Problem statement: Mobility prediction is the important issue in Personal Communication Systems (PCS). Mobile users moving logs are stored in data grid located in different locations. Distributed data mining algorithm is applied on this moving logs to generate the mobility pattern of mobile users. As new moving logs are added to the data grid, existing mobility pattern becomes invalid and it should be updated. One of the existing work to derive the new mobility pattern is re-executing the algorithm from scratch results in excessive computation. Approach: We had designed new incremental algorithm by maintaining infrequent mobility patterns, which avoids unnecessary scan of full database. Incremental data mining algorithm taken lesser time to compute new mobility patterns. The discovered location patterns can be used to provide various location based services to the mobile user by the application server in mobile computing environment. Data grid provided geographically distributed database for computational grid which implements incremental data mining algorithm. We built data grid system on a cluster of workstation using open source globus toolkit 4.0 and Message Passing Interface extended with Grid Services (MPICH-G2). Results: The experiments were conducted on original data sets and data were added incrementally and the computation time was recorded for each data sets. The performance improvement for increment size of 100 K was about 55% for 0.20% support count and it is increased to 60% for 0.25% support count. The performance is increased about 65% for the support count 0.30%. Conclusion: We analyzed our results with various sizes of data sets and the proof shows the time taken to generate mobility pattern by incremental mining algorithm is less than re-computing approach. In future the execution time can further be reduced by balancing the workload of grid nodes.

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In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper focuses on the design of effective stock price prediction using dragonfly algorithm (DFA) based deep belief network (DBN) model. The DFA-DBN technique aims to properly determine the sentiments of the investors from Twitter data and forecast future stock prices. From Twitter data, the DFA-DBN technique attempts to accurately determine the sentiments of investors, as well as predict future stock prices. For accurate stock price prediction, the proposed DFA-DBN model includes the development of a DBN model. The proposed DFA-DBN model involves the design of DBN model for accurate prediction of stock prices. Besides, the hyperparameter tuning of the DBN technique is performed by utilize of DFA and thereby boosts the overall prediction performance. For validating the supremacy of the DFA-DBN model, a comprehensive experimental analysis takes place and the results demonstrate the accurate prediction of stock prices. A predicted DFA-DBN algorithm with a higher accuracy of 94.97 percent is available. On the basis of the data in the tables and figures above, the DFA-DBN approach has been demonstrated to be an effective instrument for anticipating stock price fluctuations.

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In this paper, we propose a new SQL based incremental distributed algorithm for predicting the next location of a mobile user in a mobile web environments. Parallel and Distributed data mining algorithm is applied on moving logs stored in geographically distributed data grid to generate the mobility pattern, which provides various location based services to the mobile users. One of the existing works for deriving mobility pattern is re-executing the algorithm from scratch results in excessive computation. In our work, we have designed new incremental algorithm by maintaining infrequent mobility patterns, which avoids unnecessary scan of full database. We built data grid system on a cluster of workstation using open source Globus Toolkit (GT) and Message Passing Interface extended with Grid Services . The experiments were conducted on original data sets with incremental addition of data and the computation time was recorded for each data sets. We analyzed our results with various sizes of data sets and it shows the time taken to generate mobility pattern by incremental mining algorithm is less than re-computing approach.