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2020, Springer Science+Business Media, LLC, part of Springer Nature. Transport infrastructure systems are very complex and expensive. Projects to procure such systems are costly, long and complex to manage. The procurement context usually includes many collaborating organizations but often with different concerns and priorities, and many interactions to other parties. This makes the procurement very complex and entangled. DoDAF, MoDAF and TRAK are three architecture frameworks (AF) that model the whole enterprise/system life cycle that includes system procurement. They are expressed as metamodels. In this paper, we analyse various procurements strategies and identify the concerns that AFs should address. The TRAK AF is then applied to a Rail procurement case study in collaboration with Transport for New South Wales (NSW) in Australia to assess its effectiveness in meeting the procurement needs. In all stages of the study, TRAK is mapped and compared to DoDAF and MoDAF to examine whether DoDAF or MoDAF can cover the inadequacies of TRAK. This paper shows that there is a considerable number of procurement needs which are overlooked by these architecture frameworks. It proposes a metamodel driven expansion to these frameworks to improve their completeness and expressiveness.

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

Model-driven approaches are widely used in managing the complex domains such as infrastructure systems or disaster management. The foundation of conducting a systematic research is designing a methodology that pertinently covers the steps of research from problem definition to solution proposal and then identifying or tailoring a method for developing and validating the solution. This paper explains the application of Design Science for conducting a research which aims at providing a model-driven approach for addressing the complexities of infrastructure procurement projects. So firstly the design science artefacts are adopted for designing the method for this research. Then the steps of this method are explained briefly along with description of how each step is applied in this research. The core of this method is proposing a process for developing and validating the metamodels which is designed based on combination of other metamodeling processes.

With the development of data mining and machine learning techniques, data-driven based technology-enhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the 'big data' era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

The procurement of infrastructure systems by the public sector is very costly, long and not transparent since the processes are based on preparing huge amounts of documents which are difficult to be kept consistent and to be used (e.g. bid evaluation). Acquisition architecture frameworks (AF) are metamodels, developed to model the whole enterprise/system life cycle stages including system procurement. Our previous study analyzed the currently used AFs such as DoDAF, MoDAF and TRAK to assess their adequacy and efficiency in modelling the infrastructure projects. The results showed that many of the procurement related concerns are overlooked such as financial matters e.g. cost and revenue calculation; and risk management aspects e.g. risk calculation and risk allocation. This paper focuses on identifying the procurement concerns and adding new viewpoints to the architecture frameworks; and developing a domain specific language based on SysML to model the new viewpoints. A methodology is provided which shows how the metamodel (abstract syntax) and the language stereotypes (concrete syntax) are developed. The results firstly show the 18 identified viewpoints of procurement domain and then one of them (project funding) is chosen to be described in this paper. The conceptual definition of the 'project funding' viewpoint and the models it generates are illustrated as example diagrams of this DSL. This DSL can be used by the domain practitioners, who are the contracting officers and procurement managers, to generate the contracting materials to facilitate the contracting process, assure the consistency of the procurement documents, giving better project outcomes.

Moving mission-oriented enterprise software applications to cloud environments is a crucial IT task and requires a systematic approach. The foci of this paper is to provide a detailed review of extant cloud migration approaches from the perspective of the process model. To this aim, an evaluation framework is proposed and used to appraise and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the status quo and makes a rich inventory of important activities, recommendations, techniques, and concerns that are common in a typical cloud migration process in one place. This enables both academia and practitioners in the cloud computing community to get an overarching view of the process of the legacy application migration to the cloud. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavours.

Moving mission-oriented enterprise software applications to cloud environments is a crucial IT task and requires a systematic approach. The foci of this paper is to provide a detailed review of extant cloud migration approaches from the perspective of the process model. To this aim, an evaluation framework is proposed and used to appraise and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the status quo and makes a rich inventory of important activities, recommendations, techniques, and concerns that are common in a typical cloud migration process in one place. This enables both academia and practitioners in the cloud computing community to get an overarching view of the process of the legacy application migration to the cloud. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavours.

Moving mission-oriented enterprise software applications to cloud environments is a crucial IT task and requires a systematic approach. The foci of this paper is to provide a detailed review of extant cloud migration approaches from the perspective of the process model. To this aim, an evaluation framework is proposed and used to appraise and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the status quo and makes a rich inventory of important activities, recommendations, techniques, and concerns that are common in a typical cloud migration process in one place. This enables both academia and practitioners in the cloud computing community to get an overarching view of the process of the legacy application migration to the cloud. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavours.

