Broadcast disks: data management for asymmetric communication environments
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Broadcast scheduling for information distribution
Wireless Networks
Scheduling data broadcast in asymmetric communication environments
Wireless Networks
Scheduling data broadcast to “impatient” users
Proceedings of the 1st ACM international workshop on Data engineering for wireless and mobile access
The Architecture of Videotex Systems
The Architecture of Videotex Systems
Response time in data broadcast systems: mean, variance and tradeoff
Mobile Networks and Applications
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-Critical On-Demand Data Broadcast: Algorithms, Analysis, and Performance Evaluation
IEEE Transactions on Parallel and Distributed Systems
Utility Accrual Real-Time Scheduling under Variable Cost Functions
IEEE Transactions on Computers
RTCSA '09 Proceedings of the 2009 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
A Novel Adaptive Framework for Wireless Push Systems Based on Distributed Learning Automata
Wireless Personal Communications: An International Journal
Towards realizable, low-cost broadcast systems for dynamic environments
IEEE/ACM Transactions on Networking (TON)
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Broadcasting is scalable in terms of served users but not in terms of served data volume. Additionally, waiting time deadlines may be difficult to uphold due to the data clutter, forcing the clients to flee the system. This work proposes a way of selecting subsets of the original data that ensure near-optimal service ratio. The proposed technique relies on the novel data compatibility distance, which is introduced herein. Clustering techniques are then used for defining optimal data subsets. Comparison with related work and brute force-derived solutions yielded superior and near-optimal service ratios in all test cases. Thus, it is demonstrated that a system can attract more clients by using just a small portion of the available data pool.