Instantaneous Database Access

The biggest threat to any business is a lack of timely and accurate information. Without all the facts, businesses are pressured to make critical decisions and assess risks and opportunities based largely on guesswork, sometimes resulting in financial losses and missed opportunities. The meteoric rise of Databases (DB) appears to confirm the adage that “information is power”, but the stark reality is that information is useless if one has no way to find what one needs to know. It is more accurate perhaps to state that, “the ability to find information is power”. In this paper we show how Instantaneous Database Access System (IDAS) can make a crucial difference by pulling data together and allowing users to summarise information quickly from all areas of a business organisation

Adaptively Constructing the Query Interface for Meta-Search Engines

With the exponential growth of information on the Internet, current information integration systems have become more and more unsuitable for this “Internet age ” due to the great diversity among sources. This paper presents a constraint-based query user interface model, which can be applied to the construction of dynamically generated adaptive user interfaces for meta-search engines

Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

Election Data Visualisation

Visualisations of election data produced by the mass media, other organisations and even individuals are becoming increasingly available across a wide variety of platforms and in many different forms. As more data become available digitally and as improvements to computer hardware and software are made, these visualisations have become more ambitious in scope and more user-friendly. Research has shown that visualising data is an extremely powerful method of communicating information to specialists and non-specialists alike. This amounts to a democratisation of access to political and electoral data. To some extent political science lags behind the progress that has been made in the field of data visualisation. Much of the academic output remains committed to the paper format and much of the data presentation is in the form of simple text and tables. In the digital and information age there is a danger that political science will fall behind. This thesis reports on a number of case studies where efforts were made to visualise election data in order to clarify its structure and to present its meaning. The first case study demonstrates the value of data visualisation to the research process itself, facilitating the understanding of effects produced by different ways of estimating missing data. A second study sought to use visualisation to explain complex aspects of voting systems to the wider public. Three further case studies demonstrate the value of collaboration between political scientists and others possessing a range of skills embracing data management, software engineering, broadcasting and graphic design. These studies also demonstrate some of the problems that are encountered when trying to distil complex data into a form that can be easily viewed and interpreted by non-expert users. More importantly, these studies suggest that when the skills balance is correct then visualisation is both viable and necessary for communicating information on elections