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The main purpose of this thesis was to research and select the most usable ways of improving web application scalability especially during high traffic peaks. The most concrete meter for the application scalability was defined to be the maximum throughput of the application. From the general web application performance guidelines, caching was seen as the most promising solution for improving the maximum throughput of the application. Because of this, a couple of advanced caching techniques were implemented into a real life application: caching of content fragments with Edge Side Includes (ESI) markup language and caching of user group specific HTTP responses with a Servlet filter. After implementing the Edge Side Includes support into the application, the maximum throughput of the application front page was measured to be roughly 3 times the original. Though this was considered to be a good improvement, it seemed clear that the rendering process of the front page response skeleton was unnecessarily heavy at this point, therefore limiting the true potential of the ESI solution. In order to improve the performance of the response skeleton rendering process, a Servlet filter was configured to cache the different response skeletons to a local cache for two seconds, thus reducing the need for processing in the application especially during high traffic peaks. With this improvement, the maximum throughput of the application front page now reached approximately tenfold value compared to the original version. In addition, the maximum total throughput of the application was measured under a realistic simulation of a high traffic peak before and after the optimization steps. As a result, the optimized application seemed to perform approximately 2.3 times better than the original. In the end, the thesis project can be considered successful due to the noticeable improvement in the application scalability. Furthermore, various additional improvement possibilities were discovered during the project, which could help to improve the application scalability even more in the future.
Interpreting data collected from the mining rigs provides challenge which is alleviated with help of visualization techniques. A good visualization shows all the relevant information at a glance and helps make decisions. In mining the information can be used for example to follow the concentration of the mined minerals, adjust drill settings according the rock properties, track wear of drill bits, make drill plans, detonation plans and bolting plans in order to ensure efficient and safe work. This thesis concentrates on finding the best ways to present the data from drilling machines. It has three main points: 3D visualization for analyzing large batches of collected drill data, real time drill visualization for use in the drill equipment during and immediately after the drilling and interpolation to find ways to interpret and expand from the data available. All the features in this thesis are built into a DrillGraph software, to be used as a basis for actual products.