Virtual Environments (VEs) attempt to give people the illusion of immersion that they are in a computer generated world. VEs allow people to actively participate in a synthetic environment. They range from a single-person running on a single computer, to multiple-people running on several computers connected through a network. When VEs are distributed on multiple computers across a network, we call this a Distributed Virtual Environment (DVE). Virtual Environments can benefit greatly from distributed strategies. A networked VE system based on the Client-Server model is the most commonly used paradigm in constructing DVE systems. In a Client-Server model, data can be distributed on several server computers. The server computers provide services to their own clients via networks. In some client-server models, however, a powerful server is required, or it will become a bottleneck. To reduce the amount of data and traffic maintained by a single server, the servers themselves can be distributed, and the virtual environment can be divided over a network of servers. The system described in this thesis, therefore, is based on the client-server model with multiple servers. This grouping is called a Distributed Virtual Environment System with Multiple- Servers (DVM). A DVM system shows a new paradigm of distributed virtual environments based on shared 3D synthetic environments. A variety of network elements are required to support large scale DVM systems. The network is currently the most constrained resource of the DVM system. Development of networking architectures is the key to solving the DVM challenge. Therefore, a networking architecture for implementing a DVM model is proposed. Finally, a DVM prototype system is described to demonstrate the validity of the modelling and network architecture of a DVM model.

Regardless of the widespread application of analytics to a variety of business measurements, it is noteworthy that the use of people analytics is still no place close where it could be. The main aim of this study is to examine the perceived meaning and benefits of people analytics in selected South African organisations. People analytics is a burning-fresh topic in HR field aiming at using data to make organisational decisions and little has been done in this area especially in the South African context. The study employed qualitative-exploratory design which comprised of 10 senior HR officers from selected organisations in South Africa. From the findings, it shows that the employment of people analytics in South African context is in its early stage and its conception and repercussions are little understood. In addition, there is an accord on its usefulness, however the workforce analytic skills have found to be the major difficulty to foster its successful implementation and adoption by organisations. Because of its qualitative nature, this study had a limitation that it lack representativeness hence the findings cannot be generalised. Research opportunities for future can be quantitative and longitudinal research to objectively ascertain the extent future employability of people analytics.

M.Comm. / The primary objective of the dissertation is: • To explain the design and implementation of effective accounting systems and procedures in a small organisation with a workforce of less than 100 employees. The secondary objectives of the dissertation are as follows: • To explain the nature of current non-value adding accounting systems and procedures whch the small organisation should change or eliminate; • To explain the nature of desired value-adding accounting systems and procedures which should be introduced by the small organisation; • To explain management involvement and participation in the design and implementation of accounting systems and procedures manuals in a small organisation; • To explain the purpose, objectives, need for and basic writing principles which a written accounting procedure of a small organisation must adhere to; • To explain the steps to be followed to design and implement an accounting system and procedure; and • To propose suggestions and recommendations with reference to the design and implementation of accounting systems and procedures. • To determine the minimum and maximum accounting systems and procedures that needed to be implemented in the small organisation; • To identify the shortcomings and positives regarding the information supplied by the current Accounting systems. All of the abovelisted primary and secondary objectives have effect on a small organisation with less than 100 employees, a limited cashflow and which pays limited attention to procedures, due to time constraints. The time constraints relate primarily to the limited workforce.

