Deep Web Query Interface Understanding and Integration

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There are millions of searchable data sources on the Web and to a large extent their contents can only be reached through their own query interfaces. There is an enormous interest in making the data in these sources easily accessible. There are primarily two general approaches to achieve this objective. The first is to surface the contents of these sources from the deep Web and add the contents to the index of regular search engines. The second is to integrate the searching capabilities of these sources and support integrated access to them. In this book, we introduce the state-of-the-art techniques for extracting, understanding, and integrating the query interfaces of deep Web data sources. These techniques are critical for producing an integrated query interface for each domain. The interface serves as the mediator for searching all data sources in the concerned domain. While query interface integration is only relevant for the deep Web integration approach, the extraction and understanding of query interfaces are critical for both deep Web exploration approaches.
This book aims to provide in-depth and comprehensive coverage of the key technologies needed to create high quality integrated query interfaces automatically. The following technical issues are discussed in detail in this book: query interface modeling, query interface extraction, query interface clustering, query interface matching, query interface attribute integration, and query interface integration.
Table of Contents: Introduction / Query Interface Representation and Extraction / Query Interface Clustering and Categorization / Query Interface Matching / Query Interface Attribute Integration / Query Interface Integration / Summary and Future Research

Professional Hadoop Solutions

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The go-to guidebook for deploying Big Data solutions with Hadoop

Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth.
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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

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Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
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Hacking Web Intelligence: Open Source Intelligence and Web Reconnaissance Concepts and Techniques

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Open source intelligence (OSINT) and web reconnaissance are rich topics for infosec professionals looking for the best ways to sift through the abundance of information widely available online. In many cases, the first stage of any security assessment―that is, reconnaissance―is not given enough attention by security professionals, hackers, and penetration testers. Often, the information openly present is as critical as the confidential data.
Hacking Web Intelligence shows you how to dig into the Web and uncover the information many don't even know exists. The book takes a holistic approach that is not only about using tools to find information online but also how to link all the information and transform it into presentable and actionable intelligence. You will also learn how to secure your information online to prevent it being discovered by these reconnaissance methods.
Hacking Web Intelligence is an in-depth technical reference covering the methods and techniques you need to unearth open source information from the Internet and utilize it for the purpose of targeted attack during a security assessment. This book will introduce you to many new and leading-edge reconnaissance, information gathering, and open source intelligence methods and techniques, including metadata extraction tools, advanced search engines, advanced browsers, power searching methods, online anonymity tools such as TOR and i2p, OSINT tools such as Maltego, Shodan, Creepy, SearchDiggity, Recon-ng, Social Network Analysis (SNA), Darkweb/Deepweb, data visualization, and much more.Provides a holistic approach to OSINT and Web recon, showing you how to fit all the data together into actionable intelligenceFocuses on hands-on tools such as TOR, i2p, Maltego, Shodan, Creepy, SearchDiggity, Recon-ng, FOCA, EXIF, Metagoofil, MAT, and many moreCovers key technical topics such as metadata searching, advanced browsers and power searching, online anonymity, Darkweb / Deepweb, Social Network Analysis (SNA), and how to manage, analyze, and visualize the data you gatherIncludes hands-on technical examples and case studies, as well as a Python chapter that shows you how to create your own information-gathering tools and modify existing APIs

Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem (Addison-Wesley Data & Analytics Series)

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Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem

With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models.
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