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oday’s business processes are increasingly complex as they cross organizational boundaries. To execute their business processes, organizations develop software applications called Process-Aware Information System (PAIS). PAIS designers must consider complex scenarios involving multiple partners. Consequently, the architectural design of high quality PAIS is complex and requires vast amounts of knowledge and skills both in software architecture and in the business domain. This paper proposes a model-driven method to design the architecture of PAIS using the service-oriented architecture (SOA) style. The proposed method generates SOA-based design models expressed in SoaML from the specifications of collaborative business processes expressed in BPMN.We developed a prototype tool using the Eclipse Modeling Framework (EMF) ecosystem. We tested the method on a set of processes from the Enterprise Resource Planning literature to assess its effectiveness. Our results show that 80.95% of the identified services were relevant and corresponded to what architecture specialists expected.

In the current world, online retailing accounts for the major share of retailing due to the unique benefits it provides to customers. However, its success is highly dependent on how it manages its operations. Compared to traditional retailing, online retailing requires new decision-making models to support its operations. In this thesis, the E-SMART framework is proposed which assists e-tailers in operations management in the three areas of the sourcing, distribution, and personalization of an assortment. The E-SMART framework consists of three modules. The first module manages sourcing decisions and develops a framework to assist the e-tailer to decide if it should interact with a powerful main brand supplier or instead, procure items from an alternate supplier. The framework determines the best strategy, taking into consideration selling price, assortment size and the inventory level of the item to be procured. It also considers the importance of considering customers as shopping basket consumers in sourcing decisions in different scenarios. The second module manages distribution decisions and develops a decision-making model to determine the most profitable strategy from drop shipping and batch ordering. The interaction of the supplier and the e-tailer is modeled using the game-theory approach. Another important aspect that needs to be considered carefully in the drop-shipping model is the profit-sharing ratio between the supplier and the e-tailer. By analyzing different scenarios, the model shows which strategy is more profitable for the e-tailer. The third module of the E-SMART framework assists the e-tailer in dynamic assortment personalization to improve its sales. The problem is modeled as a Markov decision process to mitigate customers' churn rate in the case of demand uncertainty and limited inventory. Customer choice and customer lifetime are modeled using the multinomial logit model and survival analysis techniques respectively, and are estimated using h

Innovation is an economic force and an essential organisational function in need of its management. ISO56002: Innovation Management System Standard (IMSS) is a new global standard that provides guidelines to manage innovation. Within the innovation process, its Fuzzy Front-end (FFE) is fluid and complex yet essential for the success of innovation. Start-ups, are essential within the entrepreneurial economy and rely on both innovativeness and their leanness. With the fluidity of FFE and desired leanness of start-ups as its backdrop, the thesis assesses the applicability of IMSS in managing FFE of innovation for start-ups.This is done using two variables, one measuring the perception of CEOs/founders of selected start-ups of the applicability of the IMSS and the second measuring the actual use of IMSS by start-ups. A representation of FFE in IMSS, which does not exist, was developed, Structured interviews were used to collect data which was then analysed to comment on the applicability of the IMSS in this context. This thesis found that IMSS was perceived to be applicable and was mostly used by start-ups in managing FFE. The thesis also found that the applicability of IMSS varied depending on the maturity of the start-ups. The thesis found the perception of applicability to be higher than the actual use of IMSS. The thesis contributes by providing an early insight to start-up founders/CEOs, consultants and practitioners on IMSS, its applicability and adoption. The thesis also provides a representation of FFE within IMSS. Contributions from the thesis may also be used as a foundation for future research.

Communication is an indispensable aspect in the success of any business. Due to the increase in digital innovation, Internet-based services such as chatbots now play a vital role in maintaining communication between users. However, a traditional chatbot’s dialogue capability is quite inflexible as It can answer the user only if there is pattern-matching between the set of questions-answers and user’s query. This may leave the customers unhappy and research has shown that 91% of unhappy customers tend not to engage with the business again. To address this, chatbots needs to have meaningful dialogue abilities rather than merely providing either a yes, no or a short response. The major challenge in building a better AI model is ensuring it has a domain-specific conversational capability to engage with the user while presenting meaningful responses and semantically correct information.The existing literature has explored the capabilities of advanced techniques such as recurrent neural networks (DBRNN) for chatbots to engage in human-like conversation and generate responses. However, while an enormous amount of research has been done to bring this idea to realization, no significant outcome in the area of engaging with the users while generating a response is achieved to date. To address this problem, in this thesis, an innovative framework architecture, named IntelliBot, is designed and developed. IntelliBot is a chatbot which to facilitate a high degree of engagement with the user using the seq2seq model when generating its response with the user. Additionally, it not only has the ability to answer a question, but also complex user queries with a semantically correct meaningful response and solves user queries specifically in the insurance domain. To meet these challenges, IntelliBot generates a response in four distinct ways, namely, template-based strategy, knowledge-based strategy, internet retrieval strategy and generative-based strategy. An AI selection process is

