Human behavior based particle swarm optimization for materialized view selection in data warehousing environment

Because of the Materialized View (MV) space value and repair cost limitation in Data Warehouse (DW) environment, the materialization of all views was practically impossible thus suitable MV selection was one of the smart decisions in building DW to get optimal efficiency, at the same time in the modern world, techniques for enhancing DW quality were appeared continuously such as swarm intelligence. Therefore, this paper presents first framework for speeding up query response time depending on Human Particle Swarm Optimization (HPSO) algorithm for determining the best locations of the views in the DW. The results showed that the proposed method for selecting best MV using HPSO algorithm is better than other algorithms via calculating the ratio of query response time on the base tables of DW and compare it to the response time of the same queries on the MVs. Ratio of implementing the query on the base table takes 14 times more time than the query implementation on the MVs. Where the response time of queries through MVs access equal to 106 milliseconds while by direct access queries equal to 1066 milliseconds. This outlines that the performance of query through MVs access is 1471.698% better than those directly access via DW-logical

Hybrid of K-means clustering and naive Bayes classifier for predicting performance of an employee

Predicting the performance of an employee in the future is a requirement for companies to succeed. The employee is the organization's main component, the failure or organization’s success based on the performance of an employee, this has become an important interest in almost all types of companies for decision-makers and managers in the implementation of plans to find highly skilled employees correctly. Management thus becomes involved in the success of these employees. Particularly to guarantee that the right employee at the right time is assigned to the convenient job. The forecasting of analytics is a modern human resource trend. In the field of predictive analytics, data mining plays a useful role. To obtain a highly precise model, the proposed framework incorporates the K-Means clustering approach and the Naïve Bayes (NB) classification for better results in processing performance data of employees, implemented in WEKA, which enables personnel professionals and decision-makers to predict and optimize their employees' performance. The data were taken from the previous works, this was used as a test case to illustrate how the incorporates of K-Media and Naïve Bayes algorithms increases the exactness of employee performance predicting, compared with the K-Means and Naïve Bayes methods, the proposed framework increases the accuracy of predicting the performance of an employee