By Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
"Data are generated at an exponential expense around the globe. via complicated algorithms and analytics innovations, corporations can harness this knowledge, notice hidden styles, and use the findings to make significant judgements. Containing contributions from major specialists of their respective fields, this e-book bridges the space among the vastness of huge info and the perfect computational tools for medical and social discovery. It additionally explores comparable functions in assorted sectors, protecting applied sciences for media/data conversation, elastic media/data garage, cross-network media/data fusion, SaaS, and more"-- �Read more...
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Extra resources for Big data : algorithms, analytics, and applications
Brisaboa, O. Pedreira and P. Zezula, editors, Similarity Search and Applications, volume 8199 of Lecture Notes in Computer Science, pages 103–114. Springer, Berlin, 2013. 14. G. Amato and P. Savino. Approximate similarity search in metric spaces using inverted files. In Proceedings of the 3rd International Conference on Scalable Information Systems, InfoScale ’08, pages 28:1–28:10, ICST, Brussels, Belgium, 2008. ICST. 15. A. Esuli. Mipai: Using the PP-index to build an efficient and scalable similarity search system.
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The complexity of indexing is O P where t1 is the time needed to transfer the object information from the indexing process to the coordinating process. For rj ∈ R 4. dis = d(oi,rj) 5. indx = j 6. L(oi,R) = quicksort(b,n) L(oi , R ) = partiallist ( L(oi , R ), n) 7. Send the global object ID, the L(oi , R )|rc, and the reference ID rc to coordinating process. if(coordinator) 11. Recv. data from any indexer. 12. Store the received data in DSp. 2 Searching Unlike in the previous search scenario, the processes that participate to answer the query are the processes that have the references, which are located in L(q, R ).