Recently, the cloud computing platform is getting more and more attentions as a new trend of data management. Currently there are several cloud computing products that can provide various services. However, currently the cloud platforms only support simple keyword-based queries and can't answer complex queries efficiently due to lack of efficient index techniques. In this paper we propose an efficient approach to build multi-dimensional index for Cloud computing system. We use the combination of R-tree and KD-tree to organize data records and offer fast query processing and efficient index maintenance. Our approach can process typical multi-dimensional queries including point queries and range queries efficiently. Besides, frequent change of data on big amount of machines makes the index maintenance a challenging problem, and to cope with this problem we proposed a cost estimation-based index update strategy that can effectively update the index structure. Our experiments show that our indexing techniques improve query efficiency by an order of magnitude compared with alternative approaches, and scale well with the size of the data. Our approach is quite general and independent from the underlying infrastructure and can be easily carried over for implementation on various Cloud computing platforms.
efficient multi-dimensional index with node cube (EMINC) REF uses the combination of R-tree and KD-tree to organize data and develops the node bounding technique to reduce query processing cost on the cloud platform.
An efficient multi-dimensional index for cloud data management
CloudDB@CIKM
Computer Science