Dynamic multidimensional index for large-scale cloud data

Abstract

Although several cloud storage systems have been proposed, most of them can provide highly efficient point queries only because of the key-value pairs storing mechanism. For these systems, satisfying complex multi-dimensional queries means scanning the whole dataset, which is inefficient. In this paper, we propose a multidimensional index framework, based on the Skip-list and Octree, which we refer to as Skip-Octree. Using a randomized skip list makes the hierarchical Octree structure easier to implement in a cloud storage system. To support the Skip-Octree, we also propose a series of index operation algorithms including range query algorithm, index maintenance algorithms, and dynamic index scaling algorithms. Through experimental evaluation, we show that the Skip-Octree index is feasible and efficient.

File retrieval and storage in the open source cloud tool using digital bipartite and digit compact prefix indexing method

“…While retrieving a file from the compute node, the request submitted by the user is sent to the controller, and the search takes place in the global index with the file name value and instance ID. If it matches with any of the files, then the request is sent to the corresponding compute node volume instead of searching all the compute node volumes …”

Techniques for Ensuring Index Usage Predictability in Microsoft SQL Server

“…The index structure is claimed to efficiently support a variety of types of queries and provide high availability. In [14], a dynamic double layer indexing structure with Skipnet overlay for global indexing and an Octree index technique for local indexing has been proposed, and the authors claim it to be better than the previous double-layer indexing techniques for complex queries.…”