High Performance Data MiningHigh Performance Data Mining



High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area.

Author: Yike Guo

Publisher: Springer

ISBN: 1475784155

Category:

Page: 106

View: 911

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Scalable High Performance Computing for Knowledge Discovery and Data MiningScalable High Performance Computing for Knowledge Discovery and Data Mining



Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area.

Author: Paul Stolorz

Publisher: Springer Science & Business Media

ISBN: 9781461556695

Category:

Page: 100

View: 166

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

High Performance Data MiningHigh Performance Data Mining



Magnify, Inc. & National Center for Data Mining, University of Illinois at Chicago, USA His promises were, as he then wa But his performance, ...

Author: Yike Guo

Publisher: Springer Science & Business Media

ISBN: 9780306470110

Category:

Page: 106

View: 221

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

High Performance Data Mining and Big Data AnalyticsHigh Performance Data Mining and Big Data Analytics



The objective of this book is to provide a historical and comprehensive view of the recent trend toward high-performance computing technologies, especially as it relates to big data analytics and high-performance data mining.

Author: Khosrow Hassibi

Publisher: Createspace Independent Pub

ISBN: 1495301079

Category:

Page: 294

View: 776

The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late '80s and the early '90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data mining science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual's transaction spending patterns have been used since early '90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. While a decade ago, the masses did not know how their detailed data were being used by corporations for decision making, today they are fully aware of that fact. Many people, especially the millennial generation, voluntarily provide detailed information about themselves. Today people know that any mouse click they generate, any comment they write, any transaction they perform, and any location they go to, may be captured and analyzed for some business purpose. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology's true relevance. I wrote this book to provide an objective view of analytics trends today. I have written it in complete independence, and solely as a personal passion. As a result, the views expressed in this book are those of the author and do not necessarily represent the views of, and should not be attributed to, any vendor or employer.Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data. High-performance computing architectures have been devised to address the need for handling big data, not only from a transaction processing standpoint but also from a tactical and strategic analytics viewpoint. The success of big data analytics in large web companies has created a rush toward understanding the impact of new big data technologies in classic analytics environments that already employ a multitude of legacy analytics technologies. There is a wide variety of readings about big data, high-performance computing for analytics, massively parallel processing (MPP) databases, Hadoop and its ecosystem, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and big data analytics. However, none of these readings provides an overview of these topics in a single document. The objective of this book is to provide a historical and comprehensive view of the recent trend toward high-performance computing technologies, especially as it relates to big data analytics and high-performance data mining. The book also emphasizes the impact of big data on requiring a rethinking of every aspect of the analytics life cycle, from data management, to data mining and analysis, to deployment.As a result of interactions with different stakeholders in classic organizations, I realized there was a need for a more holistic view of big data analytics' impact across classic organizations, and also the impact of high-performance computing techniques on legacy data mining. Whether you are an executive, manager, data scientist, analyst, sales or IT staff, the holistic and broad overview provided in the book will help in grasping the important topics in big data analytics and its potential impact in your organizations.

High Performance Data MiningHigh Performance Data Mining



High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area.

Author: Yike Guo

Publisher: Springer Science & Business Media

ISBN: 9780792377450

Category:

Page: 105

View: 326

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

High Performance Computing HiPC 2007High Performance Computing HiPC 2007



High Performance Data Mining - Application for Discovery of Patterns in the Global Climate System Vipin Kumar William Norris Professor, Head of the Computer ...

Author: Srinivas Aluru

Publisher: Springer Science & Business Media

ISBN: 9783540772194

Category:

Page: 663

View: 204

This book examines the issues surrounding the energy options available to nations in Europe and the Mediterranean. As the supply of fossil fuels dwindles and extraction costs rise, it examines short- and long-term strategies including lifestyle change.

Emerging Technologies in Knowledge Discovery and Data MiningEmerging Technologies in Knowledge Discovery and Data Mining



High Performance Data Mining and Applications Overview Chao Xie1 and Jieyue He2 1 Department of Computer Science, Georgia State University, Atlanta, ...

Author: Takashi Washio

Publisher: Springer Science & Business Media

ISBN: 9783540770169

Category:

Page: 678

View: 205

This book constitutes the thoroughly refereed post-proceedings of three workshops and an industrial track held in conjunction with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China in May 2007. The 62 revised full papers presented together with an overview article to each workshop were carefully reviewed and selected from 355 submissions.