The technological progress in terms of increasing computational power and growing virtual space to collect data offers great potential for businesses to benefit from data mining applications. Data mining can create a competitive advantage for corporations by discovering business relevant information, such as patterns, relationships, and rules. The role of the human user within the data mining process is crucial, which is why the research area of domain knowledge becomes increasingly important. This thesis investigates the impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining. Domain knowledge is defined as methodological, data and business know-how. The thesis investigates the topic from a new perspective by shifting the focus from a one-sided approach, namely a purely analytic or purely theoretical approach towards a target group-oriented (researcher and practitioner) approach which puts the methodological aspect by means of a scientific guideline in the centre of the research. In order to ensure feasibility and practical relevance of the guideline, it is adapted and applied to the requirements of a practical business case. Thus, the thesis examines the topic from both perspectives, a theoretical and practical perspective. Therewith, it overcomes the limitation of a one-sided approach which mostly lacks practical relevance or generalisability of the results. The primary objective of this thesis is to provide a scientific guideline which should enable both practitioners and researchers to move forward the domain knowledge-driven research for variable derivation on a corporate basis. In the theoretical part, a broad overview of the main aspects which are necessary to undertake the research are given, such as the concept of domain knowledge, the data mining task of classification, variable derivation as a subtask of data preparation, and evaluation techniques. This part of the thesis refers to the methodological aspect of domain knowledge. In the practical part, a research design is developed for testing six hypotheses related to domain knowledge-driven variable derivation. The major contribution of the empirical study is concerned with testing the impact of domain knowledge on a real business data set compared to the impact of a standard and randomly derived data set. The business application of the research is a binary classification problem in the domain of an insurance business, which deals with the prediction of damages in legal expenses insurances. Domain knowledge is expressed through deriving the corporate variables by means of the business and data-driven constructive induction strategy. Six variable derivation steps are investigated: normalisation, instance relation, discretisation, categorical encoding, ratio, and multivariate mathematical function. The impact of the domain knowledge is examined by pairwise (with and without derived variables) performance comparisons for five classification techniques (decision trees, naive Bayes, logistic regression, artificial neural networks, k-nearest neighbours). The impact is measured by two classifier performance criteria: sensitivity and area under the ROC-curve (AUC). The McNemar significance test is used to verify the results. Based on the results, two hypotheses are clearly verified and accepted, three hypotheses are partly verified, and one hypothesis had to be rejected on the basis of the case study results. The thesis reveals a significant positive impact of domain knowledge-driven variable derivation on classifier performance for options of all six tested steps. Furthermore, the findings indicate that the classification technique influences the impact of the variable derivation steps, and the bundling of steps has a significant higher performance impact if the variables are derived by using domain knowledge (compared to a non-knowledge application). Finally, the research turns out that an empirical examination of the domain knowledge impact is very complex due to a high level of interaction between the selected research parameters (variable derivation step, classification technique, and performance criteria).

by Tang Wai-man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 80-85). / Abstracts in English and Chinese. / List of Figures --- p.vii / List of Tables --- p.viii / Chapter CHAPTER1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- E-commerce in Real Estate --- p.2 / Chapter 1.2 --- Need for Negotiation Supports --- p.3 / Chapter 1.3 --- Need for Real-time Information --- p.4 / Chapter 1.4 --- Motivation and Research Contributions --- p.4 / Chapter 1.5 --- Organization of the Thesis --- p.5 / Chapter CHAPTER2 --- LITERATURE REVIEW --- p.7 / Chapter 2.1 --- Electronic Markets --- p.8 / Chapter 2.1.1 --- Classifications --- p.8 / Chapter 2.1.2 --- Evolution of market systems --- p.9 / Chapter 2.1.3 --- Future trends --- p.21 / Chapter 2.2 --- The Middlemen --- p.12 / Chapter 2.2.1 --- Middlemen for HK property market --- p.14 / Chapter 2.2.2 --- Information transparency and efficiency --- p.14 / Chapter 2.2.3 --- Impacts of IT to the middlemen --- p.15 / Chapter 2.3 --- Negotiation Support Systems --- p.16 / Chapter CHAPTER3 --- TRANSACTION PROCESS --- p.18 / Chapter 3.1 --- Preparation --- p.19 / Chapter 3.2 --- Negotiation --- p.20 / Chapter 3.2.1 --- Evaluation --- p.21 / Chapter 3.2.2 --- Adjustment --- p.22 / Chapter 3.2.3 --- Concession --- p.22 / Chapter 3.3 --- Termination --- p.23 / Chapter CHAPTER4 --- MODEL OF NEGOTIATION --- p.26 / Chapter 4.1 --- Negotiation Process --- p.26 / Chapter 4.2 --- Direct Negotiation Without a Middleman --- p.28 / Chapter 4.3 --- Two-Stage Negotiation Approach --- p.29 / Chapter 4.4 --- Property Matching --- p.30 / Chapter 4.4.1 --- Principles --- p.30 / Chapter 4.4.2 --- Process --- p.32 / Chapter 4.4.3 --- Utility function --- p.34 / Chapter 4.5 --- Rating of Counteroffer --- p.38 / Chapter 4.6 --- Recommendation Price --- p.41 / Chapter CHAPTER5 --- INFORMATION NEEDED FOR NEGOTIATION --- p.42 / Chapter 5.1 --- Textual information --- p.43 / Chapter 5.2 --- Numeric data --- p.44 / Chapter 5.3 --- Signalling --- p.45 / Chapter CHAPTER6 --- SYSTEM --- p.47 / Chapter 6.1 --- System Design --- p.47 / Chapter 6.2 --- Overview of the Transaction Process --- p.48 / Chapter 6.2.1 --- Preparation --- p.48 / Chapter 6.2.2 --- Negotiation --- p.49 / Chapter 6.2.3 --- Termination --- p.57 / Chapter 6.3 --- Functionality --- p.52 / Chapter 6.3.1 --- Price Trend --- p.52 / Chapter 6.3.2 --- News --- p.52 / Chapter 6.3.3 --- Property Listing --- p.53 / Chapter 6.3.4 --- Property Searching --- p.53 / Chapter 6.3.5 --- My Property --- p.54 / Chapter 6.3.6 --- Alert --- p.54 / Chapter 6.3.7 --- Negotiation --- p.55 / Chapter CHAPTER7 --- EXPERIMENT AND RESEARCH FINDINGS/EVALUATION --- p.58 / Chapter 7.1 --- Objectives --- p.58 / Chapter 7.2 --- Design of the Experiment --- p.60 / Chapter 7.3 --- Overview of the Experiment --- p.61 / Chapter 7.4 --- Results and Findings --- p.62 / Chapter CHAPTER8 --- CONCLUSIONS AND FUTURE WORKS --- p.65 / Chapter 8.1 --- Conclusions --- p.65 / Chapter 8.2 --- Future Works --- p.67 / APPENDIX A ALGORITHM FOR PROPERTY MATCHING --- p.69 / APPENDIX B1 PRICE TREND --- p.70 / APPENDIX B2 READING AND SENDING NEWS ARTICLES --- p.71 / APPENDIX B3 PROPERTY LISTING --- p.72 / APPENDIX B4 PROPERTY SEARCHING --- p.73 / APPENDIX B5 SEARCHING RESULT --- p.74 / APPENDIX B6 MY PROPERTY --- p.75 / APPENDIX B7 SYSTEM ALERT --- p.76 / APPENDIX B8 ONGOING NEGOTIATIONS --- p.77 / APPENDIX B9 TERMINATED NEGOTIATIONS --- p.78 / APPENDIX B10 SIGNALLING --- p.79 / BIBLIOGRAPHY --- p.80

