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This research is motivated by the strong demand for an efficient index structure design for main-memory database systems of RFID applications.
An efficient index structure for complex spatial objects is one of the most challenging requirements in non-traditional applications such as geographic information systems (GISs), computer-aided design (CAD), and multimedia databases.
First, preprocess protein database can be secured, such that a more efficient index structure can be constructed.
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How to generate large-scale, high-quality activity trajectories and provide efficient indexing structure to support big data management and analysis, has become a new challenge.
It is up to the CDN application designer to come-up with efficient indexing structure that can scale to large content sizes.
To speed up the online search algorithm, we devise a space-efficient index structure, namely KEP-Index, to support efficient community search.
Due to a highly time-efficient index structure our method outperforms the only existing tool for fragment chaining under the linear gap cost model.
Given a set of n objects, each characterized by d attributes specified at m fixed time instances, we are interested in the problem of designing space efficient indexing structures such that a class of temporal range search queries can be handled efficiently.
Our goal is to design a space-efficient indexing structure with fast searching capability to catch the massive short reads produced by the next generation high-throughput DNA sequencing technology.
In this paper, we introduce an efficient indexing for protein structure search where protein structures are represented as vectors by 3D-Zernike Descriptor (3DZD).
The M-tree is an index structure that enables efficient indexing and querying of spatial data in metric spaces [ 32, 33].
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