The purpose of this study is to examine buying centre members’ information control in the context of complex organisational buying. Information control is the buying centre members’ ability and inclination to control disposition of information, and this construct development is mainly based in the gatekeeping theory. The effects of information control remain unclear in the current literature, so it is not clear whether the consequences of information control are positive or negative towards organisational buying decisions. Thus, the study investigates two consequences of buying centre members’ information control: requirements uncertainty and procurement performance. Also, there is limited discussion on antecedents behind information control, and effects of power bases on information control have not been properly investigated. Thus, the study also investigates the effects of expert power, legitimate power, and network tie strength as antecedents of buying centre members’ information control.The data was collected through a cross-sectional survey of 294 buying centre members active in complex procurement. The data analyses were conducted in AMOS version 24 and involved confirmatory factor analysis followed by structural equation modelling.Results confirm buying centre members’ information control has a significant influence on the complex organisational buying and findings elaborate that information control has positive outcomes to the overall procurement performance. Further, it is proven that expert and legitimate power are major antecedents of information control.These findings elaborate a new perspective different from the traditional understanding of sharing and disseminating of more information is the best approach to make better decisions. The results indicate that deliberate information sharing or smart information controlling of decision-makers can lead to better decision-making. Further, the findings have the potential to inform managers in larger-scale, c

Risk management is paramount for managing risks in supply chains. Although supply chain risk management involves identifying, assessing and predicting uncertain events, no particular strategy has been developed that considers both internal and external risk activities related to an organisation while managing it. This PhD thesis focuses on developing an intelligent decision support system that can identify essential risk labels related to operations, the features of service level agreement related to them and estimate risk levels based on analysing both the internal and external risk events in supply chains. This thesis provides the following three significant contributions to research on developing an intelligent decision support system for risk management in different supply chains.The first major contribution is the collection and analysis of the supply chain data obtained from different firms, with historical data and expert views used to determine the risk labels and evaluate the significance of features and their relation to the risk labels. More importantly, information from service level agreements and experts are used to create data sources for training and validating association rule mining and fuzzy inference techniques. The second significant contribution is that the critical features of risk labels are selected from data sources using emerging association patterns, which is integrated with association rule mining to determine the information related to the risk labels. The results show that this mechanism is better than other approaches for predicting risk labels from the data sources of supply chains. The third key contribution is the development of a fuzzy inference decision support system that can quantify the occurrence of risk labels based on the features selected using the emerging association patterns mechanism. The results obtained from its application on the collected data show that it outperforms existing techniques as it can accurately measur

Milk and dairy products are considered healthy and nutrient-rich foods that providea wide range of benefits to the human body. To reap such benefits, it is essentialfor raw milk quality to be maintained at the highest possible level from the pointof its extraction at the dairy farm until it reaches the processor. However, thecurrent existing real-world practices adopted by the dairy supply chain’s upstreamstakeholders of farmers and transporters do not guarantee that the maintainedquality of milk is at the highest possible level it can be. This is because thereare several critical gaps in the existing literature which results in the stakeholdersadopting a reactive rather than a proactive approach in the quality management ofraw milk. In this thesis, these critical gaps are identified and it is argued that if theobjective of these dairy supply chain stakeholders is to maximize their expectedbenefits, they need to ensure that they provide milk at the highest possible levelof quality it can be. To do this, they require next-generation artificial intelligence basedautomated systems that will assist them in taking appropriate steps to maximizemilk quality. In this thesis, such a comprehensive next generation frameworkfor proactively managing milk quality (PROTEKT) is developed and presented.The proposed PROTEKT framework empowers the farmers and transporters tomaximise their expected benefit from the extracted milk and make them accountablefor their actions to maintain milk quality up to its highest possible level. PROTEKThas four modules, namely; Event detection, Monitoring and measurement, Allocationand Validation. Each module has various definitions, phases, methods and metricsthat contribute to achieving the overall objective of the framework. The workingof each module is explained in detail along with its working on the real-worlddata acquired from an Australian dairy farm to demonstrate its applicability andvalidity in a real-world setting.

In these competitive times, designers need to constantly update their tangible products with new features and functionalities to retain market share. This is especially important for designers of emerging products, as customer expectations of them change more frequently. New Product Development (NPD) is an established process which assists product designers in achieving this aim. In the first step of this process, product designers find novel features for upgrading their products (idea generation) and select some ideas (idea selection) for developing a new product concept in the next steps of NPD process. This step plays a vital role in introducing newness into a product and consequently its success in the market. A recent trend in existing literature for generating novel ideas is transfer learning where ideas for improvements are inspired from products in other domains. However, they are heavy/process-centric and time-intensive for the designers of emerging products in finding novel ideas from products of other domains. Moreover, while enormous research has been done to accomplish the idea selection task, there are still no agreed evaluation metrics for the product designers to prioritize their ideas. To address this problem in this thesis a framework, namely FEATURE, is developed which assists product designers to accomplish idea generation and idea selection to enhance emerging products. FEATURE adopts the science and engineering research approach, and it consists of three modules, namely Novel Feature Finder (NFF), Feature Sentiment Analyser (FSA) and Targeted Feature Recommender (TFR). NFF generates ideas by searching the products of cross-domains for their novel features that can be integrated into the product to be improved. To diminish the risk of product failure, FSA then ascertains the popularity of the generated ideas by using the customers’ reviews in social media. Finally, TFR prioritizes ideas based on different decision-criteria. From an industry-appl

Contact centres (CCs) are one of the main touch points by which customers can contact an organization. They are also one of the sources of input to Customer Relationship Management (CRM) systems that enable an organization to better manage their relationship with their customers. In today’s competitive world, it is important that organizations have efficient CRM systems to ensure that the loyalties of their customers remain with them. As an indication of their importance, in the literature, various predictive and innovative CRM approaches have been proposed. However, for the better management of their operations, apart from only focusing on the satisfaction and loyalty of customers, organizations should also focus on enhancing and maintaining the satisfaction of their Customer Service Representatives (CSRs), who are the front line staff working in CCs. For this, they need to develop innovative tools that will assist the CSRs in doing their job efficiently, thereby increasing their work satisfaction. In spite of recognizing the importance of such systems in the literature, no formal approaches have been proposed that help to achieve these aims. This is in contrast to the numerous predictive and innovative CRM approaches proposed that focus on increasing the satisfaction of both existing and new customers. Thus, to address this gap, this thesis proposes innovative systems that assist CSRs to undertake their tasks efficiently. This will also lead to an improved and positive customer experience as a result of their interaction with the CSRs. In order to address the problem and realize these benefits, the Cleansed Interactive Customer Recognition Framework (CICRF) is proposed. CICRF has two broad aims, first is the deduplication of the CC's database (DB) using a hybrid technique of crowdsourcing and machine learning and the second is the development of an interactive framework for customer recognition in CCs using hybrid heuristic classical techniques. For each aim of CI

Connected autonomous vehicles (CAV) technologies are about to be in the market in the near future. This requires transportation facilities ready to operate in a mixed traffic environment where a portion of vehicles are CAVs and the remaining are manual vehicles. Since CAVs are able to run with less spacing and headway compared with manual vehicles or mixed traffic, allocating a number of freeway lanes exclusive to CAVs may improve the overall performance of freeways. In this paper, we propose an analytical managed lane model to evaluate the freeway flow in mixed traffic and to determine the optimal number of lanes to be allocated to CAVs. The proposed model is investigated in two different operation environments: single-lane and managed lane environments. We further define three different CAV technology scenarios: neutral, conservative, and aggressive. In the single-lane problem, the influence of CAV penetration rates on mixed traffic capacity is examined in each scenario. In the managed lanes problem, we propose a method to determine the optimal number of dedicated lanes for CAVs under different settings. A number of numerical examples with different geometries and demand levels are investigated for all three scenarios. A sensitivity analysis on the penetration rates is conducted. The results show that more aggressive CAV technologies need less specific allocated lanes because they can follow the vehicles with less time and space headways.

