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open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Apache Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. Hadoop services provide for data storage, data processing, data access, data governance, security, and operations.

The hadoop distributed file system: Architecture and design
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The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-

Apache hadoop
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Apache Hadoop is an open-source software framework for distributed storage and distributed processing of very large data sets on computer clusters bu ilt from commodity hardware. All the modules in Hadoop are designed with a

Terabyte sort on apache hadoop
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Apache Hadoop is a open source software framework that dramatically simplifies writing distributed data intensive applications. It provides a distributed file system, which is modelled after the Google File System , and a map/reduce implementation that

Towards Optimizing Hadoop Provisioning in the Cloud.
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Abstract Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs,

The hadoop distributed file system: Architecture and design
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The Hadoop File System (HDFS) is as a distributed file system running on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant

Greenhdfs: towards an energy-conserving, storage-efficient, hybrid hadoop compute cluster
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ABSTRACT Hadoop Distributed File System (HDFS) presents unique challenges to the existing energy-conservation techniques and makes it hard to scale-down servers. We propose an energy-conserving, hybrid, logical multi-zoned variant of HDFS for managing

Impala: A modern, open-source SQL engine for Hadoop
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ABSTRACT Cloudera Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop not delivered by batch

Fast and interactive analytics over Hadoop data with Spark
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Spark started out of our research groups discussions with Hadoop users at and outside UC Berkeley. We saw that as organizations began loading more data into Hadoop they quickly wanted to run rich applications that the single-pass, batch processing model of MapReduce

Hadoop security design
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Overview Design . .

Mochi: Visual Log-Analysis Based Tools for Debugging Hadoop .
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Abstract Mochi, a new visual, log-analysis based debugging tool correlates Hadoops behavior in space, time and volume, and extracts a causal, unified control-and dataflow model of Hadoop across the nodes of a cluster. Mochis analysis produces visualizations of

Cloud hadoop map reduce for remote sensing image analysis
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ABSTRACT Image processing algorithms related to remote sensing have been tested and utilized on the Hadoop MapReduce parallel platform by using an experimental 112-core high-performance cloud computing system that is situated in the Environmental Studies

HIPI: a Hadoop image processing interface for image-based mapreduce tasks
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Abstract The amount of images being uploaded to the internet is rapidly increasing, with Facebook users uploading over 2.5 billion new photos every month , however, applications that make use of this data are severely lacking. Current computer

Hadi: Fast diameter estimation and mining in massive graphs with hadoop
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Abstract How can we quickly find the diameter of a petabyte-sized graph Large graphs are ubiquitous: social networks (Facebook, LinkedIn, etc.), the World Wide Web, biological networks, computer networks and many more. The size of graphs of interest has been

Radoop: Analyzing big data with rapidminer and hadoop
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Abstract Working with large data sets is increasingly common in research and industry. There are some distributed data analytics solutions like Hadoop that offer high scalability and fault-tolerance, but they usually lack a user interface and only developers can exploit

Hibench: A representative and comprehensive hadoop benchmark suite
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THE HIBENCH SUITE MapReduce and its popular open source implementation, Hadoop are moving toward ubiquitous for Big Data storage and processing. Therefore, it is essential to quantitatively evaluate and characterize the Hadoop deployment through extensive

Hadoop : Scalable, flexible data storage and analysis
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Googles engineers designed and built a new data processing infrastructure to solve this problem. The two key services in this system were the Google File System, or GFS, which provided fault-tolerant, reliable, and scalable storage, and MapReduce, a data processing

An efficient implementation of a-priori algorithm based on hadoopMapReduce model
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ABSTRACT Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, it needs to scan the dataset many times and to generate

Towards a resource aware scheduler in hadoop
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Abstract HadoopMapReduce is a popular distributed computing model that has been deployed on large clusters like those owned by Yahoo and Facebook and Amazon EC2. In a practical data center of that scale, it is a common scenario that I/O-bound jobs and CPU-

Adding security to apache hadoop
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Abstract Hadoop is a distributed system that provides a distributed file system and MapReduce batch job processing on large clusters using commodity servers. Although Hadoop is used on private clusters behind an organizations firewalls, Hadoop is often

myHadoop- Hadoopon-Demand on traditional HPC resources
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ABSTRACT Traditional High Performance Computing (HPC) resources, such as those available on the TeraGrid, support batch job submissions using Distributed Resource Management Systems (DRMS) like TORQUE or the Sun Grid Engine (SGE). For large-scale

Securing big data hadoop : a review of security issues, threats and solution
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Abstract Hadoop projects treat Security as a top agenda item which in turn represents which is again classified as a critical item. Be it financial applications that are deemed sensitive, to healthcare initiatives, Hadoop is traversing new territories which demand

