323,049 research outputs found
Multimedia mining of collaborative virtual workspaces: an integrative framework for extracting and integrating collaborative process knowledge
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Towards the development of environments for designing visualisation support for visual data mining.
No full textInformating the Curious Negotiator: Automatic News Extraction from the Internet
No full textAbstract. In negotiation, informationacquisition and validationplay an important role in the decisionmaking proc-ess. In this paper we briefly present the frameworkof a smart data mining system for providing contextual informa-tion from the Internet to a negotiation agent. We then present one of its components in more details- an effective automated technique for extracting relevant articles from news web sites, so that they can be used further by the mining agents. Most current techniques experience difficulties to cope with changes in websites structure and for-mats. The proposed extracting process is completely automatic and independentof web site formats. The technique is based on identifying regularities in both format and content of the news web sites. The algorithms are applicable to both single- and multi-documentweb sites. Since invalid URLs can cause errors in data extraction, we also pre-sent a method for the negotiation agent to estimate the validity of the extracted data based on the frequency of the relevant words in the news title. This paper also presents a new procedure for constructing news data sets of given topics. The extracted news data set is further utilised by the parties involved in negotiation. The information re-trieved from the data set can support both humanand automatednegotiators.
An Application of Data Mining in Detection of Myocardial Ischemia utilizing pre and post-stress Echo Images
No full textVisual Data Mining: An Introduction and Overview
No full textIn our everyday life we interact with various information media, which present us with facts and opinions, supported with some evidence, based, usually, on condensed information extracted from data. It is common to communicate such condensed information in a visual form - a static or animated, preferably interactive, visualisation. For example, when we watch familiar weather programs on the TV, landscapes with cloud, rain and sun icons and numbers next to them quickly allow us to build a picture about the predicted weather pattern in a region. Playing sequences of such visualisations will easily communicate the dynamics of the weather pattern, based on the large amount of data collected by many thousands of climate sensors and monitors scattered across the globe and on weather satellites. These pictures are fine when one watches the weather on Friday to plan what to do on Sunday - after all if the patterns are wrong there are always alternative ways of enjoying a holiday. Professional decision making would be a rather different scenario. It will require weather forecasts at a high level of granularity and precision, and in real-time. Such requirements translate into requirements for high volume data collection, processing, mining, modelling and communicating the models quickly to the decision makers. Further, the requirements translate into high-performance computing with integrated efficient interactive visualisation. From practical point of view, if a weather pattern can not be depicted fast enough, then it has no value. Recognising the power of the human visual perception system and pattern recognition skills adds another twist to the requirements - data manipulations need to be completed at least an order of magnitude faster than real-time in order to combine them with a variety of highly interactive visualisations, allowing easy remapping of data attributes to the features of the visual metaphor, used to present the data. In this few steps in the weather domain, we have specified some requirements towards a visual data mining system
Complementing visual data mining with the sound dimension: Sonification of time dependent data
No full textThis chapter explores the extension of visual data mining by adding a sound dimension to the data representation. It presents the results of an early 2001 experiments with sonification of 2D and 3D time series data. A number of sonification means for these experiments have been implemented. The goal of these experiments was to determine how sonification of two and three-dimensional graphs can support and complement or even be an alternative to visually displayed graphs. The research methodology used the triangulation method, combining the automated generation of the sound patterns with two evaluation techniques. The first one included the assessment and evaluation of the sound sequences of the sonified data by the participants in the experiment via a dedicated server. The second one was based on the analysis of an evaluation questionnaire, filled by each participant that performed the tests. The chapter presents the results and the issues raised by the experiments. © 2008 Springer-Verlag Berlin Heidelberg
Assisting Human Cognition in Visual Data Mining
No full textAs discussed in Part 1 of the book in chapter Form-Semantics-Function. A Framework for Designing Visualisation Models for Visual Data Mining the development of consistent visualisation techniques requires systematic approach related to the tasks of the visual data mining process. Chapter Visual discovery of network patterns of interaction between attributes presents a methodology based on viewing visual data mining as a reflection-in-action process. This chapter follows the same perspective and focuses on the subjective bias that may appear in visual data mining. The work is motivated by the fact that visual, though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an understandable alternative to the highly complex statistical approaches) without the ability to understand their applicability and limitations. The chapter presents two strategies addressing the subjective bias: guided cognition and validated cognition, which result in two types of visual data mining techniques: interaction with visual data representations, mediated by statistical techniques, and validation of the hypotheses coming as an output of the visual analysis through another analytics method, respectively
Development and leadership in computer-mediated collaborative groups
No full textComputer-mediated collaboration is an important feature of modern organisational and educational settings. Despite its ever increasing popularity, it is still commonly compared unfavourably with face-to-face collaboration because non-verbal and paralinguistic cues are minimal. Although research on face-to-face group collaboration is well documented, less is known about computer-mediated collaboration.
