Scalable dataspace construction

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Date
2017Author
Shibwabo, Bernard K.
Wanyembi, Gregory N.
Ateya, Ismail L.
Omwenga, Vincent O.
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Show full item recordAbstract
This paper proposes the design and implementation of scalable dataspaces based on efficient data structures.
Dataspaces are often likely to exhibit a multidimensional structure due to the unpredictable neighbour relationship between
participants coupled by the continuous exponential growth of data. Layered range trees are incorporated to the proposed
solution as multidimensional binary trees which are used to perform d-dimensional orthogonal range indexing and searching.
Furthermore, the solution is readily extensible to multiple dimensions, raising the possibility of volume searches and even
extension to attribute space. We begin by a study of the important literature and dataspace designs. A scalable design and
implementation is further presented. Finally, we conduct experimental evaluation to illustrate the finer performance of proposed
techniques. The design of a scalable dataspace is important in order to bridge the gap resulting from the lack of coexistence of
data entities in the spatial domain as a key milestone towards pay-as-you-go systems integration