Ceph as a scalable alternative to the hadoop distributed file system
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Scalable Scientific Data Management. His current research interests include scalable file system data and

A case for flash memory ssd in hadoop applications
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Abstract As the access speed gap between DRAM and storage devices such as hard disk drives is ever widening, the I/O module dominantly becomes the system bottleneck. Meanwhile, the map-reduce parallel programming model has been actively studied for the

Managing Skew in Hadoop .
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Abstract Challenges in Big Data analytics stem not only from volume, but also variety: extreme diversity in both data types (eg, text, images, and graphs) and in operations beyond relational algebra (eg, machine learning, natural language processing, image processing,

Hadoop performance tuning-a pragmatic iterative approach
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Hadoop represents a Java-based distributed computing framework that is designed to support applications that are implemented via the MapReduce programming model. In general, workload dependent Hadoop performance optimization efforts have to focus on 3

Understanding Hadoop clusters and the network
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Understanding Hadoop Clusters and the Network Part 1. Introduction and Overview BRAD Hadoop Server Roles Data Node Task Tracker Data Node Task Tracker

Distributed processing of snort alert log using hadoop
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Abstract Snort is a famous tool for Intrusion Detection System (IDS), which is used to gather and analyse network packet in order to decide attacks through network. Until now, although processing a number of warning messages in real time, Snort is executed mainly in single

Hadoop and its evolving ecosystem
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Abstract. Socio-technical ecosystems are living organisms that grow and shrink, that change velocity, and that split from, or merge with, others. The ecosystems that surround producers of software-intensive products exhibit all of these behaviors. We report on the start of a

Leveraging big data analytics and hadoop in developing Indias healthcare services
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ABSTRACT In this paper, we analyze and reveal the benefits of Big Data Analytics and Hadoop in the applications of Healthcare where the data flow to and from is in massive volume. The developing countries like India with huge population faces various problems in

ThroughputScheduler: Learning to Schedule on Heterogeneous Hadoop Clusters.
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Abstract Hadoop is the de-facto standard for big data analytics applications. Presently available schedulers for Hadoop clusters assign tasks to nodes without regard to the capability of the nodes. We propose ThroughputScheduler, which reduces the overall job

Survey on Hadoop and Introduction to YARN
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Abstract Big Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop which will be discussed in

X-tracing Hadoop
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X-Tracing Hadoop style parallel processing masks failures and performance problems Objectives: Help Hadoop

Low-latency, high-throughput access to static global resources within the Hadoop framework
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Abstract Hadoop is an open source implementation of Googles MapReduce programming model that has recently gained popularity as a practical approach to distributed information processing. This work explores the use of memcached, an open-source distributed in-

Apache Hadoop
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specializes in efficient data structures and algorithms for large-scale distributed storage systems. He discovered a new type of balanced trees, S-trees, for optimal

Hadoop based defense solution to handle distributed denial of service (ddos) attacks
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ABSTRACT Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an

Research on job scheduling algorithm in hadoop
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Abstract On the basis of researching Fair Scheduling Strategy deeply in Hadoop cluster, the Node Health Degree is defined by constructing the relationship function between node load and job fail rate, and a job scheduling algorithm based on Node Health Degree is proposed

Integrating kerberos into apache hadoop
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Page 1. Integrating Kerberos into Apache Hadoop Kerberos Conference 2010 Owen OMalley owen@yahoo-inc.com Yahoos Hadoop Team Page 2. Kerberos Conference 2010 Who am I An architect working on Hadoop full time Mainly focused on MapReduce Tech-lead on

Big data implementation of natural disaster monitoring and alerting system in real time social network using hadoop technology
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Abstract The information generated by the social networks is exponentially higher and demand effective systems to yield effective results. In conventional techniques stay unqualified because they ignore the social related data. The existing system doesnt provide

Hadoop skeleton fault tolerance in Hadoop clusters
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ABSTRACT In the todays era of information technology and computer science storing and processing a data is very important aspect. Nowadays even a terabytes and petabytes of data is not sufficient for storing large chunks of database. Hence companies today use

Apache Hadoop NoSQL and NewSQL solutions of big data
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ABSTRACT Big Data is a popular term encompassing the use of techniques to capture, analyses, and process as well as visualize potentially large datasets in a reasonable timeframe not accessible to standard IT technologies, therefore platform, tools and software

Making hadoop MapReduce byzantine fault-tolerant
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MapReduce is a programming model and a runtime environment designed by Google for processing large data sets in its warehouse-scale machines (WSM) with hundreds to thousands of servers [2, 4]. MapReduce is becoming increasingly popular with the

