Scalable dataspace construction
Shibwabo, Bernard K.
Wanyembi, Gregory N.
Ateya, Ismail L.
Omwenga, Vincent O.
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
The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.
Dataspaces, Machine learning, Systems integration, Spatial databases, Range Trees, Scalability
Shibwabo, B. K., Wanyembi, G. N., Ateya, I. L., & Omwenga, V. O. (2017). Scalable dataspace construction. In Pan African Conference on Science, Computing and Telecommunications (PACT). Nairobi: Strathmore University. Retrieved from https://su-plus.strathmore.edu