Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

My media studies : the history and future of ideas

In the context of a multi-paper special issue of TVNM on the future of media studies, this paper traces the tradition of ‘active audience’ theory in TV scholarship, arguing that it has much to offer in the study of new digital media, especially an approach to user-created content and dynamics of change. The paper argues for a ‘cultural science’ approach to ‘active audiences’ in order to analyse and understand how non-professionals and consumers contribute to the growth of knowledge in complex open media systems.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Trust and strong ties in the selection and evaluation of business advisors by small firms

Principal Topic A small firm is unlikely to possess internally the full range of knowledge and skills that it requires or could benefit from for the development of its business. The ability to acquire suitable external expertise - defined as knowledge or competence that is rare in the firm and acquired from the outside - when needed thus becomes a competitive factor in itself. Access to external expertise enables the firm to focus on its core competencies and removes the necessity to internalize every skill and competence. However, research on how small firms access external expertise is still scarce. The present study contributes to this under-developed discussion by analysing the role of trust and strong ties in the small firm's selection and evaluation of sources of external expertise (henceforth referred to as the 'business advisor' or 'advisor'). Granovetter (1973, 1361) defines the strength of a network tie as 'a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding) and the reciprocal services which characterize the tie'. Strong ties in the context of the present investigation refer to sources of external expertise who are well known to the owner-manager, and who may be either informal (e.g., family, friends) or professional advisors (e.g., consultants, enterprise support officers, accountants or solicitors). Previous research has suggested that strong and weak ties have different fortes and the choice of business advisors could thus be critical to business performance) While previous research results suggest that small businesses favour previously well known business advisors, prior studies have also pointed out that an excessive reliance on a network of well known actors might hamper business development, as the range of expertise available through strong ties is limited. But are owner-managers of small businesses aware of this limitation and does it matter to them? Or does working with a well-known advisor compensate for it? Hence, our research model first examines the impact of the strength of tie on the business advisor's perceived performance. Next, we ask what encourages a small business owner-manager to seek advice from a strong tie. A recent exploratory study by Welter and Kautonen (2005) drew attention to the central role of trust in this context. However, while their study found support for the general proposition that trust plays an important role in the choice of advisors, how trust and its different dimensions actually affect this choice remained ambiguous. The present paper develops this discussion by considering the impact of the different dimensions of perceived trustworthiness, defined as benevolence, integrity and ability, on the strength of tie. Further, we suggest that the dimensions of perceived trustworthiness relevant in the choice of a strong tie vary between professional and informal advisors. Methodology/Key Propositions Our propositions are examined empirically based on survey data comprising 153 Finnish small businesses. The data are analysed utilizing the partial least squares (PLS) approach to structural equation modelling with SmartPLS 2.0. Being non-parametric, the PLS algorithm is particularly well-suited to analysing small datasets with non-normally distributed variables. Results and Implications The path model shows that the stronger the tie, the more positively the advisor's performance is perceived. Hypothesis 1, that strong ties will be associated with higher perceptions of performance is clearly supported. Benevolence is clearly the most significant predictor of the choice of a strong tie for external expertise. While ability also reaches a moderate level of statistical significance, integrity does not have a statistically significant impact on the choice of a strong tie. Hence, we found support for two out of three independent variables included in Hypothesis 2. Path coefficients differed between the professional and informal advisor subsamples. The results of the exploratory group comparison show that Hypothesis 3a regarding ability being associated with strong ties more pronouncedly when choosing a professional advisor was not supported. Hypothesis 3b arguing that benevolence is more strongly associated with strong ties in the context of choosing an informal advisor received some support because the path coefficient in the informal advisor subsample was much larger than in the professional advisor subsample. Hypothesis 3c postulating that integrity would be more strongly associated with strong ties in the choice of a professional advisor was supported. Integrity is the most important dimension of trustworthiness in this context. However, integrity is of no concern, or even negative, when using strong ties to choose an informal advisor. The findings of this study have practical relevance to the enterprise support community. First of all, given that the strength of tie has a significant positive impact on the advisor's perceived performance, this implies that small business owners appreciate working with advisors in long-term relationships. Therefore, advisors are well advised to invest into relationship building and maintenance in their work with small firms. Secondly, the results show that, especially in the context of professional advisors, the advisor's perceived integrity and benevolence weigh more than ability. This again emphasizes the need to invest time and effort into building a personal relationship with the owner-manager, rather than merely maintaining a professional image and credentials. Finally, this study demonstrates that the dimensions of perceived trustworthiness are orthogonal with different effects on the strength of tie and ultimately perceived performance. This means that entrepreneurs and advisors should consider the specific dimensions of ability, benevolence and integrity, rather than rely on general perceptions of trustworthiness in their advice relationships.