A Hadoopbased Multimedia Transcoding System for Processing Social Media in the PaaS Platform of SMCCSE.
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Abstract Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS

Data availability and durability with the hadoop distributed file system
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Senior Manager of Hadoop Infrastructure at LinkedIn. This work draws on Robs experience as manager of the HDFS development team at Yahoo!. A Caltech graduate, Rob earned a PhD in computer science at Carnegie Mellon University

Content-based recommendation algorithms on the hadoop mapreduce framework
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Abstract: Content-based recommender systems are widely used to generate personal suggestions for content items based on their metadata description. However, due to the required (text) processing of these metadata, the computational complexity of the

The crossing the chasm: Sneaking a parallel file system into hadoop
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Crossing the Chasm: Sneaking a parallel file system into Hadoop PARALLEL DATA LABORATORY Carnegie Mellon University Page 2. In this work Compare and contrast large storage system architectures Internet services

Blind Men and the Elephant: Piecing together Hadoop for diagnosis
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Abstract Googles MapReduce framework enables distributed, data-intensive, 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 partition in

Big data and Hadoop with components like Flume, Pig, Hive and Jaql
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To manage this data, we have to use databases with massively parallel software running on tens, hundreds, or more than thousands of servers. So Big data platforms are used to acquire, organize and analyze these types of data. In this paper, first of all, we will acquire

Big data processing using apache hadoop in cloud system
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Abstract. The ever growing technology has resulted in the need for storing and processing excessively large amounts of data on cloud. The current volume of data is enormous and is expected to replicate over 650 times by the year 2014, out of which, 85% would be

Theia: Visual Signatures for Problem Diagnosis in Large Hadoop Clusters.
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Abstract Diagnosing performance problems in large distributed systems can be daunting as the copious volume of monitoring information available can obscure the root-cause of the problem. Automated diagnosis tools help narrow down the possible root-causes however,

Snapshots in hadoop distributed file system
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Abstract The ability to take snapshots is an essential functionality of any file system, as snapshots enable system administrators to perform data backup and recovery in case of failure. We present a low-overhead snapshot solution for HDFS, a popular distributed file

A comparative analysis of join algorithms using the hadoop map/reduce framework
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Abstract The Map/Reduce framework is a programming model recently introduced by Google Inc. to support distributed computing on very large datasets across a large number of machines. It provides a simple but yet powerful way to implement distributed applications

Running hadoop on ubuntu linux (single-node cluster)
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First, we have to generate an SSH key for the hduser user.

Hadoop and its role in modern image processing
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Abstract This paper introduces MapReduce as a distributed data processing model using open source Hadoop framework for manipulating large volume of data. The huge volume of data in the modern world, particularly multimedia data, creates new requirements for

Hadoop : What it is, how it works, and what it can do
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Mike Olson: The underlying technology was invented by Google back in their earlier days so they could usefully index all the rich textural and structural information they were collecting, and then present meaningful and actionable results to users. There was nothing on the

Handling Big (ger) Logs: Connecting ProM 6 to Apache Hadoop .
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Abstract. Within process mining the main goal is to support the analysis, improvement and apprehension of business processes. Numerous process mining techniques have been developed with that purpose. The majority of these techniques use conventional

An introduction to the Hadoop distributed file system
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The Hadoop Distributed File System (HDFS) a subproject of the Apache Hadoop project is a distributed, highly fault-tolerant file system designed to run on low-cost commodity hardware. HDFS provides high-throughput access to application data and is suitable for

Hadoop MapReduce over Lustre
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Number of Maps, R→ Number of Reduces Map output records(Key--Value pairs) organized into R par ((ons Par ((ons exist in memory. Records within a par ((on are sorted A background thread monitors the buffer, spills to disk if full Each spill generates a spill file

Evaluation of codes with inherent double replication for hadoop
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Abstract In this paper, we evaluate the efficacy, in a Hadoop setting, of two coding schemes, both possessing an inherent double replication of data. The two coding schemes belong to the class of regenerating and locally regenerating codes respectively, and these two classes

Access control for sensitive data in hadoop distributed file systems
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Abstract User access limitations are very valuable in Hadoop distributed file systems to access the sensitive and personal data. Even though, user has access to the database, the access limit check is very relevant at the time of MapReduce to control the user and to

A dynamic caching mechanism for Hadoop using Memcached
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Abstract Advancements in disk capacity have greatly surpassed those in disk access time and bandwidth. As a result disk-based storage systems are finding it increasingly difficult to cope up with the performance demands of large cluster-based systems. In an attempt to
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