Smart grids have created an emerging entity of 'prosumer' in the energy value network who not only consumes energy but also generates and shares the green energy with the utility grid. Hence, effective management of prosumers has become pivotal to ensure a long-term, sustainable energy-sharing process. Recently, the concept of a Prosumer Community Group (PCG) has emerged as one of the most promising and effective ways to manage prosumers. However, developing sustainable PCGs is challenging. One of the key challenges in this regard is to assess the contribution made by individual prosumers of a PCG, and find a subset of the most influential prosumers whose behavior would facilitate the long-term sustainability of the PCG. In this paper, we have focused on this challenge and proposed an innovative methodology to assess and rank the prosumers, in order to build an influential membership base. We have assessed the long-term and short-term energy behaviors of prosumers based on multiple evaluation criteria and accordingly decided the ranks of the prosumers, whereby the higher ranked prosumers are deemed to be more influential in enhancing the long-term sustenance of the PCG. Furthermore, we have presented simulation results to verify our proposed methodology. The current literature on smart-grid research field has no work investigating this challenge, making our contribution novel.

In this study, an analysis was conducted for the relationships between the main components of customer relationship management (CRM) and customer complaints in the domain of logistics and transport. Today, complaints and the handling of complaints play a pivotal role in customer relationships. Moreover, companies are reluctant to admit that they have difficulties with customers’ complaints, but as yet there appears to be no complete solution to this issue. To remedy this situation, customer complaints must be comprehensively collected and analysed. Issues must be classified, and timely solutions must be developed. In this paper, a conceptual framework is proposed including mathematical models, hypothesised relationships, perceived value and interactivity between customer, business and the system, as well as customer satisfaction analytics. The framework will address the relationship between customer satisfaction issues, loyalty and customer acquisition and estimate customer satisfaction and loyalty. For the purpose of analysis, this study uses both qualitative and quantitative approaches. For data collection, a survey questionnaire was distributed to 60 Fremantle Port logistics and transport customers. For the quantitative approach, linear and nonlinear modelling is adopted. Using the model, we are able to address the shortcomings of CRM technology, and tackle the issues of loyalty improvement and customer acquisition. Finally, based on nonlinear modelling and using a fuzzy inference system, namely the Takagi–Sugeno-type approach, we defined fuzzy rules, by means of which we ascertain the relationship between customer satisfaction and the main relevant variables.

Energy demand is continually rising, worldwide. Most of the current demand is met by non-renewable energy sources, like coal, petroleum, and natural gas. However, currently, society is faced with the problem of dwindling and scarce non-renewable energy resources, resulting in a shortage of energy. Moreover, the process of energy production from non-renewable sources is increasing greenhouse gas emissions, leading to unpleasant and potentially dangerous climatic changes. Therefore, in today's world, the focus is on inducing users to reduce their household energy consumption, and shift to using energy produced from renewable sources, such as solar, water, and wind. Not only this, but users are being encouraged to generate the green energy, and to either store the surplus for future usage or to feed it back into the utility grid. In order to evolve such bidirectional energy and information flow, the concept of the smart-grid has been proposed.

In the recent past, there has been an exponential growth in Resource Description Framework data on the web known as web of data. The emergence of the web of data is transforming the existing web from a document-sharing medium to a decentralized knowledge platform for publishing and sharing information between humans and computers. To enable common understanding between different users, domain ontologies are being developed and deployed to annotate information on the web. This semantically annotated information is then accessed by machines to extract and aggregate information, on the basis of the underlying ontologies used. To effectively and efficiently access data on the web, insight into the usage of ontology is pivotal, because this assists users in experiencing the benefits offered by the Semantic Web. However, such an approach has not been proposed in the literature. In this paper, we present a pragmatic approach to the analysis of domain ontology usage on the web. We propose metrics to measure the use of domain ontology constructs on the web from different aspects. To comprehensively understand the usage patterns of conceptual knowledge, instance data, and ontology co-usability, we considered GoodRelations ontology as the domain ontology and built a dataset by collecting structured data from 211 web-based data sources that have published information using the domain ontology. The dataset is analyzed by using the proposed metrics and observations along with their usability and applicability to the different users of the Semantic.

