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Performance tuning of Database Management Systems(DBMS) is both complex and challenging as it involves identifying and altering several key performance tuning parameters. The quality of tuning and the extent of performance enhancement achieved greatly depends on the skill and experience of the Database Administrator(DBA). As neural networks have the ability to adapt to dynamically changing inputs and also their ability to learn makes them ideal candidates for employing them for tuning purpose. In this paper, a novel tunig algorithm based on neural network estimated tuning parameters is presented. The key performance indicators are proactively monitored and fed as input to the Neural Network and the trained network estimates the suitable size of the buffer cache, shared pool and redo log buffer size. The tuner alters these tuning parameters using the estimated values using a rate change computing algorithm. The preliminary results show that the proposed method is effective in improving the query response time for a variety of workload types.

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The amount of data from users in Hindi language is tremendously increasing on social media, blogs, online forums due to which Sentiment analysis of Indian languages has turned out to be a predominant research area. Lexicon based analysis is one of the techniques that can be used for analyzing sentiments. While using the lexicon-based sentiment analysis, ambiguous words could be an issue. This paper proposes a graph based Lesk approach to handle the word sense disambiguation issue and tries to enhance and improve the lexicon approach. Experiments performed to evaluate the performance of the proposed algorithm shows that the performance of lexicon-based sentiment analysis is increased significantly by using the graph based Lesk approach.