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Convex Optimization for Big Data

by Volkan Cevher, Stephen Becker, Mark Schmidt , 2014
"... This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods and randomizatio ..."
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This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods

An Architecture for Big Data Analytics

by Joseph O. Chan, Joseph O. Chan
"... Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big ..."
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Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big

Big Data Begets Big Database Theory

by Dan Suciu
"... variety. The data is too big to process with current tools; it arrives too fast for optimal storage and indexing; and it is too heterogeneous to fit into a rigid schema. There is a huge pressure on database researchers to study, explain, and solve the technical challenges in big data, but we find no ..."
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variety. The data is too big to process with current tools; it arrives too fast for optimal storage and indexing; and it is too heterogeneous to fit into a rigid schema. There is a huge pressure on database researchers to study, explain, and solve the technical challenges in big data, but we find

Big Data: Technologies, Trends and Applications

by Sudhakar Singh A, Pankaj Singh B, Rakhi Garg C, P K Mishra A
"... Abstract-Big Data is an excessive amount of imprecise data in variety of formats generated from variety of sources with rapid speed. It is most buzzed terms among researcher, industry and academia. Big Data is not only limited to data perspective but it has been emerged as a stream that includes ass ..."
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Abstract-Big Data is an excessive amount of imprecise data in variety of formats generated from variety of sources with rapid speed. It is most buzzed terms among researcher, industry and academia. Big Data is not only limited to data perspective but it has been emerged as a stream that includes

Big Data Analysis using Hadoop

by Nikhitha Cyril, Arun Soman
"... In the present world, where more and more users upload data to the internet, the overall size of data that need to be stored and analyzed exceeds the capacity of traditional storage and analysis techniques. Hence, it is necessary to introduce new and efficient methods for the analysis of Big Data in ..."
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in order to extract useful information from them. Big Data has tremendous importance in almost all areas such as education, healthcare, etc. Big data is defined by its three main characteristics which are high volume, high velocity and huge variety. Hadoop is provides an efficient platform for the analysis

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

by Jianfeng Zhan
"... Abstract. Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V re-quirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates ..."
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Abstract. Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V re-quirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates

Petuum: A New Platform for Distributed Machine Learning on Big Data

by Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Yaoliang Yu - IEEE Transactions on Big Data , 2015
"... How can one build a distributed framework that allows ef-ficient deployment of a wide spectrum of modern advanced machine learning (ML) programs for industrial-scale prob-lems using Big Models (100s of billions of parameters) on Big Data (terabytes or petabytes)? Contemporary paralleliza-tion strate ..."
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How can one build a distributed framework that allows ef-ficient deployment of a wide spectrum of modern advanced machine learning (ML) programs for industrial-scale prob-lems using Big Models (100s of billions of parameters) on Big Data (terabytes or petabytes)? Contemporary paralleliza-tion

Supported Platforms

by Com Api
"... In today’s organizations, XBRL mandates and Big Data trends are producing huge, ever increasing amounts of XML and XBRL data. Now, there is finally a modern, hyper-fast engine to validate, process, transform, and query all of it: RaptorXML. Altova ® RaptorXML ® is the third-generation, hyper-fast XM ..."
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In today’s organizations, XBRL mandates and Big Data trends are producing huge, ever increasing amounts of XML and XBRL data. Now, there is finally a modern, hyper-fast engine to validate, process, transform, and query all of it: RaptorXML. Altova ® RaptorXML ® is the third-generation, hyper

Scaling Big Data Mining Infrastructure: The Twitter Experience

by Jimmy Lin, Dmitriy Ryaboy
"... The analytics platform at Twitter has experienced tremendous growth over the past few years in terms of size, complexity, number of users, and variety of use cases. In this paper, we discuss the evolution of our infrastructure and the development of capabilities for data mining on “big data”. One im ..."
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The analytics platform at Twitter has experienced tremendous growth over the past few years in terms of size, complexity, number of users, and variety of use cases. In this paper, we discuss the evolution of our infrastructure and the development of capabilities for data mining on “big data”. One

ALL RIGHTS RESERVEDSCIENCE IN HIGH DIMENSIONS: MULTIPARAMETER MODELS AND BIG DATA

by Ricky Chachra Ph. D , 2014
"... Complex multiparameter models such as in climate science, economics, systems biology, materials science, neural networks and machine learning have a large-dimensional space of undetermined parameters as well as a large-dimensional space of predicted data. These high-dimensional spaces of inputs and ..."
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and outputs pose many challenges. Recent work with a diversity of nonlinear predictive models, microscopic models in physics, and analysis of large datasets, has led to important insights. In particular, it was shown that nonlinear fits to data in a variety of multiparameter models largely rely on only a few
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