A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfeld of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specifc. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifcally for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry

Design rainfall estimation: comparison between GEV and LP3 distributions and at-site and regional estimates

Design rainfall, often known as intensity–frequency–duration (IFD) data, is an important input in rainfall runoff modelling exercise. IFD data are derived by fitting a probability distribution to observed rainfall data. Although there are many researches on IFD curves in the literature, there is a lack of systematic comparison among the IFD curves obtained by different distributions and methods. This study compares the latest IFD curves in Australia, published in 2013, as a part of the new Australian rainfall and runoff (ARR) with the at-site IFD curves to examine the expected degree of variation between the at-site and regional IFD data. Ten pluviography stations from eastern New South Wales (NSW) are selected for this study. The IFD curves generated by the two most commonly adopted probability distributions, generalised extreme value (GEV) and log Pearson type 3 (LP3) distributions are also compared. Empirical and polynomial regression methods in smoothing the IFD curves are compared. Based on the three goodness-of-fit tests, it has been found that both GEV and LP3 distributions fit the annual maximum rainfall data (at 1% significance level) for the ten selected stations. The developed IFD curves based on the second-degree polynomial present better fitting than the empirical method. It has been found that the ARR87 and ARR13 IFD curves are generally higher than the at-site IFD curves derived here. The median difference between the at-site and regional ARR-recommended IFD curves is in the range of 13–19%. It is expected that the outcomes of this research will provide better guidance in selecting the correct IFD data for a given application in NSW. The methodology developed here can be adapted to other parts of Australia and other countries