating courses in computers and computation. 3. Adolescence: This third period was a time of great technological advances in computing, with the computer chip and the Internet, which grew very slowly at first, reaching about 200 nodes by 1980. Then it started to take off, reaching about 200,000 nodes by 1990. The Internet and the personal computer advanced together, accelerating each other’s progress. 4. Young adulthood: In this last period, the ‘‘IT profession’’ initiative was launched, thus recognizing that the field had evolved from a discipline to a profession. The initiative responded to the growing interest in the industry for professional standards. 3.2. Action Plan: software engineering Software engineering is a recognized sub-domain of computer science that has emerged as the principled understanding of the large-scale structures of software systems [11]. From its roots in qualitative descriptions of empirically observed useful system organizations, software engineering has matured to encompass a broad set of notations, tools, and analysis techniques that provide guidance for complex software design and development. In general, and according to Redwine and Riddle [12], software technologies have been developed and propagated over 15–20 years, through the following 6 typical phases: 1. Basic research where basic ideas and concepts were investigated, thus putting an initial structure on the problem, and framing critical research questions. 2. Concept formulation where ideas were circulated informally and a research community was developed, converging on a compatible set of ideas, solving specific sub-problems, and refining the problem’s structure.

Table 1 Scientific domains development stages compared with human development. Human development lifecycle

Scientific domains development

Infancy Childhood Adolescence Young adulthood Maturity

Foundational principles Concept formulation Development and extension Internal/external enhancement and exploration Popularization

3. Development and extension where preliminary applications of the technology were explored, clarifying underlying ideas, and generalizing the approach. 4. Internal enhancement and exploration where the approach was extended to other domains, using the technology for real problems, stabilizing it, as well as developing training materials. 5. External enhancement and exploration which was similar to the previous stage but involving a broader community of people besides developers. 6. Popularization to develop production-quality, supported versions of the technology, to commercialize and market the technology, and to expand the user community. As a conclusion, history has been demonstrating that the development of a scientific domain follows a multi-stage life cycle, which can be described in comparison with the human development, as in computer science. These stages comprehend the major phases of the science development that can be matched almost directly to the procedures followed in other disciplines (see Table 1), e.g. software engineering. 4. Roadmap for the Enterprise Interoperability Science Base Taking into consideration the fundamentals in establishing a science and the multi-stage incremental evolution of neighbouring scientific domains following a life cycle that can be described in comparison with the human development, the roadmap for the envisioned EI science base begins with the Foundational Principles and the Core Concepts Formulation, evolves through the Development and Extension, & Internal Enhancement and Exploration phases, and concludes to the External Enhancement and Exploration, & Popularization phase. In particular, the Action Plan of the Enterprise Interoperability Science Base aims at meeting the following objectives:  To dive into the basic principles, the commonalities and the lessons learnt from the history of sciences and epistemology and apply them in the EI context.  To reach consensus on a broad set of actions that will eventually lead to the creation of a reusable Science Base for Enterprise Interoperability.  To propose the stages of development, the so-called EISB waves, for the Enterprise Interoperability Science Base and decide their accompanying actions.  To outline the milestones and the key outcomes which are associated with each wave. In order to pave the way towards structuring the EISB (Enterprise Interoperability Science Base) and eventually taking up some of the initial steps of the EISB building process by conducting focused research in specific areas, the long-envisioned EISB will evolve in three (3) different but logically connected ‘‘waves’’ of activities [13,14]:

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 EISB Wave 1 – Basic Elements, with regard to the ‘‘Infancy’’ and ‘‘Childhood’’ stages.  EISB Wave 2 – Hypothesis and Experimentation, which corresponds to the ‘‘Adolescence’’ and ‘‘Young Adulthood’’ stages.  EISB Wave 3 – Empowerment, matching to the ‘‘Maturity’’ stage. These waves are analyzed in the following paragraphs. 4.1. EISB Wave 1 – basic elements The EISB first wave aims at providing the ability to identify and describe problems and solutions in the field of EI, and at establishing the research community, towards a sound convergence on the concepts in use. It sorts out the core aspects of the Interoperability scientific foundations towards the formation of inclusive and solid definitions of the main issues of the domain, whilst it deals with the identification and description of open scientific problems. In more detail, this wave includes the following actions:  Foundational principles: investigation of basic ideas and concepts, initial formal methods to describe problems and solutions, patterns identification, critical research questions (i.e. how to create and sustain interoperability [15]). Formal approaches in this area include a collection of methods stemming from mathematical formulation, such as First Order Logic, Category Theory, Pattern Theory. Another set of formalization attempts includes management and information technology systemic approaches, such as Model Driven Architecture (MDA), Business Process Management (BPM) or even Service Oriented Architecture (SOA) elements [33,35]. It has to be noted that specific methodologies are probably needed for each interoperability aspect that generates the need for diversified formal methods for technical, semantic, organizational, legal and policy issues.  Concept formulation: circulation of solution ideas, development of a research community, convergence on a compatible set of ideas, solutions on specific sub-problems, refinement of fundamental problems structure. This step works extensively on the definition of solution ideas for various interoperability problems, as they are defined in the previous step, whilst also developing and stimulating the research community – possibly through the use of on-line, Web 2.0 or similar infrastructures. In this context, the present wave shall firstly define the overall taxonomy of the EI domain and at a second stage shall identify the major issues per research area. The formalization of those problems and abstraction of their description will empower the ability of researchers to similarly identify and describe and resolve other related problems (belonging to higher granularity levels), with the identification, analysis and abstraction of the solution space and its included methods. In parallel, this wave aims at formulating the research community, which will undertake each one of the identified issues, and will distribute the accomplished results to wider audiences for attracting the interest of the major stakeholders of the domain and of other external experts, belonging to neighbouring domains. In summary, the specific objectives of Wave 1 are:  To consolidate the EISB structure and terminology  To identify evidences of interoperability in neighbouring domains  To set up the theoretical foundations of Enterprise Interoperability

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 To define the Enterprise Interoperability problems and solutions space  To curate existing approaches to key problems  To synthesize the EI community  To consolidate and validate the fundamental elements  To identify research priorities and provide a set of concrete future research recommendations 4.2. EISB Wave 2 – hypothesis and experimentation The EISB second wave builds upon the initial EISB foundations defined in Wave 1, i.e. the identification and description of EI scientific problems and EI foundation principles, with a view to stabilizing research products, methods and tools in a reusable, extendable and sustainable manner, as well as to constructing application scenarios that will prepare for the popularization of EI in the third wave. Impact assessment and simulation, together with the development of a training curriculum, is a requirement for the accomplishment of this stage. Furthermore, targeting a broader community, this wave focuses on identifying hypothesis and nurtures discussions and experiments in order to reach consensus on the challenges or to improve the basic elements defined in the first wave. The second EISB wave purposes can be summarized as follows:  Development and extension: Exploration of preliminary applications of the technological and scientific principles, populations of formal descriptions and generalization of the various approaches. This step will have to bring in extensive experimental applications, where several of the developed approaches can now be instantiated, thus formulating a more complete set of scenarios, including limitations in specific contexts. This exploration is most likely to bring a differentiated set of practices, followed in result-oriented approaches and reallife projects, to further enhance the available, populated exemplary patterns. It is also likely that such population approaches will soon result in the definition and maintenance of one or more, syndicated, Knowledge Bases with interoperability research methods and their results, in various contexts and domains.  Internal enhancement and exploration: Extension of the approaches to vertical domains, application of the technology in real problems, stabilization of technological means, initial assessment of impact, development of training curricula and material. This step constitutes a further, full width exploration of various interoperability problem solving methods and tools, now in real-life situations of specific industrial sectors. Specific approaches are bound to appear for sectors like manufacturing, process industry, health, government, small and medium enterprises, supply chain integration, telecommunications and so on. The well-known issue of interoperability impact assessment is also to be tackled in this step, most probably in a sector-specific way. The availability of formal methods, solution tools, real-life application examples and impact assessment will also form the core for the interoperability training curriculum, to be brought in a more systematic framework. In summary, the specific objectives of Wave 2 are:  To formalize the hypothesis for Enterprise Interoperability  To bridge the Enterprise Interoperability problems and solutions space  To formulate an Assessment Framework for Enterprise Interoperability

