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Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).

The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.

Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.

The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

The purpose of this paper is to propose a novel quality of service (QoS)‐aware service composition approach, called SEQOIA, capable of defining at run‐time a service composition plan meeting both functional and non‐functional constraints and optimizing the overall quality of service.

SEQOIA is a semantic‐driven QoS‐aware dynamic composition approach leveraging on an integer linear programming technique (ILP). It exploits the expressiveness of an ontology‐based service profile model handling structural and semantic properties of service descriptions. It represents the service composition problem as a set of functional and non‐functional constraints and an objective function.

The authors developed a proof of concept implementing SEQOIA, as well as an alternative composition solution based on state‐of‐the‐art AI planning and ILP techniques. Results of testing activities show that SEQOIA performs better than the alternative solution over a limited set of candidate services. This behaviour was expected, as SEQOIA guarantees to find the service composition providing the optimal QoS value, while the alternative approach does not provide this guarantee, as it handles separately the specification of the functional service composition flow and the QoS‐based service selection step.

SEQOIA leverages on semantic annotations in order to make service composition feasible by coping with syntactic and structural differences typically existing across different, even similar, service implementations. To ease the adoption of SEQOIA in real enterprise scenarios, the authors chose to leverage on an XML‐based message model of services interfaces (including but not strictly requiring the use of WSDL).

Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.

The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.

The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.

This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.

This article seeks to demonstrate a technique for using a Geographic Information System (GIS) to analyze US Census data to better understand potential library users and improve library service planning.

A GIS was used to link variables such as age, race, income, and education from the 2000 US Census with service area maps of two proposed branch libraries. Thematic maps were created for each of the census variables to display demographic information about potential library users within a three‐mile radius of the proposed libraries.

The GIS maps and their associated attribute data enhanced the ability to analyze and compare the demographics of potential users in the two library areas and identify significant differences. The data on age, race, education and income for residents in the two areas were combined with known library use indicators to help plan library services with the potential to attract different populations in the local community.

Provides practical information about downloading US Census data into a GIS to be able to present demographic data about potential library users both visually and quantitatively.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Any computing architecture cannot be designed with complete confidentiality. As a result, at any point, it may leak the information. So, it is important to decide leakage threshold in any computing architecture. To prevent leakage more than the predefined threshold, quantitative analysis is helpful. This paper aims to provide a method to quantify information leakage in service-oriented architecture (SOA)-based Web services.

To visualize the dynamic binding of SOA components, first, the orchestration of components is modeled. The modeling helps to information-theoretically quantify information leakage in SOA-based Web services. Then, the paper considers the non-interference policy in a global way to quantify information leakage. It considers not only variables which interfere with security sensitive content but also other architectural parameters to quantify leakage in Web services. To illustrate the attacker’s ability, a strong threat model has been proposed in the paper.

The paper finds that information leakage can be quantified in SOA-based Web services by considering parameters that interfere with security sensitive content and information theory. A hypothetical case study scenario of flight ticket booking Web services has been considered in the present paper in which leakage of 18.89 per cent information is calculated.

The paper shows that it is practically possible to quantify information leakage in SOA-based Web services. While modeling the SOA-based Web services, it will be of help to architects to identify parameters which may cause the leakage of secret contents.

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.

Web services play vital role in the development of emerging technologies such as Cloud computing and Internet of Things. Although, there is a close relationship among the discovery, selection and composition tasks of Web services, research community has treated these challenges at individual level rather to focus on them collectively for developing efficient solution, which is the purpose of this work. This paper aims to propose an approach to integrate the service discovery, selection and composition of Semantic Web services on runtime basis.

The proposed approach defined as a quality of service (QoS)-aware approach is based on QoS model to perform discovery, selection and composition tasks at runtime to enhance the user satisfaction and quality guarantee by incorporating non-functional parameters such as response time and throughput with the Web services and user request. In this paper, the proposed approach is based on ontology for semantic description of Web services, which provides interoperability and automation in the Web services tasks.

This work proposed an integrated framework of Web service discovery, selection and composition which supports end user to search, select and compose the Web services at runtime using semantic description and non-functional requirements. The proposed approach is evaluated by various data sets from the Web Service Challenge 2009 (WSC-2009) to show the efficiency of this work. A use case scenario of Healthcare Information System is implemented using proposed work to demonstrate the usability and requirement the proposed approach.

The main contribution of this paper is to develop an integrated approach of Semantic Web services discovery, selection and composition by using the non-functional requirements.

The purpose of this paper is to examine the redistributive effect of domestic public debt: lenders to the government lie on the higher end of the income distribution, but the burden of debt financing falls on the entire tax base, to the extent that taxes are used to service debt. Because domestic debt is typically held by domestic lenders, this involves a redistribution of resources.

The author uses cross-country panel data on debt composition, and run regressions of income inequality, as measured by the Gini coefficient, using various specifications, controlling for a variety of macroeconomic, fiscal and political variables.

The author finds that the composition of public debt is consistently a significant determinant of income inequality: the domestic share of public debt is regressive and significant across all specifications, even controlling for total and external debt servicing, political conflict, corruption and a variety of government spending variables.

The data span 18 years (1990-2007) which means that long-run effects are hard to track. While the author has a good mix in the sample of observations from low-, middle- and high-income countries, the author is constrained in the choice of countries by the availability of data on inequality and on the composition of public debt.

This is the first paper to examine the composition of public debt in terms of domestic and external debt, and any bearing it may have on income inequality. The finding is also new for both the public debt and income inequality literatures: cross-country panel data are consistent with the belief that domestic debt redistributes resources from the entire tax base to wealthy holders of government debt in a way that external debt does not.