IBM Content Analytics Version 2.2 PDF Download

Author: Wei-Dong Jackie Zhu
Publisher:
ISBN: 9780738435282
Category : Data mining
Languages : en
Pages : 0

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Author: Wei-Dong (Jackie) Zhu
Publisher: IBM Redbooks
ISBN: 0738439428
Category : Computers
Languages : en
Pages : 598

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Book Description
IBM® WatsonTM Content Analytics (Content Analytics) Version 3.0 (formerly known as IBM Content Analytics with Enterprise Search (ICAwES)) helps you to unlock the value of unstructured content to gain new actionable business insight and provides the enterprise search capability all in one product. Content Analytics comes with a set of tools and a robust user interface to empower you to better identify new revenue opportunities, improve customer satisfaction, detect problems early, and improve products, services, and offerings. To help you gain the most benefits from your unstructured content, this IBM Redbooks® publication provides in-depth information about the features and capabilities of Content Analytics, how the content analytics works, and how to perform effective and efficient content analytics on your content to discover actionable business insights. This book covers key concepts in content analytics, such as facets, frequency, deviation, correlation, trend, and sentimental analysis. It describes the content analytics miner, and guides you on performing content analytics using views, dictionary lookup, and customization. The book also covers using IBM Content Analytics Studio for domain-specific content analytics, integrating with IBM Content Classification to get categories and new metadata, and interfacing with IBM Cognos® Business Intelligence (BI) to add values in BI reporting and analysis, and customizing the content analytics miner with APIs. In addition, the book describes how to use the enterprise search capability for the discovery and retrieval of documents using various query and visual navigation techniques, and customization of crawling, parsing, indexing, and runtime search to improve search results. The target audience of this book is decision makers, business users, and IT architects and specialists who want to understand and analyze their enterprise content to improve and enhance their business operations. It is also intended as a technical how-to guide for use with the online IBM Knowledge Center for configuring and performing content analytics and enterprise search with Content Analytics.

Author: IBM Redbooks
Publisher:
ISBN: 9780738434605
Category : Computers
Languages : en
Pages : 480

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Book Description

Author: Alan Bluck
Publisher: BPB Publications
ISBN: 9390684498
Category : Computers
Languages : en
Pages : 550

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Book Description
Guidance for successful installation of a wide range of IBM software products Ê KEY FEATURESÊÊ _ Complete installation guide of IBM software systems, Redhat Enterprise, IBM Cloud, and Docker. _ Expert-led demonstration on complete configuration and implementation of IBM software solutions. _ Includes best practices and efficient techniques adopted by banks, financial services, and insurance companies. DESCRIPTIONÊ This book provides instructions for installation, configuration and troubleshooting sections to improve the IT support productivity and fast resolution of issues that arise. It covers readers' references that are available online and also step-by-step procedures required for a successful installation of a broad range ofÊ IBM Data Analytics products.Ê This book provides a holistic in-depth knowledge for students, software architects, installation specialists, and developers of Data Analysis software and a handbook for data analysts who want a single source of information on IBM Data Analysis Software products. This book provides a single resource that covers the latest available IBM Data Analysis software on the most recent RedHat Linux and IBM Cloud platforms. This book includes comprehensive technical guidance, enabling IT professionals to gain an in-depth knowledge of the installation of a broad range of IBM Software products across different operating systems.Ê WHAT YOU WILL LEARN _ Step-by-step installation and configuration of IBM Watson Analytics. _ Managing RedHat Enterprise Systems and IBM Cloud Platforms. _ Installing, configuring, and managing IBM StoredIQ. _ Best practices to administer and maintain IBM software packages. _ Upgrading VMware stations and installing Docker. WHO THIS BOOK IS FORÊÊ This book is a go-to guide for IT professionals who are primarilyÊ Solution Architects, Implementation Experts, or Technology Consultants of IBM Software suites. This will also be a useful guide for IT managers who are looking to adopt and enable their enterprise with IBM products. TABLE OF CONTENTS 1. Getting Started with IBM Resources for Analytics 2. IBM Component Software Compatibility Matrix 3. IBM Download Procedures 4. On-Premise Server Configurations and Prerequisites 5. IBM Fix Packs 6. IBM Cloud PAK Systems 7. RedHat OpenShift 4.x Installations 8. IBM Cloud Private System 9. Base VMWare System Platform 10. IBM Cloud Private Cluster on CentOS 8.0 11. UIMA Pipeline and Java Code Extensions 12. IBM Watson Explorer Foundational Components V12 13. IBM Watson Explorer oneWEX 12.0.3 14. IBM StoredIQ for Legal APPENDIX References and End of Life Support

