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This paper briefly introduces the trends towards e‐learning and amplifies some examples of state of the art systems, pointing out that all of these are, to date, limited by adaptability and shareability of content and that it is necessary for industry to develop and use an inter‐operability standard. Uses SCORM specifications to specify the skeleton of an architecture to develop an advanced learning management system.

The purpose of this paper is to describe a large-scale algorithm for generating a catalogue of scientific publication records (citations) from a crowd-sourced data, demonstrate how to learn an optimal combination of distance metrics for duplicate detection and introduce a parallel duplicate clustering algorithm.

The authors developed the algorithm and compared it with state-of-the art systems tackling the same problem. The authors used benchmark data sets (3k data points) to test the effectiveness of our algorithm and a real-life data ( > 90 million) to test the efficiency and scalability of our algorithm.

The authors show that duplicate detection can be improved by an additional step we call duplicate clustering. The authors also show how to improve the efficiency of map/reduce similarity calculation algorithm by introducing a sampling step. Finally, the authors find that the system is comparable to the state-of-the art systems for duplicate detection, and that it can scale to deal with hundreds of million data points.

Academic researchers can use this paper to understand some of the issues of transitivity in duplicate detection, and its effects on digital catalogue generations.

Industry practitioners can use this paper as a use case study for generating a large-scale real-life catalogue generation system that deals with millions of records in a scalable and efficient way.

In contrast to other similarity calculation algorithms developed for m/r frameworks the authors present a specific variant of similarity calculation that is optimized for duplicate detection of bibliographic records by extending previously proposed e-algorithm based on inverted index creation. In addition, the authors are concerned with more than duplicate detection, and investigate how to group detected duplicates. The authors develop distinct algorithms for duplicate detection and duplicate clustering and use the canopy clustering idea for multi-pass clustering. The work extends the current state-of-the-art by including the duplicate clustering step and demonstrate new strategies for speeding up m/r similarity calculations.

Developing digital content for online learning is an expensive task. A cost‐effective approach is to reuse and re‐purpose existing resources. Emerging specifications such as those developed by the IMS Global Learning Consortium allow content to be specified in standard ways, and are therefore reusable across different content management systems. This paper identifies the role of content packaging within the wider context of content management and describes the use of IMS specifications with their implementation in a prototype tool funded by the UK Joint Information Systems Committee (JISC).

With the proliferation of objectionable materials (e.g. pornography, violence, drugs, etc.) available on the WWW, there is an urgent need for effective countermeasures to protect children and other unsuspecting users from exposure to such materials. Using pornographic Web pages as a case study, this paper presents a thorough analysis of the distinguishing features of such Web pages. The objective of the study is to gain knowledge on the structure and characteristics of typical pornographic Web pages so that effective Web filtering techniques can be developed to filter them automatically. In this paper, we first survey the existing techniques for Web content filtering. A study on the characteristics of pornographic Web pages is then presented. The implementation of a Web content filtering system that combines the use of an artificial neural network and the knowledge gained in the analysis of pornographic Web pages is also given.

Data warehousing is the technological trend for the corporate decision support process. This article investigates the current business environment of the data warehouse, including OLAP, data mining, data visualization and other technologies. This article also analyzes the importance of data warehouse management and maintenance and its future developments.

In this paper, the possibilities for implementing modern Internet‐based ICT tools for the management of architectural design teams are explored. The theory of Davenport on information ecology, Schön's approach to design thinking and design team behaviour, and decision process management and information technology theories are combined in a proposal for the design of project‐based digital design teams. It is argued that the implementation of the so‐called Project Web Site (PWS) Technology may lead to significant improvement of architectural design team performance. The structure of the PWS within this specific context is described as well as implications for the working procedures and the management of building design teams.

The purpose of this paper is to present a showcase of semantic time series processing which demonstrates how this technology can improve time series processing and community building by the use of a dedicated language.

The authors have developed a new semantic time series processing language and prepared showcases to demonstrate its functionality. The assumption is an environmental setting with data measurements from different sensors to be distributed to different groups of interest. The data are represented as time series for water and air quality, while the user groups are, among others, the environmental agency, companies from the industrial sector and legal authorities.

A language for time series processing and several tools to enrich the time series with meta‐data and for community building have been implemented in Python and Java. Also a GUI for demonstration purposes has been developed in PyQt4. In addition, an ontology for validation has been designed and a knowledge base for data storage and inference was set up. Some important features are: dynamic integration of ontologies, time series annotation, and semantic filtering.

This paper focuses on the showcases of time series semantic language (TSSL), but also covers technical aspects and user interface issues. The authors are planning to develop TSSL further and evaluate it within further research projects and validation scenarios.

The research has a high practical impact on time series processing and provides new data sources for semantic web applications. It can also be used in social web platforms (especially for researchers) to provide a time series centric tagging and processing framework.

The paper presents an extended version of the paper presented at iiWAS2012.

Distributed data streams are an important topic of current research. In such a setting, data values will be missed, e.g. due to network errors. This paper aims to allow this incompleteness to be detected and overcome with either the user not being affected or the effects of the incompleteness being reported to the user.

A model for representing the incomplete information has been developed that captures the information that is known about the missing data. Techniques for query answering involving certain and possible answer sets have been extended so that queries over incomplete data stream histories can be answered.

It is possible to detect when a distributed data stream is missing one or more values. When such data values are missing there will be some information that is known about the data and this is stored in an appropriate format. Even when the available data are incomplete, it is possible in some circumstances to answer a query completely. When this is not possible, additional meta‐data can be returned to inform the user of the effects of the incompleteness.

The techniques and models proposed in this paper have only been partially implemented.

The proposed system is general and can be applied wherever there is a need to query the history of distributed data streams. The work in this paper enables the system to answer queries when there are missing values in the data.

This paper presents a general model of how to detect, represent, and answer historical queries over incomplete distributed data streams.

For a long time the European geographical indication (GI) regulation has been of great interest to economists and policymakers. To justify exclusive European regulation it is necessary to prove the positive value of a GI quality signal (i.e. label), which is often achieved by quantifying its monetary value for the consumers. But even though a large number of literary contributions already deal with this question, they lack the evaluation of overall effect sizes for the GI label. The purpose of this paper is, therefore, to quantify and evaluate the overall marginal consumer willingness to pay for the European GI label.

To reach this aim, a meta-analysis is used for which a literature survey had been carried out in order to determine the GI label effects (LEs). In addition to previous works, this paper not only includes a meta-analysis, but also implements a heterogeneity analysis to distinguish between the LEs of individual GI standards. To eliminate study- and product-specific determinants of heterogeneity, moderator variables are used.

The empirical results indicate that consumers have a highly significant and positive marginal willingness to pay for GIs. However, the marginal willingness to pay differs significantly between the individual GI standards and indicates great heterogeneity between the protected products.

As an extension to previous studies and meta-analysis; this paper includes the most extensive GIs meta-data set so far, and conducts for the first time an independent heterogeneity analysis to distinguish between the LEs of individual GI standards and implements a moderator analysis to eliminate study- and product-specific determinants of heterogeneity from the GI effects.

The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up‐skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities.

The paper provides an overview of an emerging area for work‐related research in the field of knowledge management and collaborative online communities.

The growing number of social network online communities requires a systematic assessment of the application and design of social network technologies, which makes this study relevant and timely.

This paper gives guidance in an emerging research area with major implications for online communities and human resources management.

Fulfils a need, since a lack of literature in the field is apparent.