Recent trends in Data-Mining software solutions are going in the direction that data-mining and intelligent analysis solutions are getting cheaper and cheaper. It actually does not only mean the price of the software, but the ways of integration the different solutions are getting easier and easier as well. This provides new possibilities in the classical software and application development as well.
Having the possibility of collecting data about a running application, like application usage, logs, infrastructure data, and having the possibility to analyse them with data-mining tools, provide eventuelly the way of proposing a application improvmenet. Based on data measured and cheap data-mining possibilities a general SharePoint application development process can be defined as follows:
0. Analyse: This one is a classical requirement engineering step, analysing the requrements to set up an initial application. However, the requirements should not only be collected by interviewing the stackeholders, there is a possibility to analyse existing documentations or data, that might also be supported by data-mining.
1. Setup: This is the classical step for setting up the system, planning and installing infrastructure components, developing and delivering custom solutions.
2. Collect data and usage: SharePoint collects pretty many data out of the box, like standard log files, search or usage and health data. As most of these pretty much infrastructure oriented it is important to measure data about certain application usage as well.
3. Analyse: The collected data has to be by different data-mining tools analysed.
4. Propose new structure: based on the data and analysis new application structure can be proposed. The improvement might be only infrastructure oriented to achieve a better performance, however completely modified use cases or business processes can be defined as well.
Repeat from step 1: The process can be actually repeated from the beginning, the new structure or can be again set up and the application usage can be again measured and analysed.
