The cost of recovery protocols in web-based database systems

The cost of recovery protocols is important with respect to system performance during normal operation and failure in terms of overhead, and time taken to recover failed transactions. The cost of recovery protocols for web database systems has not been addressed much. In this paper, we present a quantitative study of cost of recovery protocols. For this purpose, we use an experiment setup to evaluate the performance of two recovery algorithms, namely the, two-phase commit algorithm and log-based algorithm. Our work is a step towards building reliable protocols for web database systems.

There are always two sides to a story: the use of social dramas as a mode of data analysis in information systems

With the continually evolving social nature of information systems research there is a need to identify different “modes of analysis” (Myers, 1997) to uncover our understanding of the complex, messy and often chaotic nature of human factors. One suggested mode of analysis is that of social dramas, a tool developed in the anthropological discipline by Victor Turner. The use of social dramas also utilises the work by Goffman (1959; 1997) and enables the researcher to investigate events from the front stage, reporting obvious issues in systems implementation, and from the back stage, identifying the hidden aspects of systems implementation and the underpinning discourses. A case study exploring the social dramas involved in systems selection and implementation has been provided to support the use of this methodological tool.

The portal as information broker

The term information broker is widely used in the area of library and information science to describe a middle agent who deals in information as a commodity, enabling customers to gain more efficient access to quality data. The role of this middle agent is described as “information retrieval and information organisation” (Rugge & Glossbrenner, 1995). The role of the broker is to bring additional organisation to the market by lowering search costs (Palmer & Lindemann, 2003).

Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research.

Source-based filtering scheme against DDOS attacks

IP address spoofing is employed by a lot of DDoS attack tools. Most of the current research on DDoS attack packet filtering depends on cooperation among routers, which is hard to achieve in real campaigns. Therefore, in the paper, we propose a novel filtering scheme based on source information in this paper to defend against various source IP address spoofing. The proposed method works independently at the potential victim side, and accumulates the source information of its clients, for instance, source IP addresses, hops from the server during attacks free period. When a DDoS attack alarm is raised, we can filter out the attack packets based on the accumulated knowledge of the legitimate clients. We divide the source IP addresses into n(1 ≤ n ≤ 32) segments in our proposed algorithm; as a result, we can therefore release the challenge storage and speed up the procedure of information retrieval. The system which is proposed by us and the experiments indicated that the proposed method works effectively and efficiently.

A 3-D locomotion biomimetic robot fish with information relay

In this paper, a biomimetic robot fish is designed. It has a biomimetic tail to simulate the carangiform tail, a barycenter-adjustor for descending/ascending motions, multiple sensors, and it may communicate with the outside by an information relay system on water. Combining the movements of the tail and the structure for descending and ascending, the robot fish can simulate the swimming of real fish in water and a biomimetic motion library is established. Finally, the prototype and experiments are given.

Reducing cache lookup and publish overhead through locality awareness in P2P web caching

Peer-to-Peer (P2P) Web caching has been a hot research topic in recent years as it can create scalable and robust designs for decentralized Internet-scale applications. However, many P2P Web caching systems suffer expensive overheads such as lookup and publish messages, and lack of locality awareness. In this paper we present the development of a locality aware P2P cache system to overcome these limitations by using routing table locality, aggregation and soft state. The experiments show that our P2P cache system improves the performance of index operations through the reduction of the amount of information processed by nodes, the reduction of the number of index messages sent by nodes, and the improvement of the locality of cache pointers.

Building information modelling project decision support framework

Building Information Modelling (BIM) is an information technology [IT] enabled approach to managing design data in the AEC/FM (Architecture, Engineering and Construction/ Facilities Management) industry. BIM enables improved interdisciplinary collaboration across distributed teams, intelligent documentation and information retrieval, greater consistency in building data, better conflict detection and enhanced facilities management. Despite the apparent benefits the adoption of BIM in practice has been slow. Workshops with industry focus groups were conducted to identify the industry needs, concerns and expectations from participants who had implemented BIM or were BIM “ready”. Factors inhibiting BIM adoption include lack of training, low business incentives, perception of lack of rewards, technological concerns, industry fragmentation related to uneven ICT adoption practices, contractual matters and resistance to changing current work practice. Successful BIM usage depends on collective adoption of BIM across the different disciplines and support by the client. The relationship of current work practices to future BIM scenarios was identified as an important strategy as the participants believed that BIM cannot be efficiently used with traditional practices and methods. The key to successful implementation is to explore the extent to which current work practices must change. Currently there is a perception that all work practices and processes must adopt and change for effective usage of BIM. It is acknowledged that new roles and responsibilities are emerging and that different parties will lead BIM on different projects. A contingency based approach to the problem of implementation was taken which relies upon integration of BIM project champion, procurement strategy, team capability analysis, commercial software availability/applicability and phase decision making and event analysis. Organizations need to understand: (a) their own work processes and requirements; (b) the range of BIM applications available in the market and their capabilities (c) the potential benefits of different BIM applications and their roles in different phases of the project lifecycle, and (d) collective supply chain adoption capabilities. A framework is proposed to support organizations selection of BIM usage strategies that meet their project requirements. Case studies are being conducted to develop the framework. The results of the preliminary design management case study is presented for contractor led BIM specific to the design and construct procurement strategy.