High Performance Parallel Database Processing and Grid DatabasesHigh Performance Parallel Database Processing and Grid Databases



chapter focuses on data mining, another data-intensive application whereby parallelism can be used in order to achieve high performance.

Author: David Taniar

Publisher: John Wiley & Sons

ISBN: 9780470391358

Category:

Page: 576

View: 464

The latest techniques and principles of parallel and grid database processing The growth in grid databases, coupled with the utility of parallel query processing, presents an important opportunity to understand and utilize high-performance parallel database processing within a major database management system (DBMS). This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It presents a balanced treatment of the theoretical and practical aspects of high-performance databases to demonstrate how parallel query is executed in a DBMS, including concepts, algorithms, analytical models, and grid transactions. High-Performance Parallel Database Processing and Grid Databases serves as a valuable resource for researchers working in parallel databases and for practitioners interested in building a high-performance database. It is also a much-needed, self-contained textbook for database courses at the advanced undergraduate and graduate levels.

Proceedings of the Sixth SIAM International Conference on Data MiningProceedings of the Sixth SIAM International Conference on Data Mining



... SIAM International Conference on Data Mining continues the tradition of ... high - performance data mining ; scientific data mining ; link analysis ...

Author: Joydeep Ghosh

Publisher: SIAM

ISBN: 089871611X

Category:

Page: 658

View: 326

The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.

High Performance Computing for Computational Science VECPAR 2002High Performance Computing for Computational Science VECPAR 2002



High Performance Data Mining⋆ Vipin Kumar, Mahesh V. Joshi, Eui-Hong (Sam) Han, Pang-Ning Tan, and Michael Steinbach University of Minnesota 4-192EE/CSci ...

Author: José M.L.M. Palma

Publisher: Springer

ISBN: 9783540365693

Category:

Page: 738

View: 922

The 5th edition of the VECPAR series of conferences marked a change of the conference title. The full conference title now reads VECPAR 2002 — 5th Int- national Conference on High Performance Computing for Computational S- ence. This re?ects more accurately what has been the main emphasis of the conference since its early days in 1993 – the use of computers for solving pr- lems in science and engineering. The present postconference book includes the best papers and invited talks presented during the three days of the conference, held at the Faculty of Engineering of the University of Porto (Portugal), June 26–28 2002. The book is organized into 8 chapters, which as a whole appeal to a wide research community, from those involved in the engineering applications to those interested in the actual details of the hardware or software implementation, in line with what, in these days, tends to be considered as Computational Science and Engineering (CSE). The book comprises a total of 49 papers, with a prominent position reserved for the four invited talks and the two ?rst prizes of the best student paper competition.

Big Data Data Mining and Machine LearningBig Data Data Mining and Machine Learning



In this book, Jared Dean offers an accessible and thorough review of the current state of big data analytics and the growing trend toward high performance computing architectures.

Author: Jared Dean

Publisher: John Wiley & Sons

ISBN: 9781118618042

Category:

Page: 288

View: 783

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

BIG DATA TOOLS SAS VISUAL STATISTICS SAS HIGH PERFORMANCE ANALYTICS AND SAS IN MEMORY STATISTICSBIG DATA TOOLS SAS VISUAL STATISTICS SAS HIGH PERFORMANCE ANALYTICS AND SAS IN MEMORY STATISTICS



High-performance SAS Data Mining uses large volumes of data to perform data mining tasks such as pattern discovery, prediction and other typical mining ...

Author: César Pérez López

Publisher: Lulu Press, Inc

ISBN: 9781008984028

Category:

Page:

View: 138

SAS Visual Statistics is an add-on for SAS Visual Analytics that allows you to develop and test models using the in-memory capabilities of SAS LASR Analytic Server. SAS Analytics Visual Explorer (the explorer) allows you to explore, investigate, and visualize big data sources to discover relevant patterns. SAS Visual Estatistics extends these capabilities to create, test, and compare models based on the patterns discovered in the explorer. SAS Visual Estatistics can export the programming code, before or after performing the model comparison, for use with other SAS products and to put the model into production. SAS High-performance Analytics uses large volumes of data to perform data mining tasks such as pattern discovery, prediction and other typical mining tasks. It allows predictive models to be developed from whole data, not just subsets, so accurate insights can be obtained in minutes or seconds. The use of sophisticated analytics, the ability to work with large numbers of variables, frequent modelling iterations and other advanced features provide competitive advantages. With SAS In-Memory Statistics for Hadoop, is possible to work with rapid interactive analysis, statistical algorithms and machine learning techniques, random decision forests, descriptive statistics, analytical data preparation and Interactive in-memory programming.

