Balancing push and pull for data broadcast
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Object Organization on a Single Broadcast Channel in the Mobile Computing Environment
Multimedia Tools and Applications
Data Allocation on Wireless Broadcast Channels for Efficient Query Processing
IEEE Transactions on Computers
Data on Air: Organization and Access
IEEE Transactions on Knowledge and Data Engineering
Random Generation of Bayesian Networks
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Query Processing in Broadcasted Spatial Index Trees
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Pushing dependent data in clients-providers-servers systems
Wireless Networks
On Exploring Channel Allocation in the Diverse Data Broadcasting Environment
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
TOSA: a near-optimal scheduling algorithm for multi-channel data broadcast
Proceedings of the 6th international conference on Mobile data management
On-demand data broadcasting for data items with time constraints on multiple broadcast channels
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
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Data broadcasting is an effective way to disseminate information to clients in wireless environment. We consider how to efficiently generate the broadcast schedule on multiple channels when the data set has a DAG access pattern. It is NP-complete to find an optimal broadcast schedule which not only minimizes the latency but is a topological ordering which preserves the data dependency. We rule out a condition for the input DAGs under which one can generate an optimal broadcast schedule in linear time and propose a linear time algorithm to generate the schedule under such a condition. For general DAGs, we provide three heuristics: the first one uses the overall access probability of each vertex; the second one considers the total access probability of the posterior vertices of a vertex; the third one combines the above two heuristics. We analyze these three heuristics and compare them through experiments.