By Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
"Data are generated at an exponential fee worldwide. via complex algorithms and analytics thoughts, organisations can harness this information, observe hidden styles, and use the findings to make significant judgements. Containing contributions from major specialists of their respective fields, this booklet bridges the distance among the vastness of huge information and the proper computational equipment for medical and social discovery. It additionally explores comparable functions in various 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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Additional resources for Big data : algorithms, analytics, and applications
Rabitti. CoPhIR: A test collection for content-based image retrieval. 4627v2, 2009. A: Source Code of Mappers and Reducers 36 ABSTRACT Based on the MapReduce model and Hadoop Distributed File System (HDFS), Hadoop enables the distributed processing of large data sets across clusters with scalability and fault tolerance. Many data-intensive applications involve continuous and incremental updates of data. Understanding the scalability and cost of a Hadoop platform to handle small and independent updates of data sets sheds light on the design of scalable and cost-effective data-intensive applications.
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 ). Algorithm 4 shows the searching process.
Moffat. Inverted files for text search engines. ACM Computing Surveys, 38(2):6, 2006. H. Mohamed and S. Marchand-Maillet. Parallel approaches to permutation-based indexing using inverted files. In 5th International Conference on Similarity Search and Applications (SISAP), Toronto, CA, August 2012. 20. E. S. Tellez, E. Chavez and A. Camarena-Ibarrola. A brief index for proximity searching. In E. -O. Eklundh, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, volume 5856 of Lecture Notes in Computer Science, pages 529–536.