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To assess the impact of the EI solution space To enable real-life experiments on problem-solution paths To generalize models and tools To enrich the solution space with result-oriented practices To lay the groundwork for a young generation of researchers that will advance research in Enterprise Interoperability  To consolidate and validate the fundamental elements  To identify research priorities and provide a set of concrete future research recommendations     

of EI application problems; and a domain knowledge base that would contain the effective structure and methodological knowledge of the EISB, all together enabling the reception of inputs from practical experience, requirements, research contributions, and producing efficient EISB applications and new research requirements. Such a Knowledge Base is sustained in the FInES wiki [16] to ensure the reception and presentation of EISB requirements, experience, research contributions and solutions provided. The EISB contribution along the waves can be further specified as:

4.3. EISB Wave 3 – empowerment The EISB third wave aims at empowering the scientific foundations for Enterprise Interoperability through proper liaisons with the scientific, research and stakeholders communities, highlighting the quality of the industrial solutions and the substantiation of value. In particular, the third EISB wave includes:  External enhancement and exploration: Communication towards a broader community, substantiation of value and applicability, detailing towards complete system solutions, embodiment within training programmes. This is a rather progressed step, ‘‘announcing’’ the findings to a broader scientific community, whilst also continuing the embodiment of the scientific approaches to university and vocational training programmes. Substantiation of applicability refers to convincing, detailed, sector-specific solution examples that are to be available. An important element will also be related to the completeness of the approach, realizing end-to-end interoperability solutions, covering technical, semantic, organizational and enterprise issues in a sustainable way.  Popularization: Standardization and methodologies for production quality, systematic assistance in commercialization and marketing of scientific offerings. This last stream of activities is aimed at bringing an overall enhanced quality to the interoperability scientific offerings, elevating them, if possible, to industrial-strength level. If this level is reached, commercialization of the various offerings will be made possible, for methods, tools and services to be provided towards administrations or enterprises of various sizes. In particular, the specific objectives of Wave 3 are:  To inspire a new generation of scientists that will advance research on Enterprise Interoperability  To communicate results towards a broader community  To create EI business plans  To commercialize the EI solutions  To define EI standards  To design the EI cookbook as a comprehensible manuscript documenting the engineered problem/solution paths  To identify research priorities and provide a set of concrete future research recommendations 5. Key milestones of the EISB roadmap implementation At the moment of composing the paper at hand, an initial attempt towards the establishment of the EISB has already commenced in the European Union, supported by the ENSEMBLE FP7 project [30]. As it is natural, the focus lies mostly in the first wave of the aforementioned EISB Action Plan, as the basic ingredients of EI should be thoroughly investigated and analyzed before initiating specific actions of the next waves. The work undertaken so far has been framed through the definition of: a problem space that would address the range of application and theoretical problems addressed by the EI domain; a solution space, covering the knowledge available for the solution

 Elaboration on the EISB Scientific Areas that aim at classifying the domain knowledge and form the backbone of the EISB taxonomy and ontology.  Collection and classification of existing EI Methods and Tools as part of the solution elements (i.e. Concepts & Positions, Methods, Proof-of-concept, Tools, Experiments, Case Studies, SurveysEmpirical Data, Standards), that also match to current outstanding needs (problems).  Identification of Neighbouring Domains Methods which contribute equally to the problem and solution space, providing formal methods that can be used in the specification of EI problems, and also provide new insights of possible solutions already being addressed within other communities.  Formulation of the key EI principles and laws that govern EI. They should be observations, guidelines, do’s and don’ts which are grounded in observation and rationalization from cases and published, discussed and modified.  Definition of the EISB Assessment Framework that enables to measure the interoperability level of enterprise systems and applications. With this framework, companies will become able to recognize their specific interoperability problems and identify their desired to-be situation.  Elaboration on an interoperability problems/solutions matrix both in a bottom-up (recognizing solutions to the known issues/ challenges) and in a top-down way embarking from the interoperability assessment for each area.  Specification of the EISB supporting Tools, which start from analyzing and evaluating the current status of an enterprise in terms of Enterprise Interoperability, and through simulating the benefits and analyzing the complexity, recommendations and problem solution paths are to be provided for reaching the desired level of interoperation with other organizations.  Collection and classification of readily available EI training material and outline of a syllabus for an academic post-graduate programme that will introduce the EISB results in a concise way to a new generation of scientists.  Preparation of EISB cookbook-like guidelines for enterprises and researchers in order to help them understand how they may utilize the EISB results.  Recognition of a set of open research issues for the future. Such activities are not expected to be concluded in the frame of any project, mainly due to the need to engage the whole of the EI research community over a long time period. However, the work carried out by the authors can be seen as a starting point that aims to ease out various early stage difficulties that such an Action Plan could phase. The next paragraphs emphasize on the results related to the EISB Scientific Areas and the neighbouring domains recognition. 5.1. Defining the structure of Enterprise Interoperability In order to formulate an inclusive, yet flexible taxonomy for the EI domain, which will facilitate focused and targeted research by scientific communities, the approach adopted bears the following steps:

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(1) Consideration of the terms ‘‘Enterprise’’ and ‘‘Business Transaction’’ and decomposition of the enterprise concept in its major ingredients (e.g. strategy, assets, processes, knowledge, etc.). (2) Analysis of the major technologies behind the term ‘‘Enterprise 2.0’’ (like Cloud Computing, Social Networks) and of current technological trends that are related to the Enterprise world (like Internet of Services, Internet of Things, etc.). (3) Identification of the key EI challenges, as documented in the EI Research Roadmaps. (4) Definition of a common EI taxonomy glossary in order to ensure common understanding of the key underlying terms. Achieving interoperability generally requires resolution of issues at various distinct interoperability layers as argued by various authors in the last decade as shown in Table 2. This variety of definitions is one of the most important problems in EI. Although the presence of different perspectives and interpretations is quite useful as they try to capture the problem from various angles and business perspectives, this fragmentation has resulted in a huge set of definitions that endure the problem of achieving a common understanding amongst all researchers and practitioners. The different layers, as currently proposed in the bibliography, define at high level the necessary stack for interoperable systems, however, their abstraction level hinders researchers and practitioners to really identify problems and provide solutions, as those levels do not only overlap in many cases, but they also hide important low level aspects that deal with technologies and methods that span across all levels. For example, when we talk about data interoperability, semantic interoperability is applied as far as the concepts and their relations are concerned and technical interoperability is also related as far as the syntax and the data exchange is concerned. In this context, in order to identify a proper structure for Enterprise Interoperability, which can at a second stage be mapped to the fundamental layers adopted by other researchers, one has to focus on the real object of observation, which is the ‘‘Enterprise’’, and by analyzing it in its core components to identify the interoperability needs within them.

