STARGATE : Static Repository Gateway and Toolkit. Final Project Report

STARGATE (Static Repository Gateway and Toolkit) was funded by the Joint Information Systems Committee (JISC) and is intended to demonstrate the ease of use of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) Static Repository technology, and the potential benefits offered to publishers in making their metadata available in this way This technology offers a simpler method of participating in many information discovery services than creating fully-fledged OAI-compliant repositories. It does this by allowing the infrastructure and technical support required to participate in OAI-based services to be shifted from the data provider (the journal) to a third party and allows a single third party gateway provider to provide intermediation for many data providers (journals). Specifically, STARGATE has created a series of Static Repositories of publisher metadata provided by a selection of Library and Information Science journals. It has demonstrated the interoperability of these repositories by exposing their metadata via a Static Repository Gateway for harvesting and cross-searching by external service providers. The project has conducted a critical evaluation of the Static Repository approach in conjunction with the participating publishers and service providers. The technology works. The project has demonstrated that Static Repositories are easy to create and that the differences between fully-fledged and static OAI Repositories have no impact on the participation of small journal publishers in OAI-based services. The problems for a service that arise out of the use of Static Repositories are parallel to those created by any other repository dealing with journal articles. Problems arise from the diversity of metadata element sets provided by a given journal and the lack of specific metadata elements for the articles' volume and issue details. Another issue for the use of publishers' metadata arise as the collection policies of some existing services only allow Open Access materials to be included in them. The project recommends that the use of Static Repositories continues to be explored - in particular as a flexible way to expose existing sets of structured information to OAI services and to create the opportunity to enhance the metadata as part of the process. The project further recommends that the publishing community consider the creation or adoption of an application profile for journal articles to support information discovery that can search by volume and issue. Significant further use of the Static Repository technology by small journal publishers will require the future creation and maintenance of a community-specific Static Repository Gateway. Further use will also require advocacy within the publishing community but might initially be most effectively kick-started through the creation of OAI repositories based on metadata held by the commercial services which publish or mediate access to electronic copies of journals on behalf of small publishers

A matching approach to business services and software services

Recent studies have shown that service-oriented architecture (SOA) has the potential to revive enterprise legacy systems (Cai et al., 2011; Gaševic and Hatala, 2010; De Castro et al., 2011; Chengjun, 2008; Elgedawy, 2009; Tian et al., 2007; Chen et al., 2009; Zhang et al., 2006; Sindhgatta and Ponnalagu, 2008; Khadka, 2011), making their continued service in the corporate world viable. In the process of reengineering legacy systems to service-oriented architecture, some software services extracted in legacy system can be reused to implement business services in target systems. In order to achieve efficient reuse to software services, a matching approach is proposed to extract the software services related to specified business services, where service semantics and structure similarity measures are integrated to evaluate the similarity degree between business service and software services. Experiments indicate that the approach can efficiently map business services to relevant software services, and then legacy systems can be reused as much as possible

DC-image for real time compressed video matching

This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity

Retrieval Technology of Enterprise Data Center Resources Based on Density Peak Clustering Algorithm

In order to effectively ensure the retrieval effect of enterprise data center resources, improve the retrieval accuracy of enterprise data center resources, and shorten the retrieval time of enterprise data center resources, a retrieval technology of enterprise data center resources based on density peak clustering algorithm is proposed. Analytical clustering algorithms, density clustering algorithms, and density peak clustering algorithms are all types of clustering algorithms. To reduce the dimensionality of enterprise data center resources, the kernel principal component analysis method is used. The structure of the enterprise data center resource set is reorganized and the feature quantity of the enterprise data center resource distribution is extracted using feature space reorganization technology. On this basis, the density peak clustering is carried out on the data center resource set of enterprise, and the semantic association distribution model of data center resource retrieval in enterprise is constructed. Through the semantic registration and weighted vector combination control method, the retrieval of enterprise data center resources is realized. The experimental results show that the proposed algorithm has a good effect on the retrieval of enterprise data center resources, which can effectively improve the resource retrieval accuracy and shorten the resource retrieval time

The relationships between corporate meeting planner's personality traits and their choices of meeting places

This study is to determine the influence of personality on the novelty preference for corporate meeting destination choice. The Big-Five model of personality which consists of five traits namely openness, conscientiousness, extraversion,agreeableness, and neuroticism was employed to operationalise the personality construct. A total of 75 corporate meeting planners drawn from public listed service organisations were involved. The main method of data collection was questionnaire survey and multiple regression analysis was employed as the main statistical analysis. The results revealed that only openness (positively) and agreeableness (negatively) contributed significantly to the prediction of novelty preference for corporate meeting destination choice. This study, which also seeks to determine the relationships between some demographical variables and novelty preference, found that demographical information is not a good predictor of meeting destination choice. The main implication of this study is pertaining to the segmentation and targeting of the corporate meeting market. This study also helps in bridging the gap between tourism marketing and organisational research

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed

Personalisation of eSearch Services – Concepts, Techniques, and Market Overview

The importance of information in today’s society is still growing and information search has become an essential task in both the workplace and in private life. eSearch services provide access to the abundance of information available on the Internet by means of search engine technology. However, conventional search engines have certain limitations in dealing with the typical information overload problems. With the application of personalisation techniques search engine providers aim at moderating some of the problems by providing users with information access individualised to their needs. The aim of this paper is twofold. Firstly, techniques for personalisation of eSearch services are introduced. Secondly, the results of an empirical study of the market for eSearch services are presented. Typical examples illustrate eSearch personalisation in practice, and the diffusion of techniques and implications for further research in the domain are discussed

Social and content hybrid image recommender system for mobile social networks

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user