Author: Dr. Alfio Gliozzo
Publisher: IBM Redbooks
ISBN: 073844264X
Category : Computers
Languages : en
Pages : 130

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Book Description
The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® WatsonTM cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 1, introduces cognitive computing, its motivating factors, history, and basic concepts. This volume describes the industry landscape for cognitive computing and introduces Watson, the cognitive computing offering from IBM. It also describes the nature of the question-answering (QA) challenge that is represented by the Jeopardy! quiz game and it provides a high-level overview of the QA system architecture (DeepQA), developed for Watson to play the game. This volume charts the evolution of the Watson Developer Cloud, from the initial DeepQA implementation. This book also introduces the concept of domain adaptation and the processes that must be followed to adapt the various Watson services to specific domains.

Author: Mike Ebbers
Publisher: IBM Redbooks
ISBN: 0738437638
Category : Computers
Languages : en
Pages : 70

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Book Description
Payment fraud can be defined as an intentional deception or misrepresentation that is designed to result in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose nearly $1 trillion USD annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics. This IBM® Redbooks® publication begins with the business process flows of several industries, such as banking, property/casualty insurance, and tax revenue, where payment fraud is a significant problem. This book then shows how to incorporate technological advancements that help you move from a post-payment to pre-payment fraud detection architecture. Subsequent chapters describe a solution that is specific to the banking industry that can be easily extrapolated to other industries. This book describes the benefits of doing fraud detection on IBM System z®. This book is intended for financial decisionmakers, consultants, and architects, in addition to IT administrators.

Author: Joseph Dain
Publisher: IBM Redbooks
ISBN: 073845902X
Category : Computers
Languages : en
Pages : 108

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Book Description
This IBM® Redpaper publication explains how IBM Spectrum® Discover integrates with the IBM Watson® Knowledge Catalog (WKC) component of IBM Cloud® Pak for Data (IBM CP4D) to make the enriched catalog content in IBM Spectrum Discover along with the associated data available in WKC and IBM CP4D. From an end-to-end IBM solution point of view, IBM CP4D and WKC provide state-of-the-art data governance, collaboration, and artificial intelligence (AI) and analytics tools, and IBM Spectrum Discover complements these features by adding support for unstructured data on large-scale file and object storage systems on premises and in the cloud. Many organizations face challenges to manage unstructured data. Some challenges that companies face include: Pinpointing and activating relevant data for large-scale analytics, machine learning (ML) and deep learning (DL) workloads. Lacking the fine-grained visibility that is needed to map data to business priorities. Removing redundant, obsolete, and trivial (ROT) data and identifying data that can be moved to a lower-cost storage tier. Identifying and classifying sensitive data as it relates to various compliance mandates, such as the General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standards (PCI-DSS), and the Health Information Portability and Accountability Act (HIPAA). This paper describes how IBM Spectrum Discover provides seamless integration of data in IBM Storage with IBM Watson Knowledge Catalog (WKC). Features include: Event-based cataloging and tagging of unstructured data across the enterprise. Automatically inspecting and classifying over 1000 unstructured data types, including genomics and imaging specific file formats. Automatically registering assets with WKC based on IBM Spectrum Discover search and filter criteria, and by using assets in IBM CP4D. Enforcing data governance policies in WKC in IBM CP4D based on insights from IBM Spectrum Discover, and using assets in IBM CP4D. Several in-depth use cases are used that show examples of healthcare, life sciences, and financial services. IBM Spectrum Discover integration with WKC enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of data. The integration improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.

