We can increase the production of honey from honeybees, if we optimize the conditions affecting its production in their hive. To do this, and to enable better decision-making, we need to understand the relationships that affect honey production . This research focuses on understanding the relationship between the various factors that affect honeybee activity and honey production. Towards this goal, various data analysis software programs were developed to find the relationships that exist between the various factors affecting bee activity. One of the best ways to find the relationship between these factors would be to determine the rules that exist between them, and the frequency that they occur. In this thesis, research data were obtained by implementing various sensors in a beehive. Data is analyzed over a sliding window, and the relationship between data factors are obtained. Various times during day or night are analyzed to determine important data, and data relationships. Generally, it was found that comparing more data within a given sliding window sample, and comparing over smaller window sizes, better determines more common rules as well as global rules amongst the sensor data.