The growing number of cloud services has made service selection a challenging decision-making problem by offering wide ranging choices for cloud service consumers. This necessitates the use of formal decision making methodologies to assist a decision maker in selecting the service that best fulfills the user’s requirements. In this paper, we present a cloud service selection methodology that utilizes quality of service history of cloud services over different time periods and performs parallel multi-criteria decision analysis to rank all cloud services in each time period in accordance with user preferences before aggregating the results to determine the overall rank of all the available options for cloud service selection. This methodology assists the cloud service user to select the best possible available service according to the requirements. The multi-criteria decision making processes used for each time period are independent of the other time periods and are executed in parallel.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy-user and the utility grid, allowing conventional energy-users to become prosumers, who not only generate green energy, but also return the surplus energy back to the utility-grid. Managing these energy-sharing prosumers is a pivotal requisite in SG, and making goal-oriented prosumer community groups is an effective choice to accomplish that.However, developing sustainable prosumer community groups is challenging and one of the key challenges is recruiting dynamic prosumers to the community groups. In this research paper, we address this challenge by presenting an innovative framework for prosumer recruitment in prosumer community groups. The current literature has overlooked this challenge, making our work novel within the research field. The proposed prosumer recruitment framework suggests the use of iterative evaluation process to proactively monitor the quality of service delivered by registered prosumers before assigning them to the prosumer community groups. We demonstrate the functionality of the framework using energy behaviour dataset of 500 prosumers. Furthermore, we present performance benchmarks to validate the framework's ability to identify the dynamic prosumers within the dataset, and to assign the prosumers to the appropriate prosumer community groups. © 2013 CRL Publishing Ltd.

A decade-long conscious effort by the Semantic Web community has resulted in the formation of a decentralized knowledge platform which enables data interoperability at a syntactic and semantic level. For information interoperability, at a syntactic level, RDF provides the standard format for publishing data and RDFS gives structure to the information. For semantic-level interoperability, ontologies are used which allow information dissemination and assimilation among diverse applications and systems; where information is equally accessible and useful to humans and machines. The success of the linked open data project, recognition of explicit semantics (annotated through web ontologies) by search engines and the realized potential advantages of semantic data for publishers have resulted in tremendous growth in the use of web ontologies on the web. In order to promote the adoption of ontologies (to new users), reusability of adopted ontologies, effective and efficient utilization on ontological knowledge and evolving the ontological model, erudite insight on the usage of ontologies is imperative. While ontology evaluation attempts to evaluate a developed ontology to assess its fitness and quality, it does not provide any insight into how ontologies are being used and what is the state of prevalent knowledge patterns. Realizing the importance of measuring and analysing ontology usage to advance the adoption, reusability and exploitation of ontologies, we present a semantic framework for measuring and analysing ontology usage on the Web on empirical grounding.Our methodological approach is discussed to highlight the detail and role of each step. A framework is presented along with the set of metrics developed to measure ontology usage from different aspects such as ontology richness, usage and incentives to provide a holistic view on the state of ontology usage. The framework is then evaluated using an important use-case scenario to identify the prevalent knowledge patt

Cognitive Radio Networks (CRNs) deal with opportunistic spectrum access in order to fully utilize the scarce of spectrum resources, with the development of cognitive radio technologies to greater utilization of the spectrum. Nowa- days Cognitive Radio (CR) is a promising concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. In this paper, we propose two approaches. At first we propose a trust aware model to authenticate the secondary users (SUs) in CRNs which provides a reliable technique to establish trust for CRNs. Secondly, we propose trust throughput mechanism to measure throughput in CRNs.

With the rapid growth of wireless communication, the deployment of cyber-physical system (CPS) is increasing day by day. As a cyber-physical system involves a tight coupling between the physical and computational components, it is critical to ensure that the system, apart from being secure, is available for both the cyber and physical processes. Traditional methods have generally been employed to defend an infrastructure system against physical threats. However, this does not guarantee that the availability of the system will always be high. In this paper, we propose a multi-cyber (computational unit) framework to improve the availability of CPS based on Markov model. We evaluate the effectiveness of our proposed framework in terms of availability, downtime, downtime cost and reliability of the CPS framework.

The Semantic Web technologies and Linked Data principles enable information integration and datainteroperability at syntactic and semantic levels. This will lead to having a large amount of integrated data with increased visibility leading to the discovery and synthesis of knowledge to address financial issues. In this paper, we illustrate the implementation of the Financial Linked Data using the Linked Data principle within the financial domain. The use of XBRL (XML-based business reporting language)by various companies to generate their financial data has enabled us to obtain a standardized and uniform body of financial data. We utilize this format to link business facts to corresponding reports and then publish these data using Linked Data principles. Furthermore, financial data can be interlinked with other relevant datasets by creating semantic relationships. We believe that such linkage between various datasets represents an important step in achieving data transparency which in turn establishes a basis for making profitable decisions in the future.

