Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.

A 5 year review of information requests to QISU

The Queensland Injury Surveillance Unit (QISU) has been collecting and analysing injury data in Queensland since 1988. QISU data is collected from participating emergency departments (EDs) in urban, rural and remote areas of Queensland. Using this data, QISU produces several injury bulletins per year on selected topics, providing a picture of Queensland injury, and setting this in the context of relevant local, national and international research and policy. These bulletins are used by numerous government and non-government groups to inform injury prevention and practice throughout the state. QISU bulletins are also used by local and state media to inform the general public of injury risk and prevention strategies. In addition to producing the bulletins, QISU regularly responds to requests for information from a variety of sources. These requests often require additional analysis of QISU data to tailor the response to the needs of the end user. This edition of the bulletin reviews 5 years of information requests to QISU.

Planning for smart urban ecosystems : information technology applications for capacity building in environmental decision making

Since the industrial revolution, our world has experienced rapid and unplanned industrialization and urbanization. As a result, we have had to cope with serious environmental challenges. In this context, an explanation of how smart urban ecosystems can emerge, gains a crucial importance. Capacity building and community involvement have always been key issues in achieving sustainable development and enhancing urban ecosystems. By considering these, this paper looks at new approaches to increase public awareness of environmental decision making. This paper will discuss the role of Information and Communication Technologies (ICT), particularly Webbased Geographic Information Systems (Web-based GIS) as spatial decision support systems to aid public participatory environmental decision making. The paper also explores the potential and constraints of these webbased tools for collaborative decision making.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Enabling Open Access to Public Sector Information With Creative Commons Licences : The Australian Experience

This chapter considers how open content licences of copyright-protected materials – specifically, Creative Commons (CC) licences - can be used by governments as a simple and effective mechanism to enable reuse of their PSI, particularly where materials are made available in digital form online or distributed on disk.

Assessing information systems success models : empirical comparison (Research in Progress)

Information System (IS) success may be the most arguable and important dependent variable in the IS field. The purpose of the present study is to address IS success by empirically assess and compare DeLone and McLean’s (1992) and Gable’s et al. (2008) models of IS success in Australian Universities context. The two models have some commonalities and several important distinctions. Both models integrate and interrelate multiple dimensions of IS success. Hence, it would be useful to compare the models to see which is superior; as it is not clear how IS researchers should respond to this controversy.

Information interventions for orienting patients and their carers to cancer care facilities (Protocol)

To assess the effects of information interventions which orient patients and their carers/family to a cancer care facility and the services available in the facility.

The impact of usability on clinician acceptance of a health information system

The two longitudinal case studies that make up this dissertation sought to explain and predict the relationship between usability and clinician acceptance of a health information system. The overall aim of the research study was to determine what role usability plays in the acceptance or rejection of systems used by clinicians in a healthcare context. The focus was on the end users (the clinicians) rather than the views of the system designers and managers responsible for implementation and the clients of the clinicians. A mixed methods approach was adopted that drew on both qualitative and quantitative research methods. This study followed the implementation of a community health information system from early beginnings to its established practice. Users were drawn from different health service departments with distinctly different organisational cultures and attitudes to information and communication technology used in this context. This study provided evidence that a usability analysis in this context would not necessarily be valid when the users have prior reservations on acceptance. Investigation was made on the initial training and post-implementation support together with a study on the nature of the clinicians to determine factors that may influence their attitude. This research identified that acceptance of a system is not necessarily a measure of its quality, capability and usability, is influenced by the user’s attitude which is determined by outside factors, and the nature and quality of training. The need to recognise the limitations of the current methodologies for analysing usability and acceptance was explored to lay the foundations for further research.

The impact of health information technology on the quality of medical and health care : a systematic review

Objective: To systematically review the published evidence of the impact of health information technology (HIT) on the quality of medical and health care specifically clinicians’ adherence to evidence-based guidelines and the corresponding impact this had on patient clinical outcomes. In order to be as inclusive as possible the research examined literature discussing the use of health information technologies and systems in both medical care such as clinical and surgical, and other health care such as allied health and preventive services.----- Design: Systematic review----- Data Sources: Relevant literature was systematically searched on English language studies indexed in MEDLINE and CINAHL(1998 to 2008), Cochrane Library, PubMed, Database of Abstracts of Review of Effectiveness (DARE), Google scholar and other relevant electronic databases. A search for eligible studies (matching the inclusion criteria) was also performed by searching relevant conference proceedings available through internet and electronic databases, as well as using reference lists identified from cited papers.----- Selection criteria: Studies were included in the review if they examined the impact of Electronic Health Record (EHR), Computerised Provider Order-Entry (CPOE), or Decision Support System (DS); and if the primary outcomes of the studies were focused on the level of compliance with evidence-based guidelines among clinicians. Measures could be either changes in clinical processes resulting from a change of the providers’ behaviour or specific patient outcomes that demonstrated the effectiveness of a particular treatment given by providers. ----- Methods: Studies were reviewed and summarised in tabular and text form. Due to heterogeneity between studies, meta-analysis was not performed.----- Results: Out of 17 studies that assessed the impact of health information technology on health care practitioners’ performance, 14 studies revealed a positive improvement in relation to their compliance with evidence-based guidelines. The primary domain of improvement was evident from preventive care and drug ordering studies. Results from the studies that included an assessment for patient outcomes however, were insufficient to detect either clinically or statistically important improvements as only a small proportion of these studies found benefits. For instance, only 3 studies had shown positive improvement, while 5 studies revealed either no change or adverse outcomes.----- Conclusion: Although the number of included studies was relatively small for reaching a conclusive statement about the effectiveness of health information technologies and systems on clinical care, the results demonstrated consistency with other systematic reviews previously undertaken. Widescale use of HIT has been shown to increase clinician’s adherence to guidelines in this review. Therefore, it presents ongoing opportunities to maximise the uptake of research evidence into practice for health care organisations, policy makers and stakeholders.