Moving mission-oriented enterprise software applications to cloud environments is a crucial IT task and requires a systematic approach. The foci of this paper is to provide a detailed review of extant cloud migration approaches from the perspective of the process model. To this aim, an evaluation framework is proposed and used to appraise and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the status quo and makes a rich inventory of important activities, recommendations, techniques, and concerns that are common in a typical cloud migration process in one place. This enables both academia and practitioners in the cloud computing community to get an overarching view of the process of the legacy application migration to the cloud. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavours.

It is found that learners prefer to use micro learning mode to conduct learning activities through open educational resources (OERs). However, adaptive micro learning is scarcely supported by current OER platforms. In this paper we focus on profiling an effective micro learning process which is central to establish the raw materials and set up rules for the final adaptive process. This work consists of two parts. First, we conducted an educational data mining and learning analysis study to discover the patterns and rules in micro learning through OER. Then based on its findings, we profiled features of both learners and OERs to reveal the full learning story in order to support the decision making process. Incorporating educational data mining and learning analysis, an cloud-based architecture for Micro Learning as a Service (MLaaS) was designed to integrate all necessary procedures together as a complete service for delivering micro OERs. The MLaaS also provides a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, was also introduced.

Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.

Australia has a large number of health program initiatives whose comprehensive assessment will help refine and redefine priorities by highlighting areas of emphasis, under-emphasis, and non-emphasis. The objectives of our research are to: (a) systematically map all the programs onto an ontological framework, and (b) systemically analyse their relative emphases at different levels of granularity. We mapped all the health program initiatives onto an ontology with five dimensions, namely: (a) Policy-scope, (b) Policy-focus, (c) Outcomes, (d) Type of care, and (e) Population served. Each dimension is expanded into a taxonomy of its constituent elements. Each combination of elements from the five dimensions is a possible policy initiative component. There are 30,030 possible components encapsulated in the ontology. It includes, for example: (a) National financial policies on accessibility of preventive care for family, and (b) Local-urban regulatory policies on cost of palliative care for individual-aged. Four of the authors mapped all of Australia's health programs and initiatives on to the ontology. Visualizations of the data are used to highlight the relative emphases in the program initiatives. The dominant emphasis of the program initiatives is: [National] [educational, personnel-physician, information] policies on [accessibility, quality] of [preventive, wellness] care for the [community]. However, although (a) information is emphasized technology is not; and (b) accessibility and quality are emphasized cost, satisfaction, and quality are not. The ontology and the results of the mapping can help systematically reassess and redirect the relative emphases of the programs and initiatives from a systemic perspective.

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES (State Emergency Service), an authoritative DM agency in NSW (New State Wales) State of Australia to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW.

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES (State Emergency Service), an authoritative DM agency in NSW (New State Wales) State of Australia to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW.

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES (State Emergency Service), an authoritative DM agency in NSW (New State Wales) State of Australia to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW.

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES (State Emergency Service), an authoritative DM agency in NSW (New State Wales) State of Australia to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW.

Informal interactions with clients are prone to errors in interpretation and communication which can later result in errors in the ensuing requirements models. Resolving such errors early can prevent rework or the building of a system that is non-compliant with the client's intention. In developing complex systems and highly interactive systems, such as agent oriented systems, those errors in requirement analysis are even more likely. By focussing on the quality of interactions between the future users of the system and the human modellers, this article presents an innovative validation and verification process to support analysis of requirement analysis. We use domain knowledge to validate compliance with the client's intention. Using layered abstractions, we formulate the validation process which is methodology independent. An interactive support tool is implemented to support the developers using the requirement analysis process. The process is conducted iteratively to accommodate the lifecycle defined by most methodologies. The process and the tool are validated and illustrated using two different development exemplars illustrating different domains and different development methodologies.

Micro learning is gradually becoming a common learning mode in massive open online course learning (MOOC). We illustrate a research strategy to formalize and customize micro learning resources in order to meet personal demands at the real time. This smart micro learning environment can be organized by a Software as a Service (SaaS) we newly designed, in which educational data mining technique is mainly employed to understand learners learning behaviors and recognize learning resource features in order to identify potential micro learning solutions. A learner model with regards to internal and external factors is also proposed for personalization in micro MOOC learning context.

