[PDF] Top 20 MapReduce: Ordering and Large Scale Indexing on Large Clusters
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MapReduce: Ordering and Large Scale Indexing on Large Clusters
... The MapReduce programming model has been successfully used at Google for many different ...a large variety of problems are easily expressible as MapReduce ...of MapReduce that scales to ... See full document
5
An Efficient Platform for Large-Scale MapReduce Processing
... on large commodity clusters. The MapReduce computation takes a set of input key/value pairs, and produces a set of output key/value ...arbitrarily large lists of values to be passed ... See full document
70
Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters
... Resource mismatch: Current approaches leverage the cluster resource states (i.e. available idle or revocable re- sources) to perform oversubscription decisions, and do so via piggybacking on regular cluster scheduler ... See full document
19
Evaluation of MapReduce in a large cluster
... runs large scale data processing ...of MapReduce on large clusters and thus there is a lack of reference for expected behavior or issues while running applications in a large ... See full document
8
Large-scale data mining analytics based on MapReduce
... YARN clusters by taking an example where there is one master node and two slave nodes: The region above the grey dotted line runs locally on the master node while two boxes below the grey dotted line represent two ... See full document
99
Fast Detection of Connected Components in Large Scale Graphs Using MapReduce
... As we described, each maper of MemoryCC loads the sub graph associated with it into memory. At first this may be seemed problematic due to its high memory usage, but even in clusters made up of commodity hardware, ... See full document
8
An Improved Performance Evaluation on Large-Scale Data using MapReduce Technique
... The Map job has three sub tasks such as Mapper, Combiner and Partitioner. Mapper performs the mapping of data, Combiner combines the mapped data and Partitioner divide the data into small clusters, after which the ... See full document
11
MapReduce in MPI for Large-scale Graph Algorithms
... related MapReduce operations are provided by the ...sorted ordering of the KV pairs (or values within a multivalue), using a user-provided comparison func- ... See full document
39
Mapreduce Programming Paradigm in Cloud Environment for Large-Scale Data Mining
... from large databases, Domingo et ...knowledge. MapReduce programming is studied on the large clusters in ...with MapReduce programming framework in ...with large volumes of ... See full document
12
A Survey of Large-Scale Analytical Query Processing in MapReduce
... PowerDrill [49] uses a column-oriented datastore and various engineering choices to improve the performance of query processing even further. Compared to Dremel that uses streaming from DFS, PowerDrill relies on hav- ing ... See full document
27
A Database Perspective on Large Scale High-Dimensional Indexing
... produce clusters having quite different ...the clusters: scanning heavily filled clusters is costly as the distances to many points must be ...under-filled clusters are cheap to process, but ... See full document
178
Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)
... The prime goal of dimensionality reduction is to downsize data with size N to data with size m, where m < N or m << N . Singular Value Decomposition (SVD) [ Golub and Reinsch 1970 ], Multi- Dimensional Scaling ... See full document
67
Performance-aware Speculative Resource Oversubscription for Large-scale Clusters
... Resource mismatch: Current approaches leverage the cluster resource states (i.e. available idle or revocable re- sources) to perform oversubscription decisions, and do so via piggybacking on regular cluster scheduler ... See full document
18
MapReduce-based Parallelization of Sparse Matrix Kernels for Large-scale Scientific Applications
... of large data in a parallel and distributed fashion on clusters that generally consist of commodity ...In MapReduce programming model, these two computational phases operate on local data and there ... See full document
9
A Peer-To-Peer based Large-Scale Data processing Using MapReduce
... illustration and makes them continuously to be had. For occasional sustained analytical duties, we furnish an border for exporting the data from extended BestPeer to Hadoop and allow users to investigate those data ... See full document
5
Shape-dependent ordering of gold nanocrystals into large-scale superlattices
... (400) Figure 2 | Analysis of nanocrystal superlattices in using SEM and using SAXS. (a–d) Rhombic dodecahedra form a face-centered cubic superlattice visible along different projection directions. (e–h) Octahedra are ... See full document
9
MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
... MeSHLabeler is the last generation of MeSH indexing system, which uses the ‘learning to rank’ framework to incorporates multiple evidence to solve the challenge on the MeSH side. It has two components: MeSHRanker ... See full document
14
DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing.
... MeSH indexing can be considered as a large-scale multi-label classification problem ( Liu et ...a large number of MHs have a highly biased ... See full document
11
A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications
... In this paper we have presented and evaluated RASMO, a resource aware parallel SVM algorithm that capitalizes on the scalability, parallelism and resiliency of MapReduce for large scale image ... See full document
21
Syntax Based Word Ordering Incorporating a Large Scale Language Model
... a large additional collection of flu- ent sentences in the Agence France-Presse (AFP) and Xinhua News Agency (XIN) subsets of the English GigaWord Fourth Edition (Parker et ... See full document
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