Today’s businesses need support when making decisions. Business intelligence (BI) helps businesses to make decisions based on good pre-analysis and documented data, and enables information to be presented when and where the decisions need to be made. Real time business intelligence (RTBI) presents numbers in real time, providing the decision makers at the operational and tactical layers with data as fresh as it can be.
By having accurate, fresher and a bigger amount of data, businesses will be able to make decisions in a faster pace, and eliminate tedious complexity of the decision-making process.
The objective of this research is to show that a real time business intelligence solution would be beneficial for supporting the operational and tactical layers of decision-making within an organization. By implementing an RTBI solution, it would provide the decision-maker with fresh and reliant data to base the decisions on. Visualization of the current decision processes showed that by adding a real time business intelligence solution it would help eliminate the use of intuition, as there would be more data available and the decisions can be made where the work is performed.
The aim of this research is to contribute by visualizing how a real time business intelligence solution can shorten a complex decision process by giving the correct information to the right people. Organizations need to address potential challenges as part of a pre-project of a real time business intelligence implementation.

Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2- itemsets, the poor efficiency of counting their support. Proposed algorithm reduces one redundant pruning operations of C_2. If the number of frequent 1-itemsets is n, then the number of connected candidate 2-itemsets is C-n, while pruning operations C_n. The proposed algorithm decreases pruning operations of candidate 2-itemsets, thereby saving time and increasing efficiency. For the bottleneck: poor efficiency of counting support, proposed algorithm optimizes subset operation, through the transaction tag to speed up support calculations.
Algorithm Apriori is one of the oldest and most versatile algorithms of Frequent Pattern Mining (FPM). Its advantages and its moderate traverse of the search space pay off when mining very large databases. Proposed algorithm improves Apriori algorithm by the way of a decrease of pruning operations, which generates the candidate 2-itemsets by the apriori-gen operation. Besides, it adopts the tag-counting method to calculate support quickly. So the bottleneck is overcome.