Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book * Perform big data processing with Spark-without having to learn Scala! * Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics * Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn * Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. * Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library * Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library * Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems * Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.

Make sense of your data and predict the unpredictable About This Book * A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics * Apply the principles and techniques of predictive analytics to effectively interpret big data * Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains Who This Book Is For This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. Basic familiarity with a programming language of R is expected. What You Will Learn * Master the core predictive analytics algorithm which are used today in business * Learn to implement the six steps for a successful analytics project * Classify the right algorithm for your requirements * Use and apply predictive analytics to research problems in healthcare * Implement predictive analytics to retain and acquire your customers * Use text mining to understand unstructured data * Develop models on your own PC or in Spark/Hadoop environments * Implement predictive analytics products for customers In Detail This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data. Style and Approach This book takes a practical hands-on approach wherein the algorithms will be explained with the help of real-world use cases. It is written in a well-researched academic style which is a great mix of theoretical and practical information. Code examples are supplied for both theoretical concepts as well as for the case studies. Key references and summaries will be provided at the end of each chapter so that you can explore those topics on their own.

Gain insight into how to govern and consume IBM's unique in-motion and at-rest Big Data analytic capabilitiesA. R. Ammons once said, "A word too much repeated falls out of being", and although the term Big Data sometimes seems to be "too much repeated", it's not about to fall "out of being". That said, it is subject to a lot of hype. The term Big Data is a bit of a misnomer. Truth be told, we're not even big fans of the term--despite the fact that it is so prominently displayed on the cover of this book--because it implies that other data is somehow small (it might be) or that this particular type of data is large in size (it can be, but doesn't have to be). This is Big Data in a nutshell: It is the ability to retain, process, and understand data like never before. It can mean more data than what you are using today; but it can also mean different kinds of data, a venture into the unstructured world where most of today's data resides. The Big Data opportunity. It's a shift, rift, lift, or cliff for your business--this book is going to help you experience the shift and lift, while those that don't work to get beyond the hype end up in a rift or cliff. In this book you will learn how cognitive computing systems, like IBM Watson, fit into the Big Data world. You'll learn how Big Data needs a "ground-to-cloud" architecture, what a Data Refinery looks like, and theimportance of a next generation data platform. Gain an understanding of the concepts of data-in-motion, data-at-rest (technologies like Hadoop play here, as well as others), the role that NoSQL and polyglot play in a leading edge analytics architecture, and more. Get details about the Big Data platform manifesto and why it is a must for any Big Data project. