Software tools for conducting bibliometric analysis in science: An up-to-date review

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

i-JEN: Visual interactive Malaysia crime news retrieval system

Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crime-based event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events