The initial focus of this thesis was an in-depth analysis of a case study of a computer-mediated collaborative group. The case study was a large international group of volunteer researchers who collaborated on a two-year research project using asynchronous communication (email). This case study was a window on collaborative dialogue in the early 1990s (1992-94) at a time when information and communication technologies were at an early stage of development.
After identifying the issues emerging from this early case study, another case study using technologies and virtual environments developed over the past decade, was designed to further understand how groups work together on a collaborative activity. The second case study was a small group of students enrolled in a unit of study at Murdoch University who collaborated on a series of nine online workshops using synchronous communication (chat room). This case study was a window on collaborative dialogue in the year 2000 when information and communication technologies had developed at a rate which few people envisioned in the early 90s.
The primary aim of the research described in this thesis was to gain a better understanding of how computer-mediated collaborative communities develop and grow. In particular, the thesis addresses questions related to the developmental and leadership characteristics of collaborative groups.
Internet research requires a set of assumptions relating to ontology, epistemology, human nature and methodological approach that differs from traditional research assumptions. A research framework for Internet research - Complementary Explorative Data Analysis (CEDA) - was therefore developed and applied to the two case studies.
The results of the two case studies using the CEDA methodology indicate that computer-mediated collaborative groups are highly adaptive to the aim of the collaborative task to be completed, and the medium in which they collaborate. In the organisational setting, it has been found that virtual teams can devise and complete a collaborative task entirely online. It may be an advantage, but it is certainly not mandatory to have preliminary face-to-face discussions. What is more important is to ensure that time is allowed for an initial period of structuration which involves social interaction to develop a social presence and eventually cohesiveness. In the educational setting, a collaborative community increases pedagogical effectiveness. Providing collaborative projects and interdependent tasks promotes constructivist learning and a strong foundation for understanding how to collaborate in the global workplace. Again, this research has demonstrated that students can collaborate entirely online, although more pedagogical scaffolding may be required than in the organisational setting. The importance of initial social interaction to foster a sense of presence and community in a mediated environment has also been highlighted.
This research also provided greater understanding of emergent leadership in computer-mediated collaborative groups. It was found that sheer volume of words does not make a leader but frequent messages with topic-related content does contribute to leadership qualities.
The results described in this thesis have practical implications for managers of virtual teams and educators in e-learning
Applying web personalization techniques in e-government services
No full textMany E-commerce websites attempt to develop personalized features to encourage users' repetitive visits. Yet, there is less attention about the applications of personalization technologies in E-government services. In this study, we present a classification of personalization techniques. Also, a novel recommendation approach is proposed to improve the existing techniques by the integration of user-based and item-based collaborative filtering recommendation techniques. A recommender system prototype, named Smart Trade Exhibitions Finder, is developed to help companies choosing the right trade exhibitions. The outcome of this study will have tremendous significance in overcoming the drawback of existing recommendation approaches. © 2005. Xuetao Guo, Jie Lu & Simeon Simoff
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