MapReduce is well-applied in high performance computing for large scale data processing. However, as long as the clusters grow, handling with huge amount of intermediate data produced in the shuffle and reduce phases (middle step of Map Reduce) have impacts heavily upon the performance. With local aggregation (either combiners or in-mapper), shuffling large amounts of data can be reduced which alleviates the reduce straggler problem. The proposed modified B+ tree based indexing algorithm is applied to reduce intermediate data amount for output retrieval fast as well as scalable data storage
The proficiently-liked technology for recommender system is collaborative filtering. The current CF ...
Distributed systems are now commonly used to manage massive data flooding from the physical world, s...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...
MapReduce is well-applied in high performancecomputing for large scale data processing. However, asl...
Abstract:-The B+-tree and its variants have been reported as the good index structures for retrievin...
The rapid generation and accumulation of data in recent time have led to the concept of big data wh...
In this paper, we study to reduce network traffic cost for virtually any Map Reduce job by developin...
than the artifacts. 2. MapReduce 3. Map-Reduce-Merge: extending MapReduce 4. Using Map-Reduce-Merge ...
MapReduce is an emerging programming paradigm for data parallel applications proposed by Google to s...
As a leading framework for processing and analyzing big data, MapReduce is leveraged by many enterpr...
Big Data -A, an acceleration framework that optimizes Big Data with plug-in components for fast data...
The rapid growth of fast analytics systems, that require data processing in memory, makes memory cap...
Memory is rapidly becoming a precious resource in many data processing environments. This paper int...
[[abstract]]MapReduce is a programming model to process a massive amount of data on cloud computing....
International audienceMost researchers working on high-dimensional indexing agree on the following t...
The proficiently-liked technology for recommender system is collaborative filtering. The current CF ...
Distributed systems are now commonly used to manage massive data flooding from the physical world, s...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...
MapReduce is well-applied in high performancecomputing for large scale data processing. However, asl...
Abstract:-The B+-tree and its variants have been reported as the good index structures for retrievin...
The rapid generation and accumulation of data in recent time have led to the concept of big data wh...
In this paper, we study to reduce network traffic cost for virtually any Map Reduce job by developin...
than the artifacts. 2. MapReduce 3. Map-Reduce-Merge: extending MapReduce 4. Using Map-Reduce-Merge ...
MapReduce is an emerging programming paradigm for data parallel applications proposed by Google to s...
As a leading framework for processing and analyzing big data, MapReduce is leveraged by many enterpr...
Big Data -A, an acceleration framework that optimizes Big Data with plug-in components for fast data...
The rapid growth of fast analytics systems, that require data processing in memory, makes memory cap...
Memory is rapidly becoming a precious resource in many data processing environments. This paper int...
[[abstract]]MapReduce is a programming model to process a massive amount of data on cloud computing....
International audienceMost researchers working on high-dimensional indexing agree on the following t...
The proficiently-liked technology for recommender system is collaborative filtering. The current CF ...
Distributed systems are now commonly used to manage massive data flooding from the physical world, s...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...