MapReduce Output for Fully-Distributed Mode
PARALLEL IMAGE DATABASE PROCESSING WITH MAPREDUCE AND PERFORMANCE EVALUATION IN PSEUDO DISTRIBUTED MODE
... parallel distributed processing of a video database by using the computational resources of a cloud computing environment should be ...and distributed computing has been recognized as ...function ...
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Distributed Simulated Annealing with MapReduce
... produce intermediate outputs from all map methods. All intermediate records are then shuffled, sorted, and submitted for final processing by a user-defined reduce method. In general, the reduce method can be executed in ...
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MapReduce/Bigtable for Distributed Optimization
... about MapReduce which are especially relevant for our ...erroneous output. A fully asynchronous system would violate idempotence since each worker would be constantly updating a shared param- eter ...
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MapReduce: Distributed Computing for Machine Learning
... Figure 2 demonstrates the effect of varying the number of reduce tasks (inversely, the size of the reduce splits). When there are many reducers, there is good load balancing, as in Figure 2 (left). Additionally, because ...
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Distributed Apriori in Hadoop MapReduce Framework
... 2. Distributed Apriori in MapReduce The following diagram illustrates the components and data flow of our distributed Apriori in MapReduce ...and output files are formatted in ...A ...
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Distributed Column Subset Selection on MapReduce
... A MapReduce job is executed in two phases of user- defined data transformation functions, namely, map and reduce ...blocks distributed among the ...The output of the map function is another set of ...
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HOG: Distributed Hadoop MapReduce on the Grid
... The high replication factor for HOG allows for very good data locality. With the data on the same node as the map execution, reading in the data is very quick. But, each reduce needs to get data from each mapper. Since ...
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Distributed data management using MapReduce
... An alternative method to support continuous query processing is to implement a purely hash-based framework to replace the sort-merge process in MapReduce [Li et al. 2011]. The idea is to “group data by key, then ...
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Distributed Offline Load Balancing in MapReduce Networks
... in MapReduce Networks Themistoklis Charalambous, Evangelia Kalyvianaki, Christoforos ...of MapReduce tasks running on heterogeneous clusters, ...a fully decentralized algorithm, based on ratio ...
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Exploring MapReduce Efficiency with Highly-Distributed Data
... single MapReduce job begins; DMR transmits half the output data between the clusters after the half-size MapReduce jobs have been completed, only to encounter a similar-size result-combine ...
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Distributed Computing and Big Data: Hadoop and MapReduce
... Process Li t f List of Document IDs belonging to this Seed Clusters Encoded Documents Input Seed clusters encoded Output Cluster Centroids C-values for each document p.. Process[r] ...
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HadoopPerceptron: a Toolkit for Distributed Perceptron Training and Prediction with MapReduce
... the MapReduce framework guaran- tees that all local weights associated with a given feature are aggregated together as input to a dis- tinct reduce ...as output the average of the associated feature ...the ...
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An Adaptive Distributed Simulator for Cloud and MapReduce Algorithms and Architectures
... and MapReduce solutions are often evaluated on simulator ...complete fully parallel and distributed cloud and MapReduce simulator (Cloud 2 Sim), leveraging the Java in-memory data grid ...and ...
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SciPDFindexer: Distributed Information Retrieval system using MapReduce
... node mode, the interaction among the components is exactly as described in the ...on distributed system and this architecture is not enough to understand how the system works in detail in large-scale ...
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A MapReduce based distributed SVM algorithm for binary classification
... 2. Support Vector Machine In machine learning field, SVM is a supervised learning algorithm for classification and regression problems depending of the type of output. SVM uses statistical learning theory to ...
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Fully Distributed EM for Very Large Datasets
... To choose a heuristically good topology, we use the maximum spanning tree (MST) with edge weights equal to the sizes of the intersections |α i ∩ αj|, so that nodes with more shared statistics tend to be closer to- ...
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A novel MapReduce-based approach for distributed frequent subgraph mining
... : distributed storage of the graph, neighbor vertices finding and pattern initialization, pattern extension, and frequency ...a MapReduce pass. In each MapReduce pass, the task is divided into a ...
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A MapReduce-based distributed SVM algorithm for automatic image annotation
... single MapReduce operation for each data chunk ...of MapReduce computation steps ...the output of all mappers due to their relatively small size compared to mappers input which leads to a greater ...
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Large-Scale Distributed Storage for Highly Concurrent MapReduce Applications
... the MapReduce framework, as well as adjusting the framework itself to take advantage of such function- ...for MapReduce applications, as well as for many data-intensive wor- ...the MapReduce workers ...
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Evaluating Performance of Distributed Systems With MapReduce and Network Traffic Analysis
... provided by the JXTA. Each message of a JXTA Socket is part of a Pipe that represents a connection established between the sender and receiver. In a JXTA Socket communication, two Pipes are established, one from sender ...
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