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Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

The purpose of this paper is to explore the concept of pensionomics as a prospective tool for pension evaluation. This paper suggests a paradigm shift – a multi-disciplinary synthesis of differing perspectives in evaluating pension’s overall performance based on past work on pension evaluation – incorporating non-economic variables with significant impact on economic growth and social development.

This paper suggests a new analytical tool called “Pensions Consistency (PC) Index” that identifies the level of consistency and the strengths and weaknesses within any pension system. The new conceptual framework focusses on building inter-sectoral and holistic policies able to respond to the new multi-dimensional dynamic environment.

The consideration of pensionomics concept as an evaluation tool for pension schemes provides insights that are helpful in explaining performance differentials. Taking definition, classification and evaluation as a guiding principle, the new conceptual framework can be a useful point of reference for the overall evaluation of pension schemes, revealing deficiencies that traditional evaluation methods cannot detect. The multi-disciplinary approach focusses on building inter-sectoral and holistic policies that are able to respond to the multi-dimensional uncertainties of the new dynamic environment.

The heterogeneity and complexity in event dynamics are systemic in the sense that the impact is far from linear. The idiosyncratic nature of unexpected and unpredictable events is rather a result of multi-dimensionality based, among others, on magnitude, frequency, timing, intensity and impact. It is plausible to argue that crisis episodes can destabilize critical systems of economic activity, producing economic spillovers that can directly or indirectly affect the sustainability of pension schemes. If the calculation of direct economic impact is readily traceable, the estimation of indirect economic impact can be an onerous task.

Pensionomics places the concept of retirement in a multi-disciplinary context. Pensionomics overcomes theoretical and empirical limitations encountered by the path-dependency perspective, developing a new research agenda to study pension schemes under historical, cultural, social, political, economic, political and environmental prism. Integrating diversified data, techniques, perspectives and concepts, pensionomics’ objective is to connect natural and man-made events with social protection mechanisms for the development of a dynamic social protection framework where individual, community and society needs are met effectively and efficiently by implementing tailored policies, closely related to their specific context.

The concept of retirement has evolved constantly, transforming societies and shaping both income and non-income dimensions of well-being. Pension entitlement turned gradually from a political discourse to a human right discourse. Pension schemes have extended the scope of insurance coverage beyond labour markets and the lifecycle, supporting the broader needs of entire population. Furthermore, pension schemes are widely acknowledged as drivers of economic growth: they enhance labour productivity; foster smooth consumption; and create a stable economic environment for investment and innovation. Current expectations require pension schemes to adopt proactive and reactive policies to examine options for mitigation or for modification of potential consequences in anticipation of exceptional events.

This paper suggests a paradigm shift, a multi-disciplinary approach called pensionomics, and this “multi-disciplinary” focus builds a new analytical framework to evaluate pension’s overall performance based on past work on pension evaluation, incorporating non-economic variables with significant impact on economic growth and social development. PC-Index introduces a comprehensive evaluation tool to study the coverage, performance, efficiency, effectiveness, current trends and future possibilities of pension schemes.

Highlights the challenge of evaluating the healthy or health‐promoting school and describes the development of an instrument for profiling and monitoring the development of such institutions. The adaptability and scope of the instrument is discussed at length and this includes use of the instrument to undertake valuations of existing health‐promotion assets within schools and evaluation of the health‐promoting school. Examples of how the instrument has been used, both nationally and internationally, are provided. A detailed description of the methodology introduces the notion of profiling the health‐promoting/healthy school, and health‐promoting assets in schools, using a multiple‐axes radial profile graph. Discusses the challenges of creating an evaluation instrument that recognises the practical difficulties of undertaking evaluation in schools, the complex nature of the health‐promoting school, and the underlying principles of evaluating health promotion.

In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

As a response to the growing operational and disruptive threats to water distribution networks (WDNs), researchers have developed a vast array of methods for the reliability analysis of WDNs. In order to follow this growing number of methods, this paper reviews and documents in one place the historical developments in the reliability analysis of WDN.

A systematic literature review (SLR) is carried out to summarize the state-of-the-art research on reliability analysis of WDNs. In conducting this systemic literature review, the authors adopted an iterative approach to define appropriate keywords, analyze and synthesize data and finalizing the classification results.

First, the hydraulic approach to reliability analysis is currently pervasive, and relatively little academic research has addressed the topological reliability analysis of WDNs. Second, in order to provide a comprehensive picture of the network reliability, a different approach that integrates topological and hydraulic attributes seems a more effective method. Third, the conventional reliability analysis methods are only effective for demonstrating a snapshot of these networks at a given point in time. The availability of methods that enable researchers to evaluate the reliability in response to changes in its variables is still a major challenge.

The present paper facilitates future research in the reliability analysis of WDNs by providing a source of references for researchers and water utilities. Further, this article makes a contribution to the literature by offering a roadmap for future reliability analysis of WDNs by reviewing the evolution of the current reliability analysis methods throughout history.

Affordable mobile devices with video playback functionality are rapidly growing in the market. Current wireless and third generation communication networks enable smoother and higher quality streaming video. With the support of these technologies, most participants in telecom value‐added service chain are planning to shift their business focus to a more profitable and appealing area, mobile TV. Previous work that survey on users' behavior while consuming mobile TV has indicated that users normally watch brief and casual contents, and not the full program. However, most of the current services adopt a “push” approach since users passively receive pre‐defined contents, rather than pulling the interesting topics and segments. In order to promote a more enjoyable and rewarding watching experience, this paper will propose a framework to support a fully interactive mobile TV. The main goal is to enable users to: 1) visually locate interesting topics across multiple genres (such as news, sports and entertainment) and 2) fully control the playback flow of the multimedia items while selecting the most interesting segments. A web‐based system has been developed to implement and test the effectiveness of the proposed framework in a wireless and mobile setting.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.

The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.

This study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.

The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.