Informal interactions with clients are prone to errors in interpretation and communication which can later result in errors in the ensuing requirements models. Resolving such errors early can prevent rework or the building of a system that is non-compliant with the client's intention. In developing complex systems and highly interactive systems, such as agent oriented systems, those errors in requirement analysis are even more likely. By focussing on the quality of interactions between the future users of the system and the human modellers, this article presents an innovative validation and verification process to support analysis of requirement analysis. We use domain knowledge to validate compliance with the client's intention. Using layered abstractions, we formulate the validation process which is methodology independent. An interactive support tool is implemented to support the developers using the requirement analysis process. The process is conducted iteratively to accommodate the lifecycle defined by most methodologies. The process and the tool are validated and illustrated using two different development exemplars illustrating different domains and different development methodologies.

Recommender systems are modern applications that make suggestions to their users on a variety of items taking into account their preferences in many domains. These systems use people's opinions to recommend to their end users items that are likely to be of their interest. They are designed to help users to decide on appropriate items and facilitate finding them in a very large collection of items. Traditional syntactic-based recommender systems suffer from several disadvantages, such as polysemy or synonymy, that limit its effectiveness. Semantic technologies provide a consistent and reliable basis for dealing with data at knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a recommender system based on a Music ontology is presented. A preliminary evaluation of the system shows promising results.

Ontologies can provide many benefits during information systems development. They can provide domain knowledge to requirement engineers, are reusable software components for web applications or intelligent agent developers, and can facilitate semi-automatic model verification and validation. They also assist in software extensibility, interoperability and reuse. All these benefits critically depend on the provision of a suitable ontology (ies). This paper introduces a semantically-based three stage-approach to assist developers in checking the consistency of the requirements models and choose the most suitable and relevant ontology (ies) for their development project from a given repository. The early requirements models, documented using the i* language, are converted to a retrieval ontology. The consistency of this retrieval ontology is then checked before being used to identify a set of reusable ontologies that are relevant for the development project. The paper also provides an initial validation of each of the stages. 2014 Elsevier Ltd. All rights reserved.

Real-time tasks need attributes for monitoring their execution and performing recovery actions in case of failures. Temporal constraints are a class of real-time task attributes where the constraints relate the status of the task to temporal entities. Violating temporal constraints can produce consequences of unknown severity. This paper is part of our on-going research on real-time multi agent systems constraints. We discuss the importance of temporal constraints and present a task model that explicitly represents temporal constraints. We also present our preliminary results from our initial implementation in the domain of Meeting Schedules Management involving multiple users assisted by agents.

In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement in data-intensive service provision, the ant-inspired negotiation mechanism is considered in this paper. There are twostage negotiation procedures in our model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied for selecting the services. The experimental results show that our ant-inspired negotiation approach can facilitate the data-intensive service provision. Index Terms-Ant colony system, data-intensive service composition, automated negotiation, quality of service.

Real-time tasks need attributes for monitoring their execution and performing recovery actions in case of failures. Temporal constraints are a class of real-time task attributes where the constraints relate the status of the task to temporal entities. Violating temporal constraints can produce consequences of unknown severity. This paper is part of our on-going research on real-time multi agent systems constraints. We discuss the importance of temporal constraints and present a task model that explicitly represents temporal constraints. We also present our preliminary results from our initial implementation in the domain of Meeting Schedules Management involving multiple users assisted by agents.

Ontologies can provide many benefits during information systems development. They can provide domain knowledge to requirement engineers, are reusable software components for web applications or intelligent agent developers, and can facilitate semi-automatic model verification and validation. They also assist in software extensibility, interoperability and reuse. All these benefits critically depend on the provision of a suitable ontology (ies). This paper introduces a semantically-based three stage-approach to assist developers in checking the consistency of the requirements models and choose the most suitable and relevant ontology (ies) for their development project from a given repository. The early requirements models, documented using the i* language, are converted to a retrieval ontology. The consistency of this retrieval ontology is then checked before being used to identify a set of reusable ontologies that are relevant for the development project. The paper also provides an initial validation of each of the stages. 2014 Elsevier Ltd. All rights reserved.

