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Most of the “Big Data” applications, such as decision
support and emergency response, must provide users with fresh,
low latency results, especially for aggregation results on key
performance index, however, disk-oriented approaches to online
storage are becoming increasingly problematic. They do not scale
gracefully to meet the needs of large-scale Web applications, and
improvements in disk capacity have far outstripped improvements
in access latency and bandwidth. To this end, the paper
proposes a memory-based real-time aggregation query system
named MRAQ, which adopts the shared-nothing architecture to
support the scalability. MRAQ implements the efficient aggregation
query through the bitmap index. The experiments show the
effective on the performance improvements.
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