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This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.

This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.

Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.

This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.

This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.

This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Access to educational material has become an important issue for many stakeholders and the focus of much research worldwide. Resource discovery in educational gateways is usually based on metadata and this is an area of important developments. Resource metadata has a central role in the management of educational material and as a result there are several important metadata standards in use in the educational domain. One of the most widely used general metadata standards for learning material is the Dublin Core Metadata Element Set. The application of this general purpose, metadata standard for complex and heterogeneous educational material is not straightforward. This paper will give an overview of some practical issues and necessary steps in deploying Dublin Core based on the LITC experience in the EASEL (Educators Access to Services in the Electronic Landscape) project.

This paper aims to investigate the internet web page metadata usage behavior in terms of their metadata element co‐occurrences. Metadata are designed to facilitate both web publishers/authors to organize their web pages and search engines to index the web pages accurately.

This study examines the types of metadata elements employed by different professional groups of web authors, the number of elements they prefer to use, and the types of element combinations they typically embed in their pages' HTML code.

The findings reveal that the “keyword” and “description” elements were the most popular single elements. The most popular combination of two elements was that of “keyword and description”. Very few authors included combinations of five elements. This study also shows that preferences for element combinations varied by domains.

This approach will enhance the current understanding of metadata usage behavior and may help search engine designers as they continue their quest for improved indexing and retrieval of web pages.

The purpose of this research is to explore the product-agency benefits that emerge as consumers interact with products, and how these benefits shape consumer experiences and marketing-related outcomes.

Sixty-one depth interviews were conducted, and 78 written narratives were collected from informants, which explored how products had changed consumers’ lives. The authors applied the tenets of grounded theory in the analysis of their text, creating abstract categories or tropes that reflected consistent patterns in their consumers’ experiences.

The findings support that the conceptualization of agentic benefits should be broadened. The research identifies five salient product-agency benefits: regulation, clarification, transcendence, discovery and growth.

Prior conceptualizations of agency in marketing focus almost solely on control, yet the authors find that multiple product-agency benefits emerge, supporting the need for a broader understanding of product-related agency. The authors also find these benefits can be anticipated or unanticipated. It is also important to note that the benefits can be paradoxical, in that while they often yield positive outcomes, at times they can produce unintended and even negative consequences.

Incorporating consumers’ (vs researchers’) benefit perceptions into theory building and preference models will enhance understanding of consumer behavior and improve predictive power of preference and choice forecasts. The five salient product-agency benefits provide mechanisms for segmentation and building meaningful relationships with consumers, can propel product development and assist in creating more effective marketing–communication strategies.

The paper offers a broader, more nuanced conceptualization of agency beyond control. It identifies five types of product-agency benefits that reflect a wide spectrum of consumers’ lived experiences.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

The purpose of this paper is to characterise a number of current and future computing environments and summarises their resource discovery requirements. It then seeks to analyse, with respect to the requirements of each environment, several established service discovery protocols and some newer protocols that are still in the research domain. In addition, the key features of a new resource discovery protocol that has been developed to operate with heterogeneous computing environments are described.

A comprehensive literature survey was undertaken, highlighting the shortcomings of existing resource discovery protocols with respect to large pervasive computing environments. Given the identified gaps in existing protocols, an alternative protocol is suggested.

The main findings of this paper relate to the identified shortcomings of existing resource discovery protocols. It was also found that a hybrid resource discovery protocol capable of spanning dynamic, mobile computing environments and more stable ones was able to overcome many of the challenges presented by large‐scale pervasive computing environments.

This paper presents comprehensive literature survey of the state‐of‐the‐art in resource discovery protocols, pointing out some of the problems that are not solved. The paper describes the design of an alternative protocol, and presents an evaluation of it. The pervasive computing research community can draw upon the survey and evaluation to guide the design of future resource discovery protocols for the increasingly dynamic world in which we live.

The purpose of this paper is to focus on the distinction between smart specialisation and smart specialisation policy and it studies under what conditions a smart specialisation policy is necessary.

A conceptual framework is built based on historical evidence of successful dynamics of structural changes at regional level qualified as “smart specialisation”. The identification of market and coordination failures that are likely to impede the occurrence of spontaneous process of smart specialisation makes a good case for a smart specialisation policy.

The paper highlights important design principles for the policy process that should help to minimise potential risks of policy failures and policy capture.

The paper does assess the effect of smart specialisation on innovation and growth at regional level because it is too early to observe and measure effects. The paper confines itself to conjectures about the effects of such a policy.

The paper makes recommendations and explains some of the practicalities about the implementation of the policy at regional level.

The paper is one of the first dealing with the topic of smart specialisation policy.

Web services are going mobile. A mobile enterprise can be established in a cellular network by participating mobile hosts, which act as web service providers, and their clients. Mobile hosts enable seamless integration of user‐specific services to the enterprise, by following web service standards, also on the radio link and via resource constrained smart phones. However, establishing such a mobile enterprise poses several technical challenges, such as the quality of service (QoS), discovery aspects and proper integration solutions, for the network as well as for the mobile phone users. The purpose of this paper is to summarize the challenges and research associated with this domain and with establishing the mobile enterprise.

The paper summarizes the challenges and research in the mobile web services domain, along with a detailed discussion of the developed mobile web service mediation framework (MWSMF). MWSMF is realized based on the enterprise service bus (ESB) technology, as an intermediary between mobile hosts and web service clients. Moreover, to scale the mobile enterprise to the loads possible in cellular networks, some of the components of MWSMF are shifted to the new utility computing paradigm, cloud computing.

From the study it was found that ESB provides a good integration solution for the mobile enterprise research challenges. The detailed analysis of the MWSMF concludes that the mediation framework and its components are horizontally scalable, thus allowing to utilize elasticity of cloud platform to meet load requirements of mobile enterprise in an easy and quick manner.

The study addresses the research with providing services from smart phones and establishing mobile enterprise. The QoS challenges are addressed and the study introduces an integration framework using ESB technology. The porting of MWSMF onto the cloud is also addressed. As an added value, the research serves as a case study for porting enterprise applications to the cloud.

The purpose of this column is to explore methods in which information professionals can contribute to the intelligence of open discovery systems.

This is a conceptual column based on experience in this field and a review of the relevant literature.

There are several ways in which information professionals can contribute to make their discovery systems more robust through careful semantic analysis.

This column explores ways in which the fields of linguistics, semantic analysis, semiotics, and web ontologies can assist information professionals in building responsive and robust back end indexing systems that can be coupled to front end open discovery frameworks.

Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process.

The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation.

Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development).

The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners.

This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories.

This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.