What makes some students failed to perform well in their study is lack of talent and interest to the chosen of field of study. For example in Indonesia, there are around 2, 785 senior high school students failed in the National Examination in 2010 because most of them choose un- appropriate field of study which is not related to interest and talent. It is important to assist students to choose a potential area of study commensurate with their talents and interests and which covers all possible fields of study. To achieve that aim, standard to represents knowledge as a set of concepts is needed. This paper proposes an ontological framework to assist students to recognize the prospective fields of study related to their talent and interest. The framework is including two ontologies with different perspectives which will be linked using matching engine to get the possible field of study. Then, selection engine is to be used to get the final result of the appropriate field of study for student. This framework can be applied in educational field to improve student’s capacity and to help them in maintain their future life.

In cloud computing, service providers offer costeffective and on-demand IT services to service users on the basis of Service Level Agreements (SLAs). However the effective management of SLAs in cloud computing is essential for the service users to ensure that they achieve the desired outcomes from the formed service. In this paper, we introduce a SLA management framework that will enable serviceusers to select the best available service provider on the basis of its reputation and then monitor the run time performance of the service provider to determine whether or not it will fulfill its promise defined in the SLA. Such analysis will assist the service user to make an informed decision about the continuation of service with the service provider.

Recent advancements in wireless communication are creating a spectrum shortage problem on a daily basis. Recently, Cognitive Radio (CR), a novel technology, has attempted to minimize this problem by dynamically using the free spectrum in wireless communications and mobile computing. Cognitive radio networks (CRNs) can be formed using cognitive radios by extending the radio link features to network layer functions. The objective of CRN architecture is to improve the whole network operation to fulfil the user’s demands anytime and anywhere, through accessing CRNs in a more efficient way, rather than by just linking spectral efficiency. CRNs are more flexible and exposed to wireless networks compared with other traditional radio networks. Hence, there are many security threats to CRNs, more so than other traditional radio environments. The unique characteristics of CRNs make security more challenging. Several crucial issues have not yet been investigated in the area of security for CRNs. A typical public key infrastructure (PKI) scheme which achieves secure routing and other purposes in typical adhoc networks is not enough to guarantee the security of CRNs under limited communication and computation resources. However, there has been increasing research attention on security threats caused specifically by CR techniques and special characteristics of CR in CRNs. Therefore, in this research, a survey of CRNs and their architectures and security issues has been carried out in a broad way in this paper.

Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. This paper presents some challenges for developing distributed real-time Cyber-Physical Systems. The focus is on one particular challenge, namely event modelling in distributed real-time CPS. A Web-of-Things based CPS framework for event handling and processing is proposed. To illustrate the application of the proposed framework, a case study for achieving demand response in a smart home is provided.

One of the challenges of cloud computing is SLA assurance. On-demand and self-serve nature of cloud requires ascertaining real-time QoS assurance to meet SLA specifications of the consumer. A third-party service provider could be used to provide on demand QoS assessment service. We propose a SLA monitoring framework to address the real-time QoS assessment issue. The framework consists of two modules namely, reputation assessment module and transactional risk assessment module. Third-party service provider equipped with such assessment modules can provide real-time assessment for consumer's informed decision making to continue using a service or to migrate to another service provider in the case of service degradation. © 2012 IEEE.

The very recent development of Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. The solution to CPS must transcend the boundary between the cyber world and the physical world by providing integrated modelsaddressing issues from both worlds simultaneously. This needs new theories, conceptual frameworks and engineering practice. In this paper, we set out the key requirements that must be met by CPS systems, review and evaluate the progress that has been made in the development of theory, conceptual frameworks and practical applications. We then discuss the need for semantics and a proposed approach for addressing this. Grand challenges to informatics posed by CPS are raised in the paper.

In the current economic, budget tightening and competitive times, organizations need to be customer focused and provide customized service to customers to ensure their loyalty. To achieve this, Customer Relationship Management (CRM) systems help organizations to deal with and answer various customer queries. However with a change in the type of information being created (for example from structured to semistructured), CRM systems have to make effective use of such information which may be in multiple information sources for effective knowledge management and knowledge synthesis in order to provide customized services to the customers. In this paper, we propose a Customer Relationship Management ecosystem that conjointly utilizes multiple information sources and information types to achieve this. We explain the architecture of the proposed CRM ecosystems framework and demonstrate its application in the real estate domain.

Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository.

Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed.

The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results.

Risk assessment in business associations is the process which determines the likelihood of negative outcomes according to a given set of desired criteria. When there is more than one desired criterion to be achieved in a business association, the process of risk assessment needs to be done by capturing the importance that each of the criteria will have on the successful completion of the business activity. In this paper, we present an approach that determines the significance of each criterion with respect to the goal of the business association and by considering the inter-dependencies that may exist between the different assessment criteria. This analysis will provide important insights during the process of risk management, where the occurrence of such negative outcomes can be managed, according to their significance, to ensure the successful completion of a business activity.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share the excess energy with the utility grid or with other energy consumers. This type of energy user iscalled the “prosumer”. In current society, a massive number of energy-users have transformed into prosumers due to many reasons such as the strong society attitude with respect toalleviation of negative climate impacts, desires to decrease electricity costs, and various government regulations, including generous feed-in tariff schemes. This leads much attention withinthe research community on investigating the aspects of prosumers connected to SG. However most researchers find it challenges to find a large dataset of prosumers for performing the experiments. This leads the necessity of identifying the generic prosumers’ realistic energy behaviors, and accordingly generates a synthetic dataset. In this research paper, we present prosumers’ realistic energy behavior profiles during summer and winter periods in Australia and present its application in generating a synthetic dataset. The new researchers can use the identified energy profiles as a benchmark to generate a synthetic dataset for their experiments.

The increasing popularity of the cloud computing paradigm and the emerging concept of federated cloud computing have motivated research efforts towards intelligent cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Given the intricate and heterogeneous nature of current clouds, the cloud service selection process is, in effect, a multi criteria optimization or decision-making problem. The possible criteria for this process are related to both functional and nonfunctional attributes of cloud services. In this context, the two major issues are: (1) choice of a criteria-set and (2) mechanisms for the assessment of cloud services against each criterion for thorough continuous cloud service monitoring. In this paper, we focus on the issue of cloud service monitoring wherein the existing monitoring and assessment mechanisms are entirely dependent on various benchmark tests which, however, are unable to accurately determine or reliably predict the performance of actual cloud applications under a real workload. We discuss the recent research aimed at achieving this objective and propose a novel user-feedback-based approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms.

Cloud computing is a new paradigm for service-based computing and is gaining popularity. An efficient way for the assurances of the expected service levels in cloud computing is to establish a tailor-made Service Level Agreement (SLA) and to ensure the commitment of SLAs by service providers. In this paper, we conduct a survey of the state of the art in cloud SLA assurance from two aspects – pre- and post-interaction phases, based on which research gaps in existing approaches are identified. Newresearch requirements for SLA assurance are then presented.

Cognitive Radio Networks (CRNs) is a promising technology which deals with shared spectrum access and usage in order to improve the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Security becomes a very challenging issue in CRNs as different types of attacks are very common to cognitive radio technology compared to general wireless networks. The proper working of cognitive radio and the functionality of CRNs relies on the compliant behaviour of the secondary user. In order to address this issue, we propose two approaches in this paper. Firstly, we propose a trust aware model to authenticate the secondary users of CRNs which offers a reliable technique to provide a security-conscious decision by using trust evaluation for CRNs. Secondly, we propose an analytical model for analyzing the availability of spectrum in CRNs using a stochastic approach. We have modeled and analyzed the availability of free spectrum for the usage of secondary users by adopting different activities in a spectrum management scheme to improve the spectrum availability in CRNs.

Due to the rapid growth of wireless applications, Cognitive Radio (CR) has been considered as a technology to solve spectrum source deficiency by improving effective utilization of limited radio spectrum resources for future wireless communications and mobile computing. However, the unique characteristics of CRNs make security a greater challenge. Moreover, due to the dynamic characteristics of CRNs, a member of CRNs, may join or leave the network at any time. The issue of supporting secure communication in CRNs therefore becomes more serious than for conventional wireless networks. Public key encryption and signature techniques have been adopted here to ensure the security of message transmission in CRNs. This work proposes a digital signature-based trust-oriented scheme to ensure communication by identifying efficient trustworthy users in CRNs. The security analysis is analyzed to guarantee that the proposed approach achieves security proof.

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).

Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology.