A hitchhiker's guide to assessing young people's physical activity : deciding what method to use

Researchers and practitioners interested in assessing physical activity in  children are often faced with the dilemma of what instrument to use. While there is a plethora of physical activity instruments to choose from, there is currently no guide regarding the suitability of common assessment instruments. The purpose of this paper is to provide a user’s guide for selecting physical activity assessment instruments appropriate for use with children and adolescents. While recommendations regarding specific instruments are not provided, the guide offers information about key attributes and considerations for the use of eight physical activity assessment approaches: heart rate monitoring; accelerometry; pedometry; direct observation; self-report; parent report; teacher report; and diaries/logs. Attributes of instruments and other factors to be considered in the selection of assessment instruments include: population (age); sample size; respondent burden; method/delivery mode; assessment time frame; physical activity information required (data output); data management; measurement error; cost (instrument and administration) and other limitations. A decision flow chart has been developed to assist researchers and practitioners to select an appropriate method of assessing physical activity. Five real-life scenarios are presented to illustrate this process in light of key instrument attributes. It is important that researchers, practitioners and policy makers understand the strengths and limitations of different methods of assessing physical activity, and are guided on selection of the most appropriate instrument/s to suit their needs.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Doctoral research and scholarly communication : candidates, supervisors and information literacy

This study investigates information literacy and scholarly communication within the processes of doctoral research and supervision at a distance. Both doctoral candidates and supervisors acknowledge information literacy deficiencies and it is suggested that disintermediation and the proliferation of information may contribute to those deficiencies. Further to this, the influence of pedagogic continuity—particularly in relation to the information seeking behaviour of candidates—is investigated, as is the concomitant aspect of how doctoral researchers practise scholarly communication. The well-documented and enduring problem for candidates of isolation from the research cultures of their universities is also scrutinised. The contentious issue of more formally involving librarians in the doctoral process is also considered, from the perspective of candidates and supervisors. Superimposed upon these topical and timely issues is the theoretical framework of adult learning theory, in particular the tenets of andragogy. The pedagogical-andragogical orientation of candidates and supervisors is established, demonstrating both the differences and similarities between candidates and supervisors, as are a number of independent variables, including a comparison of on-campus and off-campus candidates. Other independent variables include age, gender, DETYA (Department of Education, Training & Youth Affairs) category, enrolment type, stage of candidature, employment and status, type of doctorate, and English/non-English speaking background. The research methodology uses qualitative and quantitative techniques encompassing both data and methodological triangulation. The study uses two sets of questionnaires and a series of in-depth interviews with a sample of on-campus and off-campus doctoral candidates and supervisors from four Australian universities. Major findings include NESB candidates being more pedagogical than their ESB counterparts, and candidates and supervisors from the Sciences are more pedagogical than those from Arts, Humanities and Social Sciences, or Education. Candidates make a transition from a more dependent and pedagogically oriented approach to learning towards more of an independent and andragogical orientation over the duration of their candidature. However, over tune both on-campus and off-campus candidates become more isolated from the research cultures of their universities, and less happy with support received from their supervisors in relation to their literature reviews. Ill The study found large discrepancies in perception between the support supervisors believed they gave to candidates in relation to the literature review, and the support candidates believed they received. Information seeking becomes easier over time, but candidates face a dilemma with the proliferation of information, suggesting that disintermediation has exacerbated the challenges of evaluation and organisation of information. The concept of pedagogic continuity was recognised by supervisors and especially candidates, both negative and positive influences. The findings are critically analysed and synthesised using the metaphor of a scholarly 'Club' of which obtaining a doctorate is a rite of passage. Recommendations are made for changes in professional practice, and topics that may warrant further research are suggested.

Improve construction knowledge management system by using the soft system methodology

The construction industry has a poor record in the management of its knowledge and results into huge wastage of resources and detrimental effect to quality. Research has shown that data and information management system plus knowledge management are a critical part of today's project management practice for construction projects. Few people will deny that 'quality information' and 'useful knowledge' are extremely important to any decision-making. However, the current processes of handling information and knowledge in the construction industry and increasingly costly. One of the major reasons is the nature of this industry is not conducive to good knowledge management and the traditional data/information systems used in the industry has long been critisized. It is very common that information is often duplicated, inconsistent and not current. In turn, making knowledge becomes difficult to manage properly. Project managers have in the past found it very difficult to source and analyse data in order to make sound decisions. This paper is part of a doctoral research project which summarizes three exploratory surveys; namely ERP system, Partnering strategy and Leadership impact of a knowledge management system in a construction company. Those findings are described by using the Soft Systems Methodology (SSM) which later becomes the basis for actions research. SSM is useful to reveal complexities of the knowledge management situations that occur in construction industry. The first stage was to conduct interviews of the different practices in knowledge and reporting process. Then, the SSM rich picture was developed to present the problematic areas including difficulties in inputting data to enable the knowledge platform in place. The research then develops root definition and CATWOE, and a conceptual model was formed. Interviewees were conducted with structured questions to identify prioritized actions and activities that can be undertaken to improve and manage the knowledge platform.

Image retrieval based on bag of images

Conventional relevance feedback schemes may not be suitable to all practical applications of content-based image retrieval (CBIR), since most ordinary users would like to complete their search in a single interaction, especially on the web search. In this paper, we explore a new approach to improve the retrieval performance based on a new concept, bag of images, rather than relevance feedback. We consider that image collection comprises of image bags instead of independent individual images. Each image bag includes some relevant images with the same perceptual meaning. A theoretical case study demonstrates that image retrieval can benefit from the new concept. A number of experimental results show that the CBIR scheme based on bag of images can improve the retrieval performance dramatically.

Nonnegative shared subspace learning and its application to social media retrieval

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.

Detection of setting and subject information in documentary video

Interpretation of video information is a difficult task for computer vision and machine intelligence. In this paper we examine the utility of a non-image based source of information about video contents, namely the shot list, and study its use in aiding image interpretation. We show how the shot list may be analysed to produce a simple summary of the 'who and where' of a documentary or interview video. In order to detect the subject of a video we use the notion of a 'shot syntax' of a particular genre to isolate actual interview sections.