Queensland University of Technology

About: Queensland University of Technology is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 14188 authors who have published 55022 publications receiving 1496237 citations. The organization is also known as: QUT.

Evaluating contractor prequalification data: selection criteria and project success factors

Abstract: A Delphic study investigating the perceived relationship between 20 contractor selection criteria (CSC) currently in use and project success factors (PSFs) in terms of time, cost and quality is described involving a sample of eight experienced construction personnel, including two validators. A consensus of the likely impact of each criterion on time, cost and quality is established in terms of pessimistic, average and optimistic values, which are then converted into expected means and variances via the PERT approach. The ten most and ten least important CSCs are identified and examined for differences and similarities between PSFs. The results show that past failures, financial status, financial stability, credit ratings, experience, ability, management personnel and management knowledge are perceived to be the dominant CSCs affecting all three PSFs, with safety criteria (safety, experience modification rate, OSHA incidence rate, management safety accountability) and the length of time in business being ...

Determining the user intent of web search engine queries

Abstract: Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the classification to the results from our algorithm. This comparison showed that our automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is generally vague or multi-faceted, pointing to the need to for probabilistic classification. We illustrate how knowledge of searcher intent might be used to enhance future Web search engines.