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  • 1
    Online Resource
    Online Resource
    Big data visualization : learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 1785284169 , 9781785284168
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Big data ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends ...
    Note: Description based on online resource; title from cover (viewed April 17, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785281945/?ar
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  • 2
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    Online Resource
    Statistics for data science : leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788295345 , 178829534X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Statistics ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortab...
    Note: Description based on online resource; title from title page (viewed January 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788290678/?ar
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  • 3
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    Online Resource
    Implementing Splunk 7 : effective operational intelligence to transform machine-generated data into valuable business insight (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788830508 , 1788830504
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition
    Parallel Title: Erscheint auch als
    Keywords: Big data Data processing ; Data mining ; Automatic data collection systems
    Note: Description based on online resource; title from title page (Safari, viewed May 3, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788836289/?ar
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  • 4
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    Implementing Splunk : a comprehensive guide to help you transform big data into valuable business insights with Splunk 6.2 (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784399306 , 1784399302 , 9781784391607
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition
    Series Statement: Professional expertise distilled
    DDC: 005.7565
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Data Mining ; Datenanalyse ; Big Data ; Big Data ; Datenanalyse ; Data Mining
    Note: Description based on online resource; title from cover (Safari, viewed August 13, 2015). - Includes index
    URL: Volltext
    URL: https://learning.oreilly.com/library/view/-/9781784391607/?ar
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  • 5
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    Mastering predictive analytics with R : machine learning techniques for advanced models (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787124356 , 1787124355
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: R (Computer program language) ; Data mining ; Data processing ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do y...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed September 11, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787121393/?ar
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  • 6
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    Online Resource
    The technological singularity : managing the journey (2017)
    Berlin, Heidelberg : Springer Berlin Heidelberg
    Details
    ISBN: 9783662540336
    Language: English
    Pages: 1 Online-Ressource (xii, 261 Seiten)
    Series Statement: The Frontiers Collection
    Parallel Title: Erscheint auch als The technological singularity
    Parallel Title: Print version Callaghan, Victor The Technological Singularity : Managing the Journey
    DDC: 100
    RVK:
    SR 990
    Keywords: Computer science ; Electronic books ; Einzigkeit ; Technischer Fortschritt ; Wissenschaftsethik ; Künstliche Intelligenz ; Risikomanagement
    Abstract: Foreword -- References -- Acknowledgements -- Contents -- 1 Introduction to the Technological Singularity -- 1.1 Why the "Singularity" Is Important -- 1.2 Superintelligence, Superpowers -- 1.3 Danger, Danger! -- 1.4 Uncertainties and Safety -- References -- Risks of, and Responses to, the Journey to the Singularity -- 2 Risks of the Journey to the Singularity -- 2.1 Introduction -- 2.2 Catastrophic AGI Risk -- 2.2.1 Most Tasks Will Be Automated -- 2.2.2 AGIs Might Harm Humans -- 2.2.3 AGIs May Become Powerful Quickly -- 2.2.3.1 Hardware Overhang -- 2.2.3.2 Speed Explosion -- 2.2.3.3 Intelligence Explosion -- References -- 3 Responses to the Journey to the Singularity -- 3.1 Introduction -- 3.2 Post-Superintelligence Responses -- 3.3 Societal Proposals -- 3.3.1 Do Nothing -- 3.3.1.1 AI Is Too Distant to Be Worth Our Attention -- 3.3.1.2 Little Risk, no Action Needed -- 3.3.1.3 Let Them Kill Us -- 3.3.1.4 "Do Nothing" Proposals-Our View -- 3.3.2 Integrate with Society -- 3.3.2.1 Legal and Economic Controls -- 3.3.2.2 Foster Positive Values -- 3.3.2.3 "Integrate with Society" Proposals-Our View -- 3.3.3 Regulate Research -- 3.3.3.1 Review Boards -- 3.3.3.2 Encourage Research into Safe AGI -- 3.3.3.3 Differential Technological Progress -- 3.3.3.4 International Mass Surveillance -- 3.3.3.5 "Regulate Research" Proposals-Our View -- 3.3.4 Enhance Human Capabilities -- 3.3.4.1 Would We Remain Human? -- 3.3.4.2 Would Evolutionary Pressures Change Us? -- 3.3.4.3 Would Uploading Help? -- 3.3.4.4 "Enhance Human Capabilities" Proposals-Our View -- 3.3.5 Relinquish Technology -- 3.3.5.1 Outlaw AGI -- 3.3.5.2 Restrict Hardware -- 3.3.5.3 "Relinquish Technology" Proposals-Our View -- 3.4 External AGI Constraints -- 3.4.1 AGI Confinement -- 3.4.1.1 Safe Questions -- 3.4.1.2 Virtual Worlds -- 3.4.1.3 Resetting the AGI -- 3.4.1.4 Checks and Balances
    Abstract: 3.4.1.5 "AI Confinement" Proposals-Our View -- 3.4.2 AGI Enforcement -- 3.4.2.1 "AGI Enforcement" Proposals-Our View -- 3.5 Internal Constraints -- 3.5.1 Oracle AI -- 3.5.1.1 Oracles Are Likely to Be Released -- 3.5.1.2 Oracles Will Become Authorities -- 3.5.1.3 "Oracle AI" Proposals-Our View -- 3.5.2 Top-Down Safe AGI -- 3.5.2.1 Three Laws -- 3.5.2.2 Categorical Imperative -- 3.5.2.3 Principle of Voluntary Joyous Growth -- 3.5.2.4 Utilitarianism -- 3.5.2.5 Value Learning -- 3.5.2.6 Approval-Directed Agents -- 3.5.2.7 "Top-Down Safe AGI" Proposals-Our View -- 3.5.3 Bottom-up and Hybrid Safe AGI -- 3.5.3.1 Evolutionary Invariants -- 3.5.3.2 Evolved Morality -- 3.5.3.3 Reinforcement Learning -- 3.5.3.4 Human-like AGI -- 3.5.3.5 "Bottom-up and Hybrid Safe AGI" Proposals-Our View -- 3.5.4 AGI Nanny -- 3.5.4.1 "AGI Nanny" Proposals-Our View -- 3.5.5 Motivational Scaffolding -- 3.5.6 Formal Verification -- 3.5.6.1 "Formal Verification" Proposals-Our View -- 3.5.7 Motivational Weaknesses -- 3.5.7.1 High Discount Rates -- 3.5.7.2 Easily Satiable Goals -- 3.5.7.3 Calculated Indifference -- 3.5.7.4 Programmed Restrictions -- 3.5.7.5 Legal Machine Language -- 3.5.7.6 "Motivational Weaknesses" Proposals-Our View -- 3.6 Conclusion -- Acknowledgementss -- References -- Managing the Singularity Journey -- 4 How Change Agencies Can Affect Our Path Towards a Singularity -- 4.1 Introduction -- 4.2 Pre-singularity: The Dynamic Process of Technological Change -- 4.2.1 Paradigm Shifts -- 4.2.2 Technological Change and Innovation Adoption -- 4.2.3 The Change Agency Perspective -- 4.2.3.1 Business Organisations as Agents of Change in Innovation Practice -- 4.2.3.2 Social Networks as Agents of Change -- 4.2.3.3 The Influence of Entrepreneurs as Agents of Change -- 4.2.3.4 Nation States as Agents of Change -- 4.3 Key Drivers of Technology Research and Their Impact
    Abstract: 4.4 The Anti-singularity Postulate -- 4.5 Conclusions -- References -- 5 Agent Foundations for Aligning Machine Intelligence with Human Interests: A Technical Research Agenda -- 5.1 Introduction -- 5.1.1 Why These Problems? -- 5.2 Highly Reliable Agent Designs -- 5.2.1 Realistic World-Models -- 5.2.2 Decision Theory -- 5.2.3 Logical Uncertainty -- 5.2.4 Vingean Reflection -- 5.3 Error-Tolerant Agent Designs -- 5.4 Value Specification -- 5.5 Discussion -- 5.5.1 Toward a Formal Understanding of the Problem -- 5.5.2 Why Start Now? -- References -- 6 Risk Analysis and Risk Management for the Artificial Superintelligence Research and Development Process -- 6.1 Introduction -- 6.2 Key ASI R&D Risk and Decision Issues -- 6.3 Risk Analysis Methods -- 6.3.1 Fault Trees -- 6.3.2 Event Trees -- 6.3.3 Estimating Parameters for Fault Trees and Event Trees -- 6.3.4 Elicitation of Expert Judgment -- 6.3.5 Aggregation of Data Sources -- 6.4 Risk Management Decision Analysis Methods -- 6.5 Evaluating Opportunities for Future Research -- 6.6 Concluding Thoughts -- Acknowledgements -- References -- 7 Diminishing Returns and Recursive Self Improving Artificial Intelligence -- 7.1 Introduction -- 7.2 Self-improvement -- 7.2.1 Evolutionary Algorithms -- 7.2.2 Learning Algorithms -- 7.3 Limits of Recursively Improving Intelligent Algorithms -- 7.3.1 Software Improvements -- 7.3.2 Hardware Improvements -- 7.4 The Takeaway -- References -- 8 Energy, Complexity, and the Singularity -- 8.1 A Contradiction -- 8.2 Challenges -- 8.2.1 Climate Change -- 8.2.2 Biodiversity and Ecosystem Services -- 8.2.3 Energy-or, Where's My Jetsons Car? -- 8.2.4 The Troubles with Science -- 8.3 Energy and Complexity -- 8.4 Exponentials and Feedbacks -- 8.5 Ingenuity, not Data Processing -- 8.6 In Summary -- Acknowledgements -- References
    Abstract: 9 Computer Simulations as a Technological Singularity in the Empirical Sciences -- 9.1 Introduction -- 9.2 The Anthropocentric Predicament -- 9.3 The Reliability of Computer Simulations -- 9.3.1 Verification and Validation Methods -- 9.4 Final Words -- References -- 10 Can the Singularity Be Patented? (And Other IP Conundrums for Converging Technologies) -- 10.1 Introduction -- 10.2 A Singular Promise -- 10.3 Intellectual Property -- 10.3.1 Some General IP Problems in Converging Technologies -- 10.3.2 Some Gaps in IP Relating to the Singularity -- 10.4 Limits to Ownership and Other Monopolies -- 10.5 Owning the Singularity -- 10.6 Ethics, Patents and Artificial Agents -- 10.7 The Open Alternative -- References -- 11 The Emotional Nature of Post-Cognitive Singularities -- 11.1 Technological Singularity: Key Concepts -- 11.1.1 Tools and Methods -- 11.1.2 Singularity: Main Hypotheses -- 11.1.3 Implications of Post-singularity Entities with Advanced, Meta-cognitive Intelligence Ruled by Para-emotions -- 11.2 Post-cognitive Singularity Entities and their Physical Nature -- 11.2.1 Being a Singularity Entity -- 11.2.1.1 Super-intelligent Entities -- 11.2.1.2 Transhumans -- 11.2.2 Post Singularity Entities as Living Systems? -- 11.3 Para-emotional Systems -- 11.4 Conclusions -- Acknowledgements -- References -- 12 A Psychoanalytic Approach to the Singularity: Why We Cannot Do Without Auxiliary Constructions -- 12.1 Introduction -- 12.2 AI and Intelligence -- 12.3 Consciousness -- 12.4 Reason and Emotion -- 12.5 Psychoanalysis -- 12.6 Conclusion -- References -- Reflections on the Journey -- 13 Reflections on the Singularity Journey -- 13.1 Introduction -- 13.2 Eliezer Yudkowsky -- 13.2.1 The Event Horizon -- 13.2.2 Accelerating Change -- 13.2.3 The Intelligence Explosion -- 13.2.4 MIRI and LessWrong -- 13.3 Scott Aaronson -- 13.4 Stuart Armstrong
    Abstract: 13.5 Too Far in the Future -- 13.6 Scott Siskind -- 13.6.1 Wireheading -- 13.6.2 Work on AI Safety Now -- 14 Singularity Blog Insights -- 14.1 Three Major Singularity Schools -- 14.2 AI Timeline Predictions: Are We Getting Better? -- 14.3 No Time Like the Present for AI Safety Work -- 14.4 The Singularity Is Far -- Appendix -- The Coming Technological Singularity: How to Survive in the Post-human Era (reprint) -- References -- References -- Titles in this Series
    DOI: 10.1007/978-3-662-54033-6
    URL: Volltext  (lizenzpflichtig)
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  • 7
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    Online Resource
    Singularity Rising : Surviving and Thriving in a Smarter, Richer, and More Dangerous World (2012)
    New York : BenBella Books, Inc
    Details
    ISBN: 9781936661657
    Language: English
    Pages: Online-Ressource (289 p)
    Parallel Title: Print version Singularity Rising : Surviving and Thriving in a Smarter, Richer, and More Dangerous World
    DDC: 303.49
    Keywords: Artificial intelligence ; Intelligence levels ; Electronic books ; Electronic books
    Abstract: In Ray Kurzweil's New York Times bestseller The Singularity is Near, the futurist and entrepreneur describes the Singularity, a likely future utterly different than anything we can imagine. The Singularity is triggered by the tremendous growth of human and computing intelligence that is an almost inevitable outcome of Moore's Law. Since the book's publication, the coming of the Singularity is now eagerly anticipated by many of the leading thinkers in Silicon Valley, from PayPal mastermind Peter Thiel to Google co-founder Larry Page. The formation of the Singularity Universit
    Description / Table of Contents: Contents; Introduction; PART 1: Rise of the Robots; 1. Exponentially Improving Hardware; 2. Where Might the Software Come From?; 3. Unfriendly Al Terrifies Me; 4. A Friendly Explosion; 5. Military Death Race; 6. Businesses' AI Race; PART 2: We Become Smarter, Even Without AI; 7. What IQ Tells You; 8. Evolution and Past Intelligence Enhancements; 9. Increasing IQ Through Genetic Manipulation; 10. Cognitive-Enhancing Drugs; 11. Brain Training; 12. International Competition in Human-Intelligence Enhancements; PART 3: Economic Implications; 13. Making Us Obsolete?
    Description / Table of Contents: 14. How Cognitive-Enhancing Drugs Might Impact the Economy15. Inequality Falling; 16. Preparing for the Singularity; 17. What Might Derail the Singularity?; 18. Singularity Watch; Acknowledgments; Notes; References; Index
    Note: Description based upon print version of record
    URL: Volltext
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
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  • 8
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    Online Resource
    Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789616279 , 1789616271
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Python (Computer program language) ; Watson (Computer) ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and feature extraction in any machine learning system Book Description IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system Who this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789611854/?ar
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  • 9
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    Online Resource
    IBM Cognos TM1 developer's certification guide : fast track your way to COG-310 certification! (2011)
    Birmingham, UK : Packt Pub.
    Details
    Language: English
    Pages: 1 online resource (vii, 220 p.) , ill.
    Keywords: IBM Cognos TM1 ; Computer software ; Development ; Certification ; Business ; Computer programs ; Management ; Computer programs ; Business planning ; Computer programs ; Electronic books ; Electronic books ; local
    Abstract: Preparing for your COG-310 certification is more engaging and enjoyable with this tutorial because it takes a hands-on approach and teaches through examples. There are also self-test sections for each exam topic. Successfully clear COG-310 certification. Master the major components that make up Cognos TM1 and learn the function of each. Understand the advantages of using Rules versus Turbo Integrator This book provides a perfect study outline and self-test for each exam topic In Detail IBM Cognos TM1 is enterprise planning software that provides a complete, dynamic environment for developing timely, reliable and personalized forecasts and budgets. It is a real time, in memory tool that helps any sized business perform planning, budgeting and forecasting as well as other financial exercises. This book prepares you to master COG-310 certification using an example-driven method that is easy to understand. The IBM Cognos TM1 Developer's Certification guide provides key technical details and background to clear the current IBM Cognos TM1 Developer (test COG-310) certification exam. This certification book covers all the modules of the certification clearly and in depth. The initial chapters cover in detail the components that make up Cognos TM1 and designing and creating dimensions and cubes. The book then dives deep into basic and advanced scripting using TurboIntegrator and then we learn to understand and write basic Rules. We then learn about the drill-through functionality of TM1, virtual and lookup cubes and lastly Time, and presenting and reporting data
    Note: Includes index. - Description based on online resource; title from PDF title page (Safari, viewed Aug. 20, 2012)
    URL: https://learning.oreilly.com/library/view/-/9781849684903/?ar
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