Crocodile farms and management of saltwater crocodiles in Northern Territory: Results of a survey of NT crocodile farmers plus analysis of secondary information

After outlining some relevant background information about the NT crocodile farming industry and explaining the purpose of our survey of NT crocodile farmers conducted in the first half of 2005, this paper reports the results of the survey. The information received from the survey is supplemented by secondary data and by information from secondary sources. This report covers the location of respondents; the size of crocodile farms; farmers’ stated knowledge of and attitudes towards the NT Crocodile Management Plan; the involvement of farms in the harvesting of crocodile eggs and the costs involved; views of crocodile farmers about whether the NT Crocodile Management Plan encourages landholders to conserve crocodiles and their perceptions of the benefits to landholders; predicted production trends and trends in the number of farms operating in NT; economic characteristics of crocodile farms producing in NT including the economic advantages and disadvantages of crocodile farming in NT. Concluding comments provide, amongst other things, an overview of the structure of the crocodile farming industry in the NT gleaned from a consideration of data available from the NT Government’s Department of Business, Industry and Resource Development.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.

Extracting ontological concepts for tendering conceptual structures

It has been argued that beyond software engineering and process engineering, ontological engineering is the third capability needed if successful e-commerce is to be realized. In our experience of building an ontological-based tendering system, we face the problem of building an ontology. In this paper, we demonstrate how to build ontologies in the tendering domain. The ontology life cycle is identified. Extracting concepts from existing resources like on-line catalogs is described. We have reused electronic data interchange (EDI) to build conceptual structures in the tendering domain. An algorithm to extract abstract ontological concepts from these structures is proposed.