Collaboration analysis of World National Library websites via webometric methods

This article aimed to study National Library Websites (NLW) using webometric methods. The in-links and co-links to national library websites were analyzed to study: firstly, the visibility of these National libraries on the web. Secondly, the collaboration on national and international level amongst the studied national libraries websites. This study found that according to the in-link count of 38 national library websites, 3 were extremely popular and we can call them the most visible national library websites as they come below: 1. United States of America (http://www.loc.gov); 2. Australia (http://www.nla.gov.au); 3. United Kingdom (http://www.bl.uk). The results of the study also showed that, there were 5 clusters (2 cross continental and 3 international) in the studied national library websites. On the other hand, the multidimensional scaling map showed 4 major collaboration clusters: 2 cross national (both European) and 2 international (European, Asian, American, Australian). African national library websites were not seen in these clusters. It means that, African national libraries have a little collaboration with others through their websites. However, due to the problems of search engines which are used for data collection in webometric studies, this method needs to be used with cautio

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Cross Validation Of Neural Network Applications For Automatic New Topic Identification

There are recent studies in the literature on automatic topic-shift identification in Web search engine user sessions; however most of this work applied their topic-shift identification algorithms on data logs from a single search engine. The purpose of this study is to provide the cross-validation of an artificial neural network application to automatically identify topic changes in a web search engine user session by using data logs of different search engines for training and testing the neural network. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that it could be possible to identify topic shifts and continuations successfully on a particular search engine user session using neural networks that are trained on a different search engine data log

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed