During the last decade, use of data mining in organizations has expanded significantly since the data mining methods can be accepted commonly as a useful tool for decision-makers in predicting the future during the decision processes. This role of data mining makes it as the oracle of 21st century. Actually, data mining approaches generally take place in decision support systems as a part of business intelligence. Additionally, the data mining approaches have a great potential to solve forecasting problems encountered in all engineering fields, medical and applied sciences, etc. For this reason, it is not surprise that the expansion trend of the use of data mining approaches is continuing.
Several software vendors and publications, such as Datamation (http://www.datamation.com/dataw/) affirm that all knowledge workers will become data miners in the future. However, there are numerous numbers of data mining tools and applications in the market that require professional data mining background and practice. Likewise, this makes data mining solutions as the software packages for only experts. Second, most of all commercial data mining solutions are implemented with non web-based approaches. (e.g., SPSS Clementine, Microsoft Analysis Services, SAS Enterprise Miner). In other words, although those applications are created to be used in worldwide companies, they are not designed for geographically distributed decision making activities so far. Also these types of tools need significant data mining theory and practical experience to be operated. These reasons complicate decision making processes and create a bottleneck of knowledge workers and decision makers to operate DSS directly. In every cycle of report generation all the participants of the process should be involved in the scenario. Therefore huge amount of time is consumed even for a single report building. Moreover, current data mining tools does not contribute to this problem and they still require exhaustive and time-consuming processes. Although, these tools offer rich options, majority of the decision makers generally need standard and quickly buildable reports. On the other hand, many companies have various numbers of offices in distributed locations. With non-web based solutions collective decision making requires much more effort due to the geographic distances.
In this study, combining data mining oriented decision making and the power of Web by developing a web-based DSS namely ASMINER is aimed. Furthermore, it is designed and implemented to take full advantages of ultimate technologies in Internet and decision support systems. Moreover, independency from location in decision making process is emphasized. In the availability of an Internet connection, online decision making is delivered to the decision makers whenever and wherever they may need it. Likewise, as emphasized in, these kinds of systems can be accessed globally; the location of decision makers is irrelevant and they do not need any installation by offering a web-based DSS.
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(Author: Ahmet Selman Bozkir, Ebru Akcapinar Sezer.