A review of non-traditional occupational therapy practice placement education : a focus on role-emerging and project placements

Practice placement education has been recognised as an integral and critical component of the training of occupational therapy students. Although there is an extensive body of literature on clinical education and traditional practice placement education models, there has been limited research on alternative placements.-------- This paper reviews the literature on various practice placement education models and presents a contemporary view on how it is currently delivered. The literature is examined with a particular focus on the increasing range of practice placement education opportunities, such as project and role-emerging placements. The drivers for non-traditional practice placement education include shortages of traditional placement options, health reform and changing work practices, potential for role development and influence on practice choice. The benefits and challenges of non-traditional practice placement education are discussed, including supervision issues, student evaluation, professional and personal development and the opportunity to practise clinical skills.--------- Further research is recommended to investigate occupational therapy graduates' perceptions of role-emerging and project placements in order to identify the benefits or otherwise of these placements and to contribute to the limited body of knowledge of emerging education opportunities.

Succession management as a knowledge management strategy

One of the most critical issues for building innovation capacity in organisations is the acquisition and maintenance of knowledge. As knowledge is the basis of human capital, then the ability to attract, retain and engage talent is argued to be an important element of innovation. By attracting and retaining good staff, the organisation is retaining organisational knowledge which is necessary particularly for exploitation of current capabilities, but will also contribute to capacity for exploration for future innovation. This paper addresses the importance of retaining and developing staff as a critical issue for knowledge management and addresses the issue of retaining talent through effective succession management practices. The findings from an exploratory study into current practices in the Australian rail sector, provides further insight into the potentially critical issues for the effective use of succession management as a knowledge management and employee retention tool for building innovation capacity.

Systematising knowledge management in projects

Purpose – The purpose of this paper is to provide a practicable systems-based approach to knowledge management (KM) in a project environment, to encourage organisations to unlock the value in their review processes. It relies on knowledge capture and storage at decision review points, to enrich individual, team and organisational learning during the project life cycle. The project's phases are typically represented horizontally with deliverables (objectives) or project "promises" as the desirable outcomes. The purpose of this paper is to give expression through introducing a vertical dimension to facilitate the KM process. A model is proposed that conceptualises project-specific knowledge drawing on and feeding into the organisation's knowledge management system (KMS) at tactical and strategic levels. Design/methodology/approach – This conceptual paper links concepts from systems theory with KM, to produce a model to identify, collate, and optimise project-based knowledge and integrate it into the management process. Findings – The application of the system theory approach enriches the knowledge generated by a project, and feeds it into the next phase of that project. At the same time, it contributes to the individual's and project team's KM, specifies possible courses of action, together with risks, costs and benefits and thus it expands the organisation's higher level KMS. Research limitations/implications – The concept suggests that the knowledge capture, storage and sharing process may best be undertaken holistically, in view of the systems relationships between the tasks. Systems theory structures this process. Research opportunities include studying the interfaces between levels of KM, in relation to the project's progress. Practical implications – Reconceptualisation of the project as a knowledge creation process may improve the project's progress as well as add to the individual's, project team's, and wider organisation's knowledge base. An example is given. Originality/value – This paper illuminates the broader potential of under-utilised opportunities in well-known management approaches to add dimension to the business project, of knowledge creation, storage and sharing.

