By Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
"Data are generated at an exponential price around the globe. via complex algorithms and analytics ideas, companies can harness this knowledge, become aware of hidden styles, and use the findings to make significant judgements. Containing contributions from top specialists of their respective fields, this ebook bridges the distance among the vastness of massive information and the best computational tools for medical and social discovery. It additionally explores similar purposes in various sectors, protecting applied sciences for media/data verbal exchange, 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
Ares, N. R. Brisaboa, M. F. Esteller, Ó. Pedreira and Á. S. Places. Optimal pivots to minimize the index size for metric access methods. In Proceedings of the 2009 Second International Workshop on Similarity Search and Applications, pages 74–80, Washington, DC, 2009. IEEE Computer Society. B. Bustos, G. Navarro and E. Chávez. Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters, 24(14):2357–2366, 2003. 6. C. -I. Lin. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets.
Hence, the searching is only done through the process that is responsible for the corresponding data domain (lines 1–4). If the query is located between two or more data domains, the closest references are shared among these Scalable Indexing for Big Data Processing ◾ 13 different domains. Formally, a process p is an active process if there exists rj ∈ Rp, such that rj ∈L(q, R ). After searching, the results from each process cannot be sent to the broker directly. The similarity between the query and each object for each process is defined by different sets of reference points Rp that are not related to each other.
Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling. Morgan Kaufmann, San Francisco, CA, 2006. B. Bustos, O. Pedreira and N. Brisaboa. A dynamic pivot selection technique for similarity search. In Proceedings of the First International Workshop on Similarity Search and Applications, pages 105–112, Washington, DC, 2008. IEEE Computer Society. 4. L. G. Ares, N. R. Brisaboa, M. F. Esteller, Ó. Pedreira and Á. S. Places.
Big data : algorithms, analytics, and applications by Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea