On-Demand Data Broadcasting for Mobile Decision Making

Abstract

Abstract. 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.

On the performance of real-time multi-item request scheduling in data broadcast environments

“…2(a) shows the request deadline miss ratio under different data access patterns. In single-item request environments, the performance of algorithms improves when the data access pattern becomes skewed because there is a higher potential to satisfy more requests in each broadcast (Hui et al, 2005;Wu et al, 2006;Sharaf and Chrysanthis, 2004). Similar performance results can be obtained in multi-item request environments except for MRF.…”

A hybrid scheduling scheme for data broadcast over a single channel in mobile environments

“…Existing broadcast scheduling schemes have been developed from two different viewpoints: one is based on data access probabilities (Acharya et al 1995;Imielinski et al 1997;Hsu et al 2005;Sharaf and Chrysanthis 2004;Sun et al 2003;Yee et al 2001) and the other is based on the semantic relations of the data (Lee et al 2002a,b;Chehadeh et al 1999;Chung and Kim 2001;Lee and Lo 2003;Shih and Liu 2008). The broadcast disks (Acharya et al 1995) approach analyzes the access preference of each data object and differentiates the delivery frequencies of the data objects so that more popular data items are broadcast more frequently.…”

AIDOA: An Adaptive and Energy-Conserving Indexing Method for On-Demand Data Broadcasting Systems

“…The default capacity of the cache is set to 0.01 × ∑ object size and the fetch delays of data objects follow an exponential distribution with mean 2.3 seconds [8]. Similar to [16], the number of users in the network is set to 250. Service holding time and service re-establishing time for each user are set to exponential distributions with means of 10 minutes and one hour, respectively.…”