Managing knowledge in an outsourcing environment

What an organisation does versus what it out-sources to the market is a classic boundaries of the firm question that has previously been dominated by efficiency arguments. However, a knowledge-based view suggests these boundaries are integral to the ability of a firm to deploy existing knowledge stocks efficiently, as well as develop new knowledge through learning that will drive future competitiveness. Furthermore, the nature of these boundaries, in respect of their permeability is critical in understanding the likelihood of knowledge flowing into and out of the organisation. Using these concepts, we present a case study of Main Roads Western Australia to illustrate how these principles have allowed it to start rebuilding its internal capabilities through repositioning its operational boundaries and via ensuring their boundaries are highly porous as they move more major projects into alliance contracts.

Documenting reproduction and inequality : revisiting Jean Anyon’s “Social class and school knowledge”

Jean Anyon’s (1981) “Social class and school knowledge” was a landmark work in North American educational research. It provided a richly detailed qualitative description of differential, social-class-based constructions of knowledge and epistemological stance. This essay situates Anyon’s work in two parallel traditions of critical educational research: the sociology of the curriculum and classroom interaction and discourse analysis. It argues for the renewed importance of both quantitative and qualitative research on social reproduction and equity in the current policy context.

Extending knowledge management across the supply chains in the construction industry : knowledge sharing in construction supply chains

Supply chain management and knowledge management have emerged as two distinct business philosophies in the last decade. Both are making rapid inroads into the construction industry. The premise of this paper is that knowledge management would make it possible for all the trading partners in a supply chain to reap benefits. Current research in knowledge management in the construction industry is generally targeting those big organisations that are main contractors. This has restricted the scope of knowledge management, and limits the benefits to a few, rather than the whole industry. If the construction industry as a whole is to prosper and improve its productivity, strategies for knowledge management strategy at the industry level must be established. This paper argues the case for extending the scope of knowledge management across the full extent of the supply chain, and attempts to identify the benefits that may arise out of sharing knowledge across the supply chain.

Five case studies applying soft systems methodology to knowledge management

Construction projects are faced with a challenge that must not be underestimated. These projects are increasingly becoming highly competitive, more complex, and difficult to manage. They become problems that are difficult to solve using traditional approaches. Soft Systems Methodology (SSM) is a systems approach that is used for analysis and problem solving in such complex and messy situations. SSM uses “systems thinking” in a cycle of action research, learning and reflection to help understand the various perceptions that exist in the minds of the different people involved in the situation. This paper examines the benefits of applying SSM to problems of knowledge management in construction project management, especially those situations that are challenging to understand and difficult to act upon. It includes five case studies of its use in dealing with the confusing situations that incorporate human, organizational and technical aspects.

Knowledge channelization and innovation

This paper examines knowledge management and innovation in the Australian Construction Industry. A conceptual model is presented, based upon analysis of the literature and a series of preliminary construction industry interviews. Extensive knowledge management (KM) research has focused upon types of knowledge contained within specific organizational settings. However, we argue that a crucial missing link in KM research concerns the interface between flows of knowledge from external sources of innovations and its channelization in and out, and between organizations. This interface, regulating and facilitating knowledge from external sources of innovation into the organisation, operates under the influence of two main forces visualized as “pulling” and “pushing” forces in the model presented in this paper. The premise of the model lies in a hypothesis that as an organization changes itself into a more mature, learning organization (LO) over time, knowledge flows into it through “pull” rather than “push” forces. We conclude that a successful knowledge management initiative installs a learning and knowledge sharing culture, which is easily adaptable to new learning offering little resistance to new knowledge that flows into the organisation. The model bridges the gap between research and its application in construction practice.