This research presents results concerning the large scale automatic extraction of pragmatic content from Email, by a system based on a phrase matching approach to Speech Act detection combined with the empirical detection of Speech Act patterns in corpora. The results show that most Speech Acts that occur in such a corpus can be recognized by the approach. This investigation is supported by the analysis of a corpus consisting of 1000 Emails. We describe experimental work to sort a substantial sample of Emails based on their function, which is to say, whether they contain a statement of fact, a request for the recipient to do something, or ask a question. This could be highly desirable functionality for the overburdened Email user, especially if combined with other, more traditional, measures of content relevance and filters based on desirable and undesirable mail sources. We have attempted to apply an lE engine to the extraction of message content located in the message, in part by the use of speech-act detection criteria, e. g. for what it is to be a request for action, under the many possible surface forms that can be used to express that in English, so as to locate the action requested as well as the fact it is a request. The work may have potential practical uses, but here we describe it as the challenge of adapting an IE engine to a somewhat different, task: that of message function detection. The major contributions are: Defining Request Speech Act types. The Request Speech Act is one of the most important functions of an utterance to be recognised, in order to find out the gist of a message. The present work has concentrated on three sub-types of Requests: Requests for Information, Action, and Permission. An algorithm to recognise Speech Acts Patterns found frequently in a domain, together with linguistic rules, make it possible to recognise most of the examples of Requests in the corpus. The results of the evaluation of the system are encouraging and suggest that, in order to avoid long-response time systems, a fast and friendly system is the right approach to implement.

Thesis (MBA)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The telecommunications industry generates vast amounts of data on a daily basis. The exponential growth in this industry has, therefore, increased the amounts of nodes that generates data on a near real-time basis, and the required processing power to process all this information has increased as well. Organisations in different industries have experienced the same growth in information processing, and, in recent years, professionals in the Information Systems (IS) industry have started referring to these challenges as the concept of Big Data (BD). This theoretical research investigated the definition of big data as defined by several leading players in the industry. The theoretical research further focussed on several key areas relating to the big data era: i) Common attributes of big data. ii) How do organisations respond to big data? iii) What are the opportunities that big data provide to organisations? A selecting of case studies are presented to determine what other players in the IS industry does to exploit big data opportunities. The study signified that the concept of big data has emerged due to IT infrastructure struggling to cope with the increased volumes, variety and velocity of data being generated and that organisations are finding it difficult to incorporate the results from new and advanced mining and analytical techniques into their operations in order to extract the maximum value from their data. The study further found that big data impacts each component of the modern day computer based information system and the exploration of several practical cases further highlighted how different organisations have addressed this big data phenomenon in their IS environment. Using all this information, the study investigated the 4C Group business model and identified some key opportunities for this IT vendor in the big data era. As the 4C Group has positioned themselves across the ICT value chain, big data presents several good opportunities to explore in all components of the IS. While training and consulting can establish the 4C Group as a big data knowledgeable vendor, some enhancements to their application software functionalities can provide additional big data opportunities. And as true big data value only originates from the utilization of the data in the daily decision making processes, by offering IaaS the 4C Group can enable their clients to achieve the illusive goal of becoming a data driven organisation.

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