Hadoop: The Definitive Guide

By Tom White. 2009

4,254 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “However, due to the limited capability of sensor nodes, fusion impacts the lifetime of sensor battery and may result in unsatisfactory performance [119].”
Petuum: A New Platform for Distributed Machine Learning on Big Data (2013, background): “Many programmable platforms have provided partial solutions to bridge this research-to-production gap: while Hadoop [11] is a popular and easy to program platform, its implementation of MapReduce requires the program state”
Towards a Big Data Analytics Framework for IoT and Smart City Applications (2015, methodology): “The actual processing of data uses different computational frameworks such as Hadoop’s MapReduce [ 63], Hama’s Bulk Synchronous Processing framework [ 53], or graph processing frameworks [ 24].”
Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics (2013, background): “Some functionality is similar to that of more general purpose systems such as Hadoop [43], but this is not currently used.”
Cloud manufacturing: key characteristics and applications (2017, methodology): “It is important for a cloud manufacturing system to utilise cloud-based storage technologies (White 2010) to handle the unstructured data.”
Big data related technologies, challenges and future prospects (2015, methodology): “For example, Richert’s book (Richert 2013) walks you through using Python in analysis phrase; whereas White’s book (White 2012) teaches how to build scalable distributed big data systems with Hadoop.”
Understandable Big Data: A survey (2015, methodology): “It enables easy data ETL from HDFS or other data storage like HBase [11,15] or other traditional DBMS [25].”
Sparrow: distributed, low latency scheduling (2013, result): “This feature set is similar to that of the Hadoop MapReduce scheduler [23] and the Spark [26] scheduler.”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “For instance, Hadoop guidelines [239] suggest that the number of reduced tasks should be set to approximately 0.95 or 1.75 times the number of reduced slots available in the cluster to ensure system tolerance for re-executing failed or slow tasks.”
Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results (2015, background): “Technologies from the Big Data context such as [22] are able to process the enormous amounts of data and analyze them in near-real-time.”
Scalable K-Means++ (2012, background): “Since the pioneering work by Dean and Ghemawat [13], MapReduce and its open source version, Hadoop [33], has become a de facto standard for large data analysis, and a variety of algorithms have been designed for it [7, 6].”
Hadoop-HBase for large-scale data (2011, background): “…more and more Binary Large Objects (BLOBs) (images, audio, video etc.) and Character Large Objects (CLOBs) are appearing in databases which adds one more degree of complexity in the identification of the actual content and thereby in processing and identifying patterns that actually make sense…”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “Many IT vendors benefit from Hadoop technology [389]–[392], which allows us to execute tasks directly from its hosting place without copying to local memory.”