In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement in data-intensive service provision, the ant-inspired negotiation mechanism is considered in this paper. There are twostage negotiation procedures in our model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied for selecting the services. The experimental results show that our ant-inspired negotiation approach can facilitate the data-intensive service provision. Index Terms-Ant colony system, data-intensive service composition, automated negotiation, quality of service.

Real-time tasks need attributes for monitoring their execution and performing recovery actions in case of failures. Temporal constraints are a class of real-time task attributes where the constraints relate the status of the task to temporal entities. Violating temporal constraints can produce consequences of unknown severity. This paper is part of our on-going research on real-time multi agent systems constraints. We discuss the importance of temporal constraints and present a task model that explicitly represents temporal constraints. We also present our preliminary results from our initial implementation in the domain of Meeting Schedules Management involving multiple users assisted by agents.

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement in data-intensive service provision, the ant-inspired negotiation mechanism is considered in this paper. There are twostage negotiation procedures in our model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied for selecting the services. The experimental results show that our ant-inspired negotiation approach can facilitate the data-intensive service provision. Index Terms-Ant colony system, data-intensive service composition, automated negotiation, quality of service.

Ontologies can provide many benefits during information systems development. They can provide domain knowledge to requirement engineers, are reusable software components for web applications or intelligent agent developers, and can facilitate semi-automatic model verification and validation. They also assist in software extensibility, interoperability and reuse. All these benefits critically depend on the provision of a suitable ontology (ies). This paper introduces a semantically-based three stage-approach to assist developers in checking the consistency of the requirements models and choose the most suitable and relevant ontology (ies) for their development project from a given repository. The early requirements models, documented using the i* language, are converted to a retrieval ontology. The consistency of this retrieval ontology is then checked before being used to identify a set of reusable ontologies that are relevant for the development project. The paper also provides an initial validation of each of the stages. 2014 Elsevier Ltd. All rights reserved.

In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

Ontologies can provide many benefits during information systems development. They can provide domain knowledge to requirement engineers, are reusable software components for web applications or intelligent agent developers, and can facilitate semi-automatic model verification and validation. They also assist in software extensibility, interoperability and reuse. All these benefits critically depend on the provision of a suitable ontology (ies). This paper introduces a semantically-based three stage-approach to assist developers in checking the consistency of the requirements models and choose the most suitable and relevant ontology (ies) for their development project from a given repository. The early requirements models, documented using the i* language, are converted to a retrieval ontology. The consistency of this retrieval ontology is then checked before being used to identify a set of reusable ontologies that are relevant for the development project. The paper also provides an initial validation of each of the stages. 2014 Elsevier Ltd. All rights reserved.

Real-time tasks need attributes for monitoring their execution and performing recovery actions in case of failures. Temporal constraints are a class of real-time task attributes where the constraints relate the status of the task to temporal entities. Violating temporal constraints can produce consequences of unknown severity. This paper is part of our on-going research on real-time multi agent systems constraints. We discuss the importance of temporal constraints and present a task model that explicitly represents temporal constraints. We also present our preliminary results from our initial implementation in the domain of Meeting Schedules Management involving multiple users assisted by agents.

In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach.

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement in data-intensive service provision, the ant-inspired negotiation mechanism is considered in this paper. There are twostage negotiation procedures in our model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied for selecting the services. The experimental results show that our ant-inspired negotiation approach can facilitate the data-intensive service provision. Index Terms-Ant colony system, data-intensive service composition, automated negotiation, quality of service.

Australian government enterprises have shown a significant interest in the cloud technology-enabled enterprise transformation. Australian government suggests the whole-of-a-government strategy to cloud adoption. The challenge is how best to realise this cloud adoption strategy for the cloud technology-enabled enterprise transformation? The cloud adoption strategy realisation requires concrete guidelines and a comprehensive practical framework. This paper proposes the use of an agile enterprise architecture framework to developing and implementing the adaptive cloud technology-enabled enterprise architecture in the Australian government context. The results of this paper indicate that a holistic strategic agile enterprise architecture approach seems appropriate to support the strategic whole-of-a-government approach to cloud technology-enabled government enterprise transformation.

This paper argues for placing ontologies at the centre of the software development life cycle for distributed component-based systems and, in particular, for service-oriented systems. It presents an ontology-based development process which relies on three levels of abstraction using ontologies: architecture layer, application layer and domain layer. The paper discusses the key roles of ontologies with respect to the various abstraction layers and their corresponding impact on the concomitant workproducts. In addition, a peer-to-peer-based service selecting and composing tool is suggested as a way of supporting the process. The paper presents the architecture of the proposed tool and illustrates the whole process in the development of a mobile banking application based on dynamic Web services.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

Agents are promising software building blocks to create open information and distributed systems. Towards enhancing trust in them, we propose making them security aware. We suggest extending existing modelling languages used for the development of agent based systems to enable representation of agent security awareness. We present a generic analysis of agent security for identifying internal representational concepts required for agents to be able to manage their own security at runtime. The analysis, from both the agent and the system perspectives, provides a generic security language extension which is domain independent. The use of this language extension is illustrated in identifying security requirements of a distributed agent system application for semantic-based components sharing.

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system's performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system-the cost of keeping a domain expert on-line-is reduced. © 2012 Springer-Verlag London Limited.

Context: A common distributed intelligent system architecture is Multi Agent Systems (MASs). Creating systems with this architecture has been recently supported by Agent Oriented Software Engineering (AOSE) methodologies. But two questions remain: how do we determine the suitability of a MAS implementation for a particular problem? And can this be determined without AOSE expertise? Objective: Given the relatively small number of software engineers that are AOSE experts, many problems that could be better solved with a MAS system are solved using more commonly known but not necessarily as suitable development approaches (e.g. object-oriented). The paper aims to empower software engineers, who are not necessarily AOSE experts, in deciding whether or not they should advocate the use of an MAS technology for a given project. Method: The paper will construct a systematic framework to identify key criteria in a problem requirement definition to assess the suitability of a MAS solution. The criteria are first identified using an iterative process. The features are initially identified from MAS implementations, and then validated against related work. This is followed by a statistical analysis of 25 problems that characterise agent-oriented solutions previously developed to group features into key criteria. Results: Key criteria were sufficiently prominent using factor analysis to construct a framework which provides a process that identifies within the requirements the criteria discovered. This framework is then evaluated for assessing suitability of a MAS architecture, by non-AOSE experts, on two real world problems: an electricity market simulation and a financial accounting system. Conclusion: Substituting a software engineer's personal inclination to (or not to) use a MAS, our framework provides an objective mechanism. It can supplant current practices where the decision to use a MAS architecture for a given problem remains an informal process. It was successfully illu

Predictions for Service Oriented Architectures (SOA) to deliver transformational results to the role and capabilities of IT for businesses have fallen short. Unforseen challenges have often emerged in SOA adoption. They have fallen into two categories: technical issues stemming from service components reuse difficulties and organisational issues stemming from inadequate support or understanding of what is required from the executive management in an organisation to facilitate the technical rollout. This special issue of IJIIT presents a collection of papers which attempt to resolve some of these issues. This paper first explores and analyses the hindrances to the full exploitation of SOA.

Many information systems developers face high cost in adopting service oriented architectures because of the high cost of locating appropriate services to customise and integrate into their system. This paper aims at reducing this cost by automating much of the composition and service selection effort. It illustrates the use of a Peer-Peer multi agent system (MAS) to facilitate service selection with multiple Quality of Service properties. The system will use semantic enrichment of services in order to facilitate their identification and composition. With semantic driven composition, services can be shared between teams of developers and across multiple organisations connected via the Internet. In this paper, we focus on a conceptual framework for peer selection with a preliminary mathematical model and a selection process, so as to enhance the P2P-based service coordination system and its components.

Metamodelling produces a 'metamodel' capable of generalizing the domain. A metamodel gathers all domain concepts and their relationships. It enables partitioning a domain problem into sub-problems. Decision makers can then develop a variety of domain solutions models based on mixing and matching solutions for sub-problems indentified using the metamodel. A repository of domain knowledge structured using the metamodel would allow the transformation of models generated from a higher level to a lower level according to scope of the problem on hand. In this paper, we reveal how a process of mixing and matching disaster management actions can be accomplished using our Disaster Management Metamodel (DMM). The paper describes DM model transformations underpinned by DMM. They are illustrated benefiting DM users creating appropriate DM solution models from extant partial solutions.