Author: Joseph Dain
Publisher: IBM Redbooks
ISBN: 0738457868
Category : Computers
Languages : en
Pages : 152

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Book Description
This IBM® Redpaper publication provides a comprehensive overview of the IBM Spectrum® Discover metadata management software platform. We give a detailed explanation of how the product creates, collects, and analyzes metadata. Several in-depth use cases are used that show examples of analytics, governance, and optimization. We also provide step-by-step information to install and set up the IBM Spectrum Discover trial environment. More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data such as: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.

Author: Lydia Parziale
Publisher: IBM Redbooks
ISBN: 0738441864
Category : Computers
Languages : en
Pages : 218

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Book Description
Regarding online transaction processing (OLTP) workloads, IBM® z SystemsTM platform, with IBM DB2®, data sharing, Workload Manager (WLM), geoplex, and other high-end features, is the widely acknowledged leader. Most customers now integrate business analytics with OLTP by running, for example, scoring functions from transactional context for real-time analytics or by applying machine-learning algorithms on enterprise data that is kept on the mainframe. As a result, IBM adds investment so clients can keep the complete lifecycle for data analysis, modeling, and scoring on z Systems control in a cost-efficient way, keeping the qualities of services in availability, security, reliability that z Systems solutions offer. Because of the changed architecture and tighter integration, IBM has shown, in a customer proof-of-concept, that a particular client was able to achieve an orders-of-magnitude improvement in performance, allowing that client's data scientist to investigate the data in a more interactive process. Open technologies, such as Predictive Model Markup Language (PMML) can help customers update single components instead of being forced to replace everything at once. As a result, you have the possibility to combine your preferred tool for model generation (such as SAS Enterprise Miner or IBM SPSS® Modeler) with a different technology for model scoring (such as Zementis, a company focused on PMML scoring). IBM SPSS Modeler is a leading data mining workbench that can apply various algorithms in data preparation, cleansing, statistics, visualization, machine learning, and predictive analytics. It has over 20 years of experience and continued development, and is integrated with z Systems. With IBM DB2 Analytics Accelerator 5.1 and SPSS Modeler 17.1, the possibility exists to do the complete predictive model creation including data transformation within DB2 Analytics Accelerator. So, instead of moving the data to a distributed environment, algorithms can be pushed to the data, using cost-efficient DB2 Accelerator for the required resource-intensive operations. This IBM Redbooks® publication explains the overall z Systems architecture, how the components can be installed and customized, how the new IBM DB2 Analytics Accelerator loader can help efficient data loading for z Systems data and external data, how in-database transformation, in-database modeling, and in-transactional real-time scoring can be used, and what other related technologies are available. This book is intended for technical specialists and architects, and data scientists who want to use the technology on the z Systems platform. Most of the technologies described in this book require IBM DB2 for z/OS®. For acceleration of the data investigation, data transformation, and data modeling process, DB2 Analytics Accelerator is required. Most value can be achieved if most of the data already resides on z Systems platforms, although adding external data (like from social sources) poses no problem at all.

Author: Marcus Oppitz
Publisher: Springer
ISBN: 3319611615
Category : Computers
Languages : en
Pages : 624

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Book Description
This book combines the three dimensions of technology, society and economy to explore the advent of today’s cloud ecosystems as successors to older service ecosystems based on networks. Further, it describes the shifting of services to the cloud as a long-term trend that is still progressing rapidly.The book adopts a comprehensive perspective on the key success factors for the technology – compelling business models and ecosystems including private, public and national organizations. The authors explore the evolution of service ecosystems, describe the similarities and differences, and analyze the way they have created and changed industries. Lastly, based on the current status of cloud computing and related technologies like virtualization, the internet of things, fog computing, big data and analytics, cognitive computing and blockchain, the authors provide a revealing outlook on the possibilities of future technologies, the future of the internet, and the potential impacts on business and society.