Books in Information systems

Conceptual Design for Interactive Systems

1st Edition
March 11, 2015
Avi Parush
English
Paperback
9 7 8 0 1 2 4 1 9 9 6 9 9
eBook
9 7 8 0 1 2 4 1 9 9 8 3 5

Conceptual Design for Interactive Systems: Designing for Performance and User Experience provides readers with a comprehensive guide to the steps necessary to take the leap from research and requirements to product design. The text presents a proven strategy for transforming research into a conceptual model, discussing the iterative process that allows users to build the essential foundation for a successful interactive system, while also taking the users’ mental model into consideration. Readers will gain a better understanding of the framework they need to perceive, understand, and experience their tasks and processes in the context of their products. The text is ideal for those seeking a proven, innovative strategy for meeting goals through intuitive and effective thinking.

Data Mining and Predictive Analysis

2nd Edition
December 30, 2014
Colleen McCue
English
Paperback
9 7 8 0 1 2 8 0 0 2 2 9 2
eBook
9 7 8 0 1 2 8 0 0 4 0 8 1

Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security analysis, and operational law enforcement settings.This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics in intelligence and security analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment.

Reliability Assurance of Big Data in the Cloud

1st Edition
December 9, 2014
Yun Yang + 2 more
English
Paperback
9 7 8 0 1 2 8 0 2 5 7 2 7
eBook
9 7 8 0 1 2 8 0 2 6 6 8 7

With the rapid growth of Cloud computing, the size of Cloud data is expanding at a dramatic speed. A huge amount of data is generated and processed by Cloud applications, putting a higher demand on cloud storage. While data reliability should already be a requirement, data in the Cloud needs to be stored in a highly cost-effective manner. This book focuses on the trade-off between data storage cost and data reliability assurance for big data in the Cloud. Throughout the whole Cloud data lifecycle, four major features are presented: first, a novel generic data reliability model for describing data reliability in the Cloud; second, a minimum replication calculation approach for meeting a given data reliability requirement to facilitate data creation; third, a novel cost-effective data reliability assurance mechanism for big data maintenance, which could dramatically reduce the storage space needed in the Cloud; fourth, a cost-effective strategy for facilitating data creation and recovery, which could significantly reduce the energy consumption during data transfer.

Building an Intelligence-Led Security Program

1st Edition
December 5, 2014
Allan Liska
English
Paperback
9 7 8 0 1 2 8 0 2 1 4 5 3
eBook
9 7 8 0 1 2 8 0 2 3 7 0 9

As recently as five years ago, securing a network meant putting in a firewall, intrusion detection system, and installing antivirus software on the desktop. Unfortunately, attackers have grown more nimble and effective, meaning that traditional security programs are no longer effective. Today's effective cyber security programs take these best practices and overlay them with intelligence. Adding cyber threat intelligence can help security teams uncover events not detected by traditional security platforms and correlate seemingly disparate events across the network. Properly-implemented intelligence also makes the life of the security practitioner easier by helping him more effectively prioritize and respond to security incidents. The problem with current efforts is that many security practitioners don't know how to properly implement an intelligence-led program, or are afraid that it is out of their budget. Building an Intelligence-Led Security Program is the first book to show how to implement an intelligence-led program in your enterprise on any budget. It will show you how to implement a security information a security information and event management system, collect and analyze logs, and how to practice real cyber threat intelligence. You'll learn how to understand your network in-depth so that you can protect it in the best possible way.

How to Define and Build an Effective Cyber Threat Intelligence Capability

1st Edition
December 5, 2014
Henry Dalziel
Eric Olson + 1 more
English
Paperback
9 7 8 0 1 2 8 0 2 7 3 0 1
eBook
9 7 8 0 1 2 8 0 2 7 5 2 3

Intelligence-Led Security: How to Understand, Justify and Implement a New Approach to Security is a concise review of the concept of Intelligence-Led Security. Protecting a business, including its information and intellectual property, physical infrastructure, employees, and reputation, has become increasingly difficult. Online threats come from all sides: internal leaks and external adversaries; domestic hacktivists and overseas cybercrime syndicates; targeted threats and mass attacks. And these threats run the gamut from targeted to indiscriminate to entirely accidental. Among thought leaders and advanced organizations, the consensus is now clear. Defensive security measures: antivirus software, firewalls, and other technical controls and post-attack mitigation strategies are no longer sufficient. To adequately protect company assets and ensure business continuity, organizations must be more proactive. Increasingly, this proactive stance is being summarized by the phrase Intelligence-Led Security: the use of data to gain insight into what can happen, who is likely to be involved, how they are likely to attack and, if possible, to predict when attacks are likely to come. In this book, the authors review the current threat-scape and why it requires this new approach, offer a clarifying definition of what Cyber Threat Intelligence is, describe how to communicate its value to business, and lay out concrete steps toward implementing Intelligence-Led Security.

Predictive Analytics and Data Mining

1st Edition
November 27, 2014
Vijay Kotu + 1 more
English
Paperback
9 7 8 0 1 2 8 0 1 4 6 0 8
eBook
9 7 8 0 1 2 8 0 1 6 5 0 3

Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveA...

Data Architecture: A Primer for the Data Scientist

1st Edition
November 26, 2014
W.H. Inmon + 1 more
English
Paperback
9 7 8 0 1 2 8 0 2 0 4 4 9
eBook
9 7 8 0 1 2 8 0 2 0 9 1 3

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data

RDF Database Systems

1st Edition
November 24, 2014
Olivier Curé + 1 more
English
Paperback
9 7 8 0 1 2 7 9 9 9 5 7 9
eBook
9 7 8 0 1 2 8 0 0 4 7 0 8

RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database. This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology. With an approach that combines technical detail with theoretical background, this book shows how to design and develop semantic web applications, data models, indexing and query processing solutions.

Enterprise Business Intelligence and Data Warehousing

1st Edition
November 24, 2014
Alan Simon
English
Paperback
9 7 8 0 1 2 8 0 1 5 4 0 7
eBook
9 7 8 0 1 2 8 0 1 7 4 6 3

Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the Enterprise BI/DW Program Manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the Enterprise Program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing. Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing.

Social Data Analytics

1st Edition
November 10, 2014
Krish Krishnan + 1 more
English
Paperback
9 7 8 0 1 2 3 9 7 1 8 6 9
eBook
9 7 8 0 1 2 3 9 7 7 8 0 9

Social Data Analytics is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease. You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization’s next social data analytics project.

Related subjects

Database management

Information interfaces and presentation

Information storage and retrieval

Information systems applications

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