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By Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
"Data are generated at an exponential price worldwide. via complicated algorithms and analytics thoughts, companies can harness this knowledge, realize hidden styles, and use the findings to make significant judgements. Containing contributions from best specialists of their respective fields, this ebook bridges the space among the vastness of huge facts and the ideal computational equipment for �Read more...
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Sample text
In addition, its efficiency increases when the number of processes increases. There are two reasons for that. First, the data domain with respect to each process is independent, which helps to decrease the search space for each process. That identifies the objects in a better way using the local references. The second reason is the DDC, between the query and the candidate list from each process. Hence, the IB approach gives a better recall and position error than the sequential and the other distributed implementations.
When the processes are in the indexing role, they share all the reference information R. When they are in the coordinating role, the reference set is divided between the processes. Each process has a n subset of the references Rp ⊂ R and R p = . Algorithm 3 shows the indexing process. All P the processes start in the indexing role. For each object oi ∈ Dp, the partial ordered list L(oi , R ) is created using the full reference set R (lines 1–7). Afterwards, the locations of the closest reference points in the partial ordered list of each object L(oi , R )|rj , the reference ID j, and the related object global ID are sent to the corresponding coordinating process (line 8–12).
Lucchese, R. Perego, T. Piccioli and F. 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.