In Demand-driven Production with Just-in-Time inputs, there are several sources of uncertainty which impact on the manufacturer’s ability to meet the required customer’s demand within the given time frame. This can result in a loss of revenue and customers, which will have undesirable impacts on the financial aspects and on the viability of the manufacturer. Hence, a key concern for manufacturers in just-in-time production is to determine whether they can meet a specific level of demand within a given time frame, to meet the customers’ orders and also to achieve the required revenue target for that period of time. In this paper, we propose a methodology by which a manufacturer can ascertain the probability of not meeting the required demand within a given period by considering the uncertainties in the availability of production units and raw materials, and the loss of financial revenue that it would experience as a result.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the “prosumer”. The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of virtual communities, which involves with computation, software, data access, and storage services that do not need end-prosumer knowledge of the physical location and system configuration. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyses the different parameters influencing the prosumer’s energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.

Service Level Agreement (SLA) has its application in Service-Oriented Computing, Grid Computing and Cloud Computing. SLA sets a benchmark against which the Quality of Service (QoS) a service provider is assessed. This assessment can be pre-interaction, post-interaction or during service delivery. QoS assessment during service delivery helps in decision making to select alternate service in case a service degrades below a given benchmark. This is particularly helpful in Cloud Computing where sometimes decisions need to be taken on the fly to avoid complete failure of service and to meet business needs. In this paper we use Risk Assessment to assess QoS during service delivery. In the literature, approaches have been proposed that allow for service re-composition in SOA but in Cloud Computing techniques are needed that assist in making informed service management decision. Since, similar to SOA, Cloud has self-serve autonomous mechanism, informed service management decision will help in Cloud service re-composition.

Recently, there has been much research focus on trust and reputation modelling as one of the key strategies for the formation of successful business intelligence strategies, particularly for service in mobile applications. One of the key trust modelling activities is trust prediction. During this process, the accuracy and reliability of the predicted trust values play an important role in the making of informed business decisions. Key factors to be considered at this stage are the variability and the high levels of distortion in the input series that have to be captured when predicting the trust values at a point in time in the future. In this paper, we propose a Multi-layer Feed Forward Artificial Neural Network to predict the future trust values of entities (services, agents, products etc.) for a future point in time based on data series input. We use four different non-uniform’ data input series and measure the accuracy of the predicted values under different experimental scenarios for benchmarking and comparison with existing approaches. Results indicate that the model is reliable in predicting trust values even in scenarios where there are only limited data available on training the neural network and a high level of distortion is present in the input series.

Climate change represents the possibility of losing the quality of the existing ecosystems around the world. To manage such consequences, dedicated actions for resilience are needed and the process of adaptation is the key that has to be carried out at a regional level. The challenge at this level is not only to assess vulnerabilities and risks to each ecosystem, but also to develop policies to adapt so as to achieve sustainability. In this paper, we focus on the ecosystem of water and study the different impacts on it from the perspective of regional Australia. We then propose a digital ecosystem-based architecture in a multisystem approach, which can be utilised for the process of water sustainability adaptation in regional Australia.

Risk Management is an important process to be carried out for any form of interaction before decision-making. This process will help the concerned user to take steps and actions accordingly in order to address risks and achieve the activity's desired goals. Various techniques and approaches for risk management have been discussed in the literature. But the process of risk management is not generic and it varies and changes according to the domain or purpose of the interaction for which it is being considered. E-business interactions are collaborative interactions that are mostly carried out over a virtual environment. But by considering the growth of e-business interactions, a process of risk management is needed by which the interacting user can actually minimize and lessen the level of risk before decision-making. In this paper, a quantitative approach for risk management in e-business interactions is proposed which the interacting user can utilize to lessen the level of risk and then make an interaction-based decision.

Business activities are a result of carefully formed associations between different users in order to achieve certain pre-decided outcomes. Decision-making in such associations is an important step and transactional risk analysis is one of the integral processes that facilitates this step. This paper presents an approach that determines the negative consequences (termed as financial risk) of forming e-business associations. Unlike other approaches, our model captures the different types of events and their uncertainties to determine the financial risk by using the convolution operator and expressing it as a probabilistic measure rather than as a crisp financial value. Such representation makes sense as the financial risk may be determined at a point of time in future where nothing is certain. Depending upon the complexity of the problem, we explain the different ways of using the convolution operator to determine the financial risk. The simulation result shows a better representation and understanding of the financial risk that will provide important inputs to the transactional risk analysis and the decision-making process.