Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
The evolving landscape of data management in the cloud
International Journal of Computational Science and Engineering
An efficient index for massive IOT data in cloud environment
Proceedings of the 21st ACM international conference on Information and knowledge management
MobiS: a distributed paradigm of mobile sensor data analytics for evaluating environmental exposures
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Distributed and Parallel Databases
The data partition strategy based on hybrid range consistent hash in NoSQL database
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Efficient distributed multi-dimensional index for big data management
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
CG_Hadoop: computational geometry in MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with multi-dimensional index structures, can efficiently handle spatio-temporal data. However, popular open source relational database systems are overwhelmed by the high insertion rates, real-time querying requirements, and terabytes of data that these systems must handle. On the other hand, Key-value stores can effectively support large scale operation, but do not natively support multi-attribute accesses needed to support the rich querying functionality essential for the LBSs. We present MD-HBase, a scalable data management system for LBSs that bridges this gap between scale and functionality. Our approach leverages a multi-dimensional index structure layered over a Key-value store. The underlying Key-value store allows the system to sustain high insert throughput and large data volumes, while ensuring fault-tolerance, and high availability. On the other hand, the index layer allows efficient multi-dimensional query processing. We present the design of MD-HBase that builds two standard index structures芒聙"the K-d tree and the Quad tree芒聙"over a range partitioned Key-value store. Our prototype implementation using HBase, a standard open-source Key-value store, can handle hundreds of thousands of inserts per second using a modest 16 node cluster, while efficiently processing multidimensional range queries and nearest neighbor queries in real-time with response times as low as hundreds of milliseconds.