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An Enterprise, as defined in [17] is ‘‘. . .an organization designed to provide goods, services, or both to consumers.’’ The main ingredients of such a system are the following:  Infrastructures referring to all the facilities and non-human assets possessed by an enterprise, which are used for their operation. Under infrastructures, software platforms, hardware systems, building facilities, automobiles, etc. can be also found.  Data used for the business transactions within and outside the boundaries of the enterprise. This includes the documents, application forms, transactional data exchanged by the enterprise.  Processes including all the related, structured activities or tasks that produce a specific service or product.  Strategy and Policies embracing the different philosophies and rules that are applied either due to external (e.g. legislation, business association rules, etc.) or internal factors (e.g. working hours, dress code, etc.).  People with all the human resources that are part of an enterprise system. Starting from those core ingredients of an Enterprise, and by analyzing the current technological trends and the background knowledge of the domain of Enterprise Interoperability, the Scientific Areas (SA) are formulated as following (adapted by [18]):  [S.A.1] Data Interoperability: The ability of data (including documents, multimedia content and digital resources) to be universally accessible, reusable and comprehensible by all transaction parties (in a human-to-machine and machine-tomachine basis), by addressing the lack of common understanding caused by the use of different representations, different purposes, different contexts, and different syntax-dependent approaches;  [S.A.2] Process Interoperability: The ability to align and connect processes of different enterprises, in order for them to exchange data and to conduct business in a seamless way;  [S.A.3] Rules Interoperability: The ability of enterprises to align and match their business and legal rules for conducting

Table 2 Interoperability layers. Approach

Classification layers

Levels of Information System Interoperability [6]

Five interoperability maturity levels, i.e. Isolated Systems, Connected Systems, Distributed Systems, Domain Systems and Enterprise Systems, affecting four interoperability attributes: Procedures, Applications, Infrastructure and Data. Five levels are identified: Independent, Ad hoc, Collaborative, Integrated, and Unified. Five categories: No Data Exchange, Unstructured Data Exchange, Structured Data Exchange, Seamless Sharing of Data, and Seamless Sharing of Information. Three layers: Business layer, Knowledge layer and ICT systems layer. Semantic and Quality dimensions, cutting across the three identified layers, focusing on supporting mutual understanding on all layers. Three layers: Technical interoperability, Semantic or business interoperability, Organizational interoperability. Sector-specific issues can cut through the entire stack. Four interoperability types: Connection, Communication, Consolidation, and Collaboration, containing three objects of integration: Channel, Information, Process. Three parts: Conceptual integration, Applicative integration, and Technical integration that include: Interoperability at the enterprise/business level, Interoperability of processes, Interoperability of services, and Interoperability of information/data. 5 levels in the EIMM (Enterprise Interoperability Maturity Model): Performed, Modelled, Integrated, Interoperable, and Optimizing. Synchronic Interoperability, Model-driven Interoperability, Semantic-driven Interoperability, Vertical Interoperability, Horizontal Interoperability, Diachronic Interoperability Five levels: Political Context, Legal Interoperability, Organizational Interoperability, Semantic Interoperability, and Technical Interoperability. Five levels: (1) computer interoperability, (2) process interoperability, (3) knowledge interoperability, (4) value interoperability, and (5) goal interoperability. Slack Interoperability, Unregulated Interoperability, Standard-based Interoperability, Semantic Interoperability, Sustainable Interoperability Six levels: L6 (conceptual), L5 (dynamic), L4 (pragmatic), L3 (semantic), L2 (syntactic) and L1 (technical).

Organizational Interoperability Maturity Model [29] NATO Allied Data Publication 34 [37] IDEAS Interoperability Framework CEN/ISSS [28] Peristeras and Tarabanis [38] ATHENA Interoperability Framework [40]

Panetto [41] IDABC [7] Gottschalk [42] Agostinho and Jardim-Goncalves [27] Wang et al. [43]

