A P system is a computability model which is biochemically inspired, it is a general distributed model, highly parallel, nondeterministic, based on the notion of a membrane structure. Till this moment, there is no exact idea about the real implementation of P systems. P systems are used in solving NP-complete problems in polynomial time, but with building the whole exponential search space. Cloud computing assume infinite memory and infinite processing power. This paper proposes an algorithm that uses the cloud resources in a fully parallel manner as a step towards P systems implementation, the nondeterminism property of P systems is certainly not maintained. The paper used the SAT problem as the case study.

Stock market prediction is one of the most attractive places for investment. It is one of the most prevalent topics for research studies and commercial applications because of its dynamic and unpredictable nature. One of the most important sources of data can be found in social media such as Twitter or Facebook. This is because the data reveals to a great extent the social mood of the public and their future behavior, if the data is well analyzed using intelligent financial web-mining and stock prediction systems. This framework builds on previous researchers’ efforts to analyze and determine social media impact on stock market behavior, and hence predict prices movement. We found that results reached by researchers in the last decade have notbeen satisfying to investors. In response, we are attempting to establish a greater correlation between market data and social media feeds in order to increase the accuracy of stock market prediction.

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