Handbook Of Massive Data Sets

Download Handbook Of Massive Data Sets full books in PDF, epub, and Kindle. Read online Handbook Of Massive Data Sets ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Handbook of Massive Data Sets

Author : James Abello
Publisher : Springer
ISBN 13 : 1461500052
Total Pages : 1209 pages
Book Rating : 4.4/5 (615 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Massive Data Sets by : James Abello

Download or read book Handbook of Massive Data Sets written by James Abello and published by Springer. This book was released on 2013-12-21 with total page 1209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.

Mining of Massive Datasets

Author : Jure Leskovec
Publisher : Cambridge University Press
ISBN 13 : 1107077230
Total Pages : 480 pages
Book Rating : 4.1/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Mining of Massive Datasets by : Jure Leskovec

Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Handbook of Statistical Analysis and Data Mining Applications

Author : Robert Nisbet
Publisher : Elsevier
ISBN 13 : 0124166458
Total Pages : 822 pages
Book Rating : 4.1/5 (241 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Statistical Analysis and Data Mining Applications by : Robert Nisbet

Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Robert Nisbet and published by Elsevier. This book was released on 2017-11-09 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Algorithms and Data Structures for Massive Datasets

Author : Dzejla Medjedovic
Publisher : Simon and Schuster
ISBN 13 : 1638356564
Total Pages : 302 pages
Book Rating : 4.6/5 (383 download)

DOWNLOAD NOW!

Book Synopsis Algorithms and Data Structures for Massive Datasets by : Dzejla Medjedovic

Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-08-16 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting

Handbook of Big Data

Author : Peter Bühlmann
Publisher : CRC Press
ISBN 13 : 1482249081
Total Pages : 480 pages
Book Rating : 4.4/5 (822 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Big Data by : Peter Bühlmann

Download or read book Handbook of Big Data written by Peter Bühlmann and published by CRC Press. This book was released on 2016-02-22 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical

Kernel Based Algorithms for Mining Huge Data Sets

Author : Te-Ming Huang
Publisher : Springer Science & Business Media
ISBN 13 : 3540316817
Total Pages : 266 pages
Book Rating : 4.5/5 (43 download)

DOWNLOAD NOW!

Book Synopsis Kernel Based Algorithms for Mining Huge Data Sets by : Te-Ming Huang

Download or read book Kernel Based Algorithms for Mining Huge Data Sets written by Te-Ming Huang and published by Springer Science & Business Media. This book was released on 2006-03-02 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.

Frontiers in Massive Data Analysis

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287812
Total Pages : 190 pages
Book Rating : 4.3/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Frontiers in Massive Data Analysis by : National Research Council

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

Research Handbook on Big Data Law

Author : Roland Vogl
Publisher : Edward Elgar Publishing
ISBN 13 : 1788972821
Total Pages : 544 pages
Book Rating : 4.7/5 (889 download)

DOWNLOAD NOW!

Book Synopsis Research Handbook on Big Data Law by : Roland Vogl

Download or read book Research Handbook on Big Data Law written by Roland Vogl and published by Edward Elgar Publishing. This book was released on 2021-05-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains.

Data Processing Handbook for Complex Biological Data Sources

Author : Gauri Misra
Publisher : Academic Press
ISBN 13 : 0128172800
Total Pages : 188 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Data Processing Handbook for Complex Biological Data Sources by : Gauri Misra

Download or read book Data Processing Handbook for Complex Biological Data Sources written by Gauri Misra and published by Academic Press. This book was released on 2019-03-23 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing

Bad Data Handbook

Author : Q. Ethan McCallum
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449324975
Total Pages : 264 pages
Book Rating : 4.4/5 (493 download)

DOWNLOAD NOW!

Book Synopsis Bad Data Handbook by : Q. Ethan McCallum

Download or read book Bad Data Handbook written by Q. Ethan McCallum and published by "O'Reilly Media, Inc.". This book was released on 2012-11-07 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis

Handbook of Big Geospatial Data

Author : Martin Werner
Publisher : Springer Nature
ISBN 13 : 3030554627
Total Pages : 641 pages
Book Rating : 4.0/5 (35 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Big Geospatial Data by : Martin Werner

Download or read book Handbook of Big Geospatial Data written by Martin Werner and published by Springer Nature. This book was released on 2021-05-07 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data. It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique. This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data.

Handbook of Big Data Privacy

Author : Kim-Kwang Raymond Choo
Publisher : Springer Nature
ISBN 13 : 3030385574
Total Pages : 397 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Big Data Privacy by : Kim-Kwang Raymond Choo

Download or read book Handbook of Big Data Privacy written by Kim-Kwang Raymond Choo and published by Springer Nature. This book was released on 2020-03-18 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook. The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks. The final part of this handbook is focused on analyzing cyber threats applicable to the big data environments. It offers an in-depth review of attacks applicable to big data platforms in smart grids, smart farming, FinTech, and health sectors. Multiple solutions are presented to detect, prevent and analyze cyber-attacks and assess the impact of malicious payloads to those environments. This handbook provides information for security and privacy experts in most areas of big data including; FinTech, Industry 4.0, Internet of Things, Smart Grids, Smart Farming and more. Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry 4.0 and network analysts will also find this handbook useful as a reference.

Handbook of Research on Cloud Computing and Big Data Applications in IoT

Author : Gupta, B. B.
Publisher : IGI Global
ISBN 13 : 1522584080
Total Pages : 609 pages
Book Rating : 4.5/5 (225 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Research on Cloud Computing and Big Data Applications in IoT by : Gupta, B. B.

Download or read book Handbook of Research on Cloud Computing and Big Data Applications in IoT written by Gupta, B. B. and published by IGI Global. This book was released on 2019-04-12 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, cloud computing, big data, and the internet of things (IoT) are becoming indubitable parts of modern information and communication systems. They cover not only information and communication technology but also all types of systems in society including within the realms of business, finance, industry, manufacturing, and management. Therefore, it is critical to remain up-to-date on the latest advancements and applications, as well as current issues and challenges. The Handbook of Research on Cloud Computing and Big Data Applications in IoT is a pivotal reference source that provides relevant theoretical frameworks and the latest empirical research findings on principles, challenges, and applications of cloud computing, big data, and IoT. While highlighting topics such as fog computing, language interaction, and scheduling algorithms, this publication is ideally designed for software developers, computer engineers, scientists, professionals, academicians, researchers, and students.

Handbook of Research on Innovations in Database Technologies and Applications

Author : Viviana E. Ferraggine
Publisher : IGI Global
ISBN 13 : 1605662437
Total Pages : 986 pages
Book Rating : 4.6/5 (56 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Research on Innovations in Database Technologies and Applications by : Viviana E. Ferraggine

Download or read book Handbook of Research on Innovations in Database Technologies and Applications written by Viviana E. Ferraggine and published by IGI Global. This book was released on 2009-01-01 with total page 986 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a wide compendium of references to topics in the field of the databases systems and applications"--Provided by publisher.

Handbook On Big Data And Machine Learning In The Physical Sciences (In 2 Volumes)

Author :
Publisher : World Scientific
ISBN 13 : 9811204586
Total Pages : 1001 pages
Book Rating : 4.8/5 (112 download)

DOWNLOAD NOW!

Book Synopsis Handbook On Big Data And Machine Learning In The Physical Sciences (In 2 Volumes) by :

Download or read book Handbook On Big Data And Machine Learning In The Physical Sciences (In 2 Volumes) written by and published by World Scientific. This book was released on 2020-03-10 with total page 1001 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.

Maximizing Social Science Research Through Publicly Accessible Data Sets

Author : Perry, S. Marshall
Publisher : IGI Global
ISBN 13 : 1522536175
Total Pages : 328 pages
Book Rating : 4.5/5 (225 download)

DOWNLOAD NOW!

Book Synopsis Maximizing Social Science Research Through Publicly Accessible Data Sets by : Perry, S. Marshall

Download or read book Maximizing Social Science Research Through Publicly Accessible Data Sets written by Perry, S. Marshall and published by IGI Global. This book was released on 2017-10-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making research in all fields of study readily available is imperative in order to circulate new information and upcoming trends. This is possible through the efficient utilization of collections of information. Maximizing Social Science Research Through Publicly Accessible Data Sets is an essential reference source for the latest academic perspectives on a wide range of methodologies and large data sets with the purpose of enhancing research in the areas of human society and social relationships. Featuring coverage on a broad range of topics such as student achievement, teacher efficacy, and instructional leadership, this book is ideally designed for academicians, researchers, and practitioners seeking material on the availability and distribution methods of research content.

The Data Science Handbook

Author : Field Cady
Publisher : John Wiley & Sons
ISBN 13 : 1119092949
Total Pages : 420 pages
Book Rating : 4.1/5 (19 download)

DOWNLOAD NOW!

Book Synopsis The Data Science Handbook by : Field Cady

Download or read book The Data Science Handbook written by Field Cady and published by John Wiley & Sons. This book was released on 2017-02-28 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.