Evaluating information systems

The book within which this chapter appears is published as a research reference book (not a coursework textbook) on Management Information Systems (MIS) for seniors or graduate students in Chinese universities. It is hoped that this chapter, along with the others, will be helpful to MIS scholars and PhD/Masters research students in China who seek understanding of several central Information Systems (IS) research topics and related issues. The subject of this chapter - ‘Evaluating Information Systems’ - is broad, and cannot be addressed in its entirety in any depth within a single book chapter. The chapter proceeds from the truism that organizations have limited resources and those resources need to be invested in a way that provides greatest benefit to the organization. IT expenditure represents a substantial portion of any organization’s investment budget and IT related innovations have broad organizational impacts. Evaluation of the impact of this major investment is essential to justify this expenditure both pre- and post-investment. Evaluation is also important to prioritize possible improvements. The chapter (and most of the literature reviewed herein) admittedly assumes a blackbox view of IS/IT1, emphasizing measures of its consequences (e.g. for organizational performance or the economy) or perceptions of its quality from a user perspective. This reflects the MIS emphasis – a ‘management’ emphasis rather than a software engineering emphasis2, where a software engineering emphasis might be on the technical characteristics and technical performance. Though a black-box approach limits diagnostic specificity of findings from a technical perspective, it offers many benefits. In addition to superior management information, these benefits may include economy of measurement and comparability of findings (e.g. see Part 4 on Benchmarking IS). The chapter does not purport to be a comprehensive treatment of the relevant literature. It does, however, reflect many of the more influential works, and a representative range of important writings in the area. The author has been somewhat opportunistic in Part 2, employing a single journal – The Journal of Strategic Information Systems – to derive a classification of literature in the broader domain. Nonetheless, the arguments for this approach are believed to be sound, and the value from this exercise real. The chapter drills down from the general to the specific. It commences with a highlevel overview of the general topic area. This is achieved in 2 parts: - Part 1 addressing existing research in the more comprehensive IS research outlets (e.g. MISQ, JAIS, ISR, JMIS, ICIS), and Part 2 addressing existing research in a key specialist outlet (i.e. Journal of Strategic Information Systems). Subsequently, in Part 3, the chapter narrows to focus on the sub-topic ‘Information Systems Success Measurement’; then drilling deeper to become even more focused in Part 4 on ‘Benchmarking Information Systems’. In other words, the chapter drills down from Parts 1&2 Value of IS, to Part 3 Measuring Information Systems Success, to Part 4 Benchmarking IS. While the commencing Parts (1&2) are by definition broadly relevant to the chapter topic, the subsequent, more focused Parts (3 and 4) admittedly reflect the author’s more specific interests. Thus, the three chapter foci – value of IS, measuring IS success, and benchmarking IS - are not mutually exclusive, but, rather, each subsequent focus is in most respects a sub-set of the former. Parts 1&2, ‘the Value of IS’, take a broad view, with much emphasis on ‘the business Value of IS’, or the relationship between information technology and organizational performance. Part 3, ‘Information System Success Measurement’, focuses more specifically on measures and constructs employed in empirical research into the drivers of IS success (ISS). (DeLone and McLean 1992) inventoried and rationalized disparate prior measures of ISS into 6 constructs – System Quality, Information Quality, Individual Impact, Organizational Impact, Satisfaction and Use (later suggesting a 7th construct – Service Quality (DeLone and McLean 2003)). These 6 constructs have been used extensively, individually or in some combination, as the dependent variable in research seeking to better understand the important antecedents or drivers of IS Success. Part 3 reviews this body of work. Part 4, ‘Benchmarking Information Systems’, drills deeper again, focusing more specifically on a measure of the IS that can be used as a ‘benchmark’3. This section consolidates and extends the work of the author and his colleagues4 to derive a robust, validated IS-Impact measurement model for benchmarking contemporary Information Systems (IS). Though IS-Impact, like ISS, has potential value in empirical, causal research, its design and validation has emphasized its role and value as a comparator; a measure that is simple, robust and generalizable and which yields results that are as far as possible comparable across time, across stakeholders, and across differing systems and systems contexts.