On-demand data broadcasting for mobile decision making

The wide spread of mobile computing devices is transforming the newly emerged e-business world into a mobile e-business one, a world in which hand-held computers are the user's front-ends to access enterprise data. For good mobile decision making, users need to count on up-to-date, business-critical data. Such data are typically in the form of summarized information tailored to suit the user's analysis interests. In this paper, we are addressing the issue of time and energy efficient delivery of summary tables to mobile users with hand-held computers equipped with OLAP (On-Line Analytical Processing) front-end tools. Towards this, we propose a new on-demand scheduling algorithm, called STOBS, that exploits the derivation semantics among OLAP summary tables. It maximizes the aggregated data sharing between mobile users and reduces the broadcast length for satisfying a set of requests compared to the already existing techniques. The algorithm effectiveness with respect to access time and energy consumption is evaluated using simulation.