SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A hybrid data mining anomaly detection technique in ad hoc networks
International Journal of Wireless and Mobile Computing
A distributed clustering method for energy-efficient data gathering in sensor networks
International Journal of Wireless and Mobile Computing
International Journal of Wireless and Mobile Computing
MapDupReducer: detecting near duplicates over massive datasets
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Join Optimization in the MapReduce Environment for Column-wise Data Store
SKG '10 Proceedings of the 2010 Sixth International Conference on Semantics, Knowledge and Grids
Cloud computing paradigms for pleasingly parallel biomedical applications
Concurrency and Computation: Practice & Experience
Hadoop: The Definitive Guide
Toward scalable internet traffic measurement and analysis with Hadoop
ACM SIGCOMM Computer Communication Review
Balanced data gathering strategy based on ant colony algorithm in WSNs
International Journal of Wireless and Mobile Computing
Genome sequencing using mapreduce on FPGA with multiple hardware accelerators (abstract only)
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
An evolvable cellular automata based data encryption algorithm
International Journal of Wireless and Mobile Computing
Design Dynamic Data Allocation Scheduler to Improve MapReduce Performance in Heterogeneous Clouds
ICEBE '12 Proceedings of the 2012 IEEE Ninth International Conference on e-Business Engineering
Scheduling strategies for efficient ETL execution
Information Systems
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State Key Laboratory for GeoMechanics and Deep Underground Engineering, Deep-Lab for short, has been committed to the study of rock burst. Deep-Lab accumulated a large number of rock-burst data. With the deepening of the research progress, massive-data dilemma, artificial-management-data dilemma and experimental-data-analysis dilemma have become three big problems of rock burst. These dilemmas restrict the development of rock burst research technologies. This article takes big data in rock burst experiment as research objects and innovatively introduces big data technology into rock burst. Digital features of rock-burst experimental data were extracted. On this basis, a big data based data storage systems for rock burst experiment, BDSS for short, was designed and built. Then an integrated rock burst experimental platform was constructed. Experiments show that, BDSS solves three dilemmas of rock burst, and realises the distributed storage system of data. BDSS also realises dynamic and efficient load of rock-burst big data, and its efficient query under complication conditions.