apache spark IEEE PAPER 2016
Implementing a GPU-based Machine Learning Library on Apache Spark
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As data storage becomes increasingly commoditized, companies are collecting transactional records on the order of several petabytes that are beyond the ability of typical database software tools to store and analyze. Analysis of this big data can yield business
Machine Learning and Data Mining with Apache Spark
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Abstract. This paper deals with the concepts of machine learning and data mining social networks, which are increasingly useful for businesses to know the consumers' sentiment towards their brand. This project, intended for use by engineers at Orange France, focuses
Performance Evaluation of Apache Spark on Cray XC Systems
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Abstract:We report our experiences in porting and tuning the Apache Spark data analytics framework on the Cray XC30 (Edison) and XC40 (Cori) systems, installed at NERSC. Spark has been designed for cloud environments where local disk I/O is cheap and performance
When Apache Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration
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FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into stateof-the-art big-data computing frameworks like
SparkScore: Leveraging Apache Spark for Distributed Genomic Inference
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Abstract:The method of the efficient score statistic is used extensively to conduct inference for high throughput genomic data due to its computational efficiency and ability to accommodate simple and complex phenotypes. Inference based on these statistics can
Apache spark
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Lab Objective: Dealing with massive amounts of data often requires parallelization and cluster computing; Apache Spark is an industry standard for doing just that. In this lab we introduce the basics of PySpark, Sparks Python API, including data structures, syntax, and
Comparing apache spark and map reduce with performance analysis using k-means
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Big Data has long been the topic of fascination for Computer Science enthusiasts around the world, and has gained even more prominence in the recent times with the continuous explosion of data resulting from the likes of social media and the quest for tech giants to gain
Graysort on apache spark by databricks
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Apache Spark is a general cluster compute engine for scalable data processing. It was originally developed by researchers at UC Berkeley AMPLab . The engine is faulttolerant and is designed to run on commodity hardware. It generalizes two stage Map/Reduce to
Real-time News Recommendations using Apache Spark .
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Recommending news articles is a challenging task due to the continuous changes in the set of available news articles and the contextdependent preferences of users. Traditional recommender approaches are optimized for analyzing static data sets. In news
Modeling and simulating Apache Spark streaming applications
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Stream processing systems are used to analyze big data streams with low latency. The performance in terms of response time and throughput is crucial to ensure all arriving data are processed in time. This depends on various factors such as the complexity of used
An integrated data preprocessing framework based on apache spark for fault diagnosis of power grid equipment
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Big data techniques have been applied to power grid for the prediction and evaluation of grid conditions. However, the raw data quality can rarely meet the requirement of precise data analytics since raw data set usually contains samples with missing data to which the
spark
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Of Computer Science, Colorado State University L12.3 Professor: SHRIDEEP PALLICKARA Topics covered in this lecture ? Spark ? Software stack ? Interactive shells in Spark ? Core Spark concepts October 2019 CS555: Distributed Systems [Fall 2019] Dept. Of Computer Science
An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark
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On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway thereLate detection and manual resolutions of performance anomalies in Cloud Computing and Big Data systems lead to performance violations and financial penalties. Motivated by this issue, we propose an artificial neural network based methodology for anomaly detection
Static and dynamic big data partitioning on apache spark .
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Many of todays large datasets are organized as a graph. Due to their size it is often infeasible to process these graphs using a single machine. Therefore, many software frameworks and tools have been proposed to process graph on top of distributed
Beyond hadoop mapreduce apache tez and apache spark
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Hadoop MapReduce has become the de facto standard for processing voluminous data on large cluster of machines, however this requires any problem to be formulated into strict three-stage process composed of Map, Shuffle/Sort and Reduce. Lack of choice in inter
Predicting potential banking customer churn using apache spark ML and MLlib packages: a comparative study
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This study was conducted based on an assumption that Spark ML package has much better performance and accuracy than Spark MLlib package in dealing with big data. The used dataset in the comparison is for bank customers transactions. The Decision tree algorithm
Efficient big data analysis with apache spark in HDFS
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With the size of data increasing each day, the traditional methods of data processing have become inefficient and time consuming. Today, Facebook, Google, Twitter are generating Petabytes of data each day. This large amount of data is given the term Big Data. To
MaRe: Processing Big Data with application containers on Apache Spark
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Background Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in
A review study of apache spark in big data processing
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Why Spark becomes a hot topic in Big Data analytics Is really Apache Spark going to replace Hadoop If we involved seriously into Big Data analytics, then, should we really care about Spark Apache Spark is a lightning-fast cluster computing designed for fast
Benchmarking Apache Spark with Machine Learning Applications
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Abstract We benchmarked Apache Spark with a popular parallel machine learning training application, Distributed Stochastic Gradient Descent for Matrix Factorization and compared the Spark implementation with alternative approaches for communicating model
Cost Efective Road Traic Prediction Model using Apache Spark
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Objectives: We proposed a cost effective model to predict the traffic to inform the public about the current traffic condition to all persons who are entering the same lane. Analysis: In real time application like traffic monitoring, it needs to process huge volume of data in huge
Scalable sde filtering and inference with apache spark
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In this paper, we consider the problem of Bayesian filtering and inference for time series data modeled as noisy, discrete-time observations of a stochastic differential equation (SDE) with undetermined parameters. We develop a Metropolis algorithm to sample from the high
Big Data Analysis: Comparision of Hadoop MapReduce and Apache Spark
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In recent years, the rapid development of the Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from
Spatio-temporal hotspot computation on apache spark (gis cup)
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Large quantities of mobility data are produced by people and vehicles daily. Mining and analysis of patterns, such as hotspots, in this data can serve to improve location-based services. However, due to the massive amount of information, efficient techniques are
A study and performance comparison of MapReduce and apache spark on twitter data on hadoop cluster
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We explore Apache Spark the newest tool to analyze big data, which lets programmers perform inmemory computation on large data sets in a fault tolerant manner. MapReduce is a high-performance distributed BigData programming framework which is highly preferred
Classification approach for big data driven traffic flow prediction using apache spark
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Traffic problems are crucial issues in the rapidly developing society. Traffic flow prediction is an important problem in Intelligent Transportation Systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation
Scalability Potential of BWA DNA Mapping Algorithm on Apache Spark .
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This paper analyzes the scalability potential of embarrassingly parallel genomics applications using the Apache Spark big data framework and compares their performance with native implementations as well as with Apache Hadoop scalability. The paper uses the
Big data and apache spark : a review
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Big Data is currently a very burning topic in the fields of Computer Science and Business Intelligence, and with such a scenario at our doorstep, a humungous amount of information waits to be documented properly with emphasis on the market. By market, we mean the
Performance evaluation of apache spark on cray xc systems
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We report our experiences in porting and tuning the Apache Spark data analytics framework on the Cray XC30 (Edison) and XC40 (Cori) systems, installed at NERSC. Spark has been designed for cloud environments where local disk I/O is cheap and performance is
Early detection and prediction of amblyopia by predictive analytics using apache spark
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Amblyopia is the turmoil of visual discernment which regularly impacts the children. It is a visual inability on account of the despicable working of eye and mind through the optic nerve. Unless viably treated in adolescence, amblyopia unquestionably continues into
Deepspark: Spark-based deep learning supporting asynchronous updates and caffe compatibility
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challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learn- ing framework that simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration
On realizing rough set algorithms with apache spark
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Page 1. On Realizing Rough Set Algorithms with Apache Spark Kuo-Min Huang, Hsin-Yu Chen Kan-Lin Hsiung † Innovation Center for Big Data and Digital Convergence Department of Electrical Engineering, Yuan Ze University 135 Yuan-Tung Road, Chung-Li, TAIWAN 32003
Towards Distributed Model Analytics with Apache Spark .
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The growing number of models and other related artefacts in model-driven engineering has recently led to the emergence of approaches and tools for analyzing and managing them on a large scale. The framework SAMOS applies techniques inspired by information retrievalApache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling
STREAMING TWITTER DATA ANALYSIS USING SPARK FOR EFFECTIVE JOB SEARCH.
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accuracy. Apache Spark the trendy big data processing engine that offers faster solutions compared to Hadoop, can be effectively utilized in finding patterns of relevance useful for the common man from these sites. Recently
Applications of Apache Spark for Numerical Simulation
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We analyze the viability of Apache Spark for numerical simulation applications. To simulate gravitational lensing, we ray-trace approximately 10 8 rays through a galaxy, followed by a spatial query. For optimal performance, we implement custom partitioning
A scalable, secure and real-time healthcare analytics framework with apache spark
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A Big Data analytics framework with related computing technologies can process huge amounts of real-time data to obtain tremendous insights for effective clinical decision making in the healthcare research. In this paper, we propose a healthcare analytics framework with
Sentiment Analysis of Twitter Streaming Data for Recommendation using Apache Spark
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Revised 17th 201 Accepted 11th 201 Online 30th 2017 Abstract Twitter is free social networking micro blogging service. In that micro-blogging allows to registered members to broadcasting the short posts also called tweets. It can broadcast the tweets by
A comparison of machine learning techniques for android malware detection using apache spark
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Wide-scale popularity of Android devices has necessitated the need of having effective means for detection of malicious applications. Machine learning based classification of android applications require training and testing on a large dataset. Motivated by these GraphX. This book also discusses how to tune Spark parameters for production scenarios and how to write robust applications in Apache Spark using Scala in cloud computing environment. The book is organized into 11 chapters
Network Anomaly Detection by Means of Machine Learning: Random Forest Approach with Apache Spark
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Nowadays the network security is a crucial issue and traditional intrusion detection systems are not a sufficient way. Hence the intelligent detection systems should have a major role in network security by taking into consideration to process the network big data and predict the
Survey on frameworks for distributed computing: Hadoop, spark and storm
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sys- tems. Apache Spark is a data parallel general-purpose batch-processing engine. Work Hadoop MapReduce. Apache Spark has its Streaming API project that allows for continuous processing via short interval batches. Similar
Big data analysis: Apache storm perspective
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information in small batches and uses MapReduce framework to process the data and is called batch processing software . 3. Apache Spark Apache Spark project is open source based for processing fast and large-scale data, which relies on cluster computing system
Future of big data application and apache spark vs. map reduce
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Department of Computer Science Engineering, Kurukshetra University, Haryana, India Abstract-Now a days, Apache project is working in a new system for social networks and healthcare system that is Apache Spark . It is a fast expressive cluster computing engine CSE PROJECTS