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legitimate automated transactions that are also compatible with the internal business operation rules of each other; [S.A.4] Objects Interoperability: The networked interconnection of everyday objects contributing to the enterprise operation. [S.A.5] Software Interoperability: The ability of an enterprise software application to work seamlessly and in a sustainable way with other enterprise software application; [S.A.6] Cultural Interoperability: The degree to which knowledge and information is anchored to a unified model of meaning across cultures; [S.A.7] Knowledge Interoperability: The ability of two or more different entities to share their intellectual assets, take immediate advantage of the mutual knowledge and utilize it, and to further extend them through cooperation; [S.A.8] Services Interoperability: The ability of an entity to seamlessly and automatically discover, aggregate and utilize a service that belongs to another entity; [S.A.9] Social Networks Interoperability: The ability of enterprises to utilize social networks for collaborations and interconnection purposes, by aligning part of their internal structure and functions to the characteristics of the social networks; [S.A.10] Electronic Identity Interoperability: The ability of different eID systems to collaborate in order to automatically authenticate entities and to pass on security roles and permissions to eID holders, regardless the system that they originate from; [S.A.11] Cloud Interoperability: The ability of cloud services to be able to work together with both different cloud services and providers, and other applications or platforms that are not cloud dependent; [S.A.12] Ecosystems Interoperability: The ability of instant and seamless collaboration between different ecosystems, ecosystems and independent entities, entities within the ecosystems and the ability of different independent entities to formulate virtual structures for specific purposes.

5.2. Enterprise Interoperability positioning in the neighbouring scientific disciplines landscape In terms of content, any scientific domain exists in an ecosystem of neighbouring domains, and must recognize its relationship with these neighbours and with formal definitions of science bases already established for them. Normally, this relationship includes (adapted by [19]):  Boundaries between application fields, which may be fuzzy in the sense that there are some fields which, despite of being common to more than one scientific domain, could be addressed from the perspective of either domain.  Shared methodologies, techniques and tools that may be applicable to problems in more than one domain. Recognition of such sharing provides the opportunity for domains to advance by absorbing methodological and technical results from related disciplines.  Conflicts in approach may also exist, and present possible barriers to interdisciplinary research or application. Formal documentation of such conflict areas will reduce risk of failure in projects arising out of the application of incompatible approaches. Modern sciences introduce a paradigm shift since, unlike the traditional philosophy of science, they usually do not apply to a single domain, being interdisciplinary and eclectic. Modern sciences enlarged the neighbouring ecosystem searching their methods and raising research questions in broad areas, crossing borders and engineering different scientific fields. For example, the modern computer science embraces formalisms and algorithms

created to support particular desired behaviour using concepts from physics, chemistry, and biology [20,21]. Thus, being also a multi-disciplinary domain by nature, the establishment of Enterprise Interoperability (EI) scientific base should be developed comprising concepts and theories from related neighbouring sciences and scientific domains [13,36]. In order to maximize the impact and input from the neighbouring domains, the following 6 steps have been iteratively taken: (1) Neighbourhood Recognition including the identification and structuring of the relevant neighbouring domains, which enable ‘‘learning’’ and ‘‘extensibility’’ steps. (2) Learning – In the ‘‘learning’’ step, the neighbours are analyzed in terms both of formal methods for EI problems and solutions description and of results and principles that could be adopted by EI, as well. (3) Extensibility – The ‘‘extensibility’’ step evaluates the interdisciplinary nature of EI, by recognizing evidences of interoperability in the specific neighbouring scientific domains. (4) Conceptualization – Finally, having concluded both of them, ‘‘conceptualization’’ can formalize the definitions of contents and structure of the EISB framework, which will be instantiated through the more detailed, and EI focused Action Plan, refined each time this step is taken. (5) Emergence – Leads to emergence of a new science, i.e. EISB. However this is not a straightforward process thus it needs to be iterative with the previous steps. Social sciences, applied sciences and natural sciences are recognized in the general classification of scientific domains. In all of them, characteristics of interoperability are identified, and are considered to be promising contributors for the EISB formulation, thus leading to a simple glossary of potential neighbouring scientific domains – the EISB Scientific neighbouring Domain Reference Glossary (EISB-SDRG) divided into the following branches (adapted by [22]):  Natural sciences are branches of science that seek to elucidate the rules that govern the natural world by applying an empirical and scientific method to the study of the universe. Today, natural sciences are more commonly divided into life sciences, such as botany and zoology which seem somewhat distant from the EI domain; and physical sciences, which include physics or chemistry.  Social & behavioural sciences commonly used as an umbrella term to refer to a plurality of fields outside of the natural sciences. These include: anthropology, business administration, communication, economics, education, government, linguistics, international relations, political science and, in some contexts, geography, history, law, and psychology. However not all are identified as relevant neighbours of EI. Creative arts such as music or dancing can also be of interest and explored inside this domain. In fact, a lot of research has been developed concerning psychology of music and performance, performance sciences, etc.  Formal sciences are the branches of knowledge that are concerned with formal systems, such as logic, mathematics, theoretical computer science, information theory, systems theory, etc. Unlike other sciences, the formal sciences are not concerned with the validity of theories based on observations in the real world, but instead with the properties of formal systems based on definitions and rules, thus they are many times used to define other scientific domains.  Applied sciences – the application of scientific knowledge transferred into a physical environment [39]. Fields of engineering

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are closely related to applied sciences, which are very important for technology development. Its use in industrial settings is usually referred to as research and development (R&D) [34]. The classification of neighbouring scientific domains under these categories must be undertaken with the proper flexibility of any classification exercise. It was inspired by several classification structures such as: (1) the AMS mathematics subject classification [23]; (2) the ACM computing classification system [24]; (3) the European Science Foundation research areas [25]; and (4) the SCIENCE magazine subject collections [26]. The rationale for the selected scientific sub-domains and proposed glossary is related with the identification of evidences of interoperability issues/ problems/solutions in each of the scientific sub-domains foreseen [31]. For example, EI implies seamless communications within networked enterprise environments, which are complex systems, emerging in many forms and from different application domains, consisting of many facets whose proper understanding requires the contribution from multiple disciplines. This way, the scientific foundations related with the major EI research topics can be worked out based on the proposed EISB-SDRG and then connected with the results of the existing applied research, e.g. with the scientific domains of systems complexity, network science, information theory, or web science. Naturally the areas enumerated in the EISB-SDRG are far too large to be analyzed in detail (at the time this paper was written they were approximately 200), but with the criteria of estimated proximity to EI, the level of maturity and our awareness, Software Engineering, Complexity Science, Design Science and Services Science are considered as the nearest neighbours of EI. 6. Conclusions History has generally proved that when different scientific disciplines and perspectives come together, the outcome cannot be predicted. One discipline can become dominant and absorb parts of the others, or the overlapping pieces can break away and form a new field. But if the new field never becomes more than the sum of its parts, it can fade away over time. From time to time, though, a new and important discipline emerges as a synthetic combination. In the last decade, Enterprise Interoperability has been recognized as a well-acknowledged application domain in which substantial progress has been made through EU- and national funded research. However, the lack of scientific foundations seems to hinder unlocking its real value and full potential to all its stakeholders: from industry and SMEs to researchers. Taking into account that new scientific knowledge may lead to new applications, that new technological advances may lead to new scientific discoveries, and that potential applications actually motivate new scientific investigations, Enterprise Interoperability has the credentials to gradually evolve to a rigorous scientific discipline or sub-discipline. Time will, of course, eventually prove its actual positioning and real perspective in relation to its neighbouring scientific disciplines. In its quest for recognition, the key challenges that Enterprise Interoperability will have to face include, without being restricted to: substantiation of value, strong engagement and support by industry, sustainable research in the domain through appropriate curricula, and coordination of efforts undertaken by many stakeholders and neighbouring disciplines. As a first step in its scientific establishment, a Science Base for Enterprise Interoperability making explicit the knowledge and skills, which industry and academia have empirically observed, is now being initiated. Next steps along the proposed approach include continuation of work along the waves, indicatively as follows:

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 Sustainability and further expansion of the results achieved so far in accordance with the proposed Action Plan.  Targeted approach of high-calibre experts from neighbouring scientific disciplines in order to exchange ideas and knowledge on what can be used from their own perspective (learning phase) and where EI can be used for their own benefit (extensibility phase).  Analyzing and measuring the potential impact of a successful EISB initiative to the research, industry and SMEs.

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