High Performance Computing and NetworkingHigh Performance Computing and Networking



An enterprise data mining architecture should be flexible enough to scale well in all these cases . This will require access to high - performance ...

Author: Peter Sloot

Publisher: Springer Science & Business Media

ISBN: 3540658211

Category:

Page: 1320

View: 247

This book constitutes the refereed proceedings of the 7th International Conference on High-Performance Computing and Networking, HPCN Europe 1999, held in Amsterdam, The Netherlands in April 1999. The 115 revised full papers presented were carefully selected from a total of close to 200 conference submissions as well as from submissions for various topical workshops. Also included are 40 selected poster presentations. The conference papers are organized in three tracks: end-user applications of HPCN, computational science, and computer science; additionally there are six sections corresponding to topical workshops.

Large Scale Parallel Data MiningLarge Scale Parallel Data Mining



We describe a high performance DSTP server we are designing called Osiris, which is a component of a distributed and high performance data mining system we ...

Author: Mohammed J. Zaki

Publisher: Springer

ISBN: 9783540465027

Category:

Page: 260

View: 840

With the unprecedented growth-rate at which data is being collected and stored electronically today in almost all fields of human endeavor, the efficient extraction of useful information from the data available is becoming an increasing scientific challenge and a massive economic need. This book presents thoroughly reviewed and revised full versions of papers presented at a workshop on the topic held during KDD'99 in San Diego, California, USA in August 1999 complemented by several invited chapters and a detailed introductory survey in order to provide complete coverage of the relevant issues. The contributions presented cover all major tasks in data mining including parallel and distributed mining frameworks, associations, sequences, clustering, and classification. All in all, the volume presents the state of the art in the young and dynamic field of parallel and distributed data mining methods. It will be a valuable source of reference for researchers and professionals.

Principles of Data Mining and Knowledge DiscoveryPrinciples of Data Mining and Knowledge Discovery



A Tutorial Introduction to High Performance Data Mining Robert Grossman Magnify , Inc. and University of Illinois at Chicago Abstract The goal of the ...

Author: Jan Komorowski

Publisher: Springer Science & Business Media

ISBN: 3540632239

Category:

Page: 396

View: 705

This book constitutes the refereed proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '97, held in Trondheim, Norway, in June 1997. The volume presents a total of 38 revised full papers together with abstracts of one invited talk and four tutorials. Among the topics covered are data and knowledge representation, statistical and probabilistic methods, logic-based approaches, man-machine interaction aspects, AI contributions, high performance computing support, machine learning, automated scientific discovery, quality assessment, and applications.

Current Trends in High Performance Computing and Its ApplicationsCurrent Trends in High Performance Computing and Its Applications



A Platform for Parallel Data Mining on Cluster System Shaochun Wu , Gengfeng Wu , Zhaochun Yu , and Hua Ban School of Computer Engineering and Science ...

Author: Wu Zhang

Publisher: Springer Science & Business Media

ISBN: 3540257853

Category:

Page: 639

View: 956

This volume contains 88 research articles written by prominent researchers. The articles are chosen from a large international conference on high performance computing and its applications held in Shanghai, China. Topics covered include a variety of subjects in modern high performance computing and its applications, such as the design and analysis of high performance computing algorithms, tools and platforms, and their scientific, engineering, medical, and industrial applications. The book serves as an excellent reference work for graduate students and researchers working with high performance computing for problems in science and engineering.

High Performance Computing for Big DataHigh Performance Computing for Big Data



This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications.

Author: Chao Wang

Publisher: CRC Press

ISBN: 0367572893

Category:

Page: 268

View: 820

This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It covers fundamental issues in Big Data research, including emerging architectures for data-intensive applications, novel analytical strategies to boost data processing, and cutting-edge applications.