Hadoop++

By Jens-Peter Dittrich, Jorge-Arnulfo Quiané-Ruiz, Alekh Jindal, Yagiz Kargin, Vinay Setty, Jörg Schad. Proceedings of the VLDB Endowment, 2010

884 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/0138f5e5c6598cec0d64bf42f68a2706ea21bea8

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Citations in the corpus

Big Data: Survey, Technologies, Opportunities, and Challenges (2014, background): “[49] In particular, Hadoop can process extremely large volumes of data with varying structures (or no structure at all).”
Analytics over large-scale multidimensional data: the big data revolution! (2011, background): “Several studies, like [9,12], have provided recommendations for further improving the computational capabilities of Hadoop, whereas [1] proposes HadoopDB, a novel hybrid architecture that combines MapReduce and traditional DBMS technologies for supporting advanced analytics over large-scale data rep”
An Analysis of Traces from a Production MapReduce Cluster (2010, methodology): “Hadoop [9] is an open-source implementation of Google’s MapReduce [10] framework that enables distributed, dataintensive, parallel applications by decomposing a massive job into smaller (Map and Reduce) tasks and a massive data-set into smaller partitions, such that each task processes a different p”
Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture (2012, methodology): “In [18] Hyrax enables smartphone applications to form a distributed computing cloud, which implements Hadoop [19] in Android phones using MapReduce function.”
SkewTune: mitigating skew in mapreduce applications (2012, methodology): “proposed the Hadoop++ system that optimizes MapReduce jobs by leveraging indexing and join techniques [9].”
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference (2020, neutral): “Lastly, the distributed scheme described in this work, that shards only the embedding tables, can be compared to traditional scale-out, in-memory distributed databases or hash tables that serve large magnitudes of tabular data [53], [54].”
Efficient Machine Learning for Big Data: A Review (2015, methodology): “There are several computing frameworks, e.g., Hadoop [56], SHadoop [57], ComMapReduce [58], Dryad [59], Pic-colo [60], and IBM parallel machine learning toolbox, such systems have the capabilities to scale up machine learning.”
On the use of MapReduce for imbalanced big data using Random Forest (2014, background): “Algorithm 4 is devoted to obtaining all instances in a mapper's partition and the Hadoop framework calls it for each hkey; valuei pair in this partition.”
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads (2012, methodology): “Using the original MapReduce use case of data indexing in support of web search [22] and the workload assumptions behind common microbenchmarks of stand-alone, large-scale jobs [4,6,45], one would expect answers to the above to be: (1).”
Toward a Unified Ontology of Cloud Computing (2008, neutral): “In this respect, a Hadoop [10] deployment on the cloud would be considered a cloud software environment, as it provides its applications' developers with a programming environment, i.e. map reduce framework for the cloud.”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, background): “Hadoop [5] is a famous cloud platform widely used in data mining.”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Hadoop++ [264] provides an index-structured file format that improves the I/O cost.”
Hyracks: A flexible and extensible foundation for data-intensive computing (2011, background): “Recently, there has been work in making Hadoop jobs more performant through clever placement and organization of data [30] or by sharing intermediate computations across jobs [31].”
Profiling, what-if analysis, and cost-based optimization of MapReduce programs (2011, background): “Hadoop Vaidya [12] and Hadoop Performance Monitoring UI [11] execute a small set of predeﬁned diagnostic rules against the job execution counters to diagnose various performance problems, and offer targeted advice.”