Real-Time Business Intelligence & Frequent Pattern Mining Algorithm
Timely Consistent Analysis Using Real-Time Data Warehouse Environment and Improving Efficiency of Apriori Algorithm
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In the competitive world of business, the survival of a company depends on how fast they are able to recognize changing business dynamics and challenges, and respond correctly and quickly. Companies must also anticipate trends, identify new opportunities and transform their strategy to stay ahead of the competition. Companies collect significant volumes of data. They need the ability to transform this raw data to actionable information by capturing, consolidating, organizing, storing, distributing, analyzing, and providing quick and easy access to it. Decision makers need up-to-date informatio...
In the competitive world of business, the survival of a company depends on how fast they are able to recognize changing business dynamics and challenges, and respond correctly and quickly. Companies must also anticipate trends, identify new opportunities and transform their strategy to stay ahead of the competition. Companies collect significant volumes of data. They need the ability to transform this raw data to actionable information by capturing, consolidating, organizing, storing, distributing, analyzing, and providing quick and easy access to it. Decision makers need up-to-date information to make timely and sound business decisions. A real-time data warehouse aims at decreasing the time it takes to make business decisions and tries to attain zero latency between the cause and effect of a business decision. Mining association rules involves a lot of memory and CPU costs. In this book, I present a new algorithm for mining frequent itemsets which will decrease CPU cost and increase efficiency. This book presents a new data integration approach to help a company create knowledge from information to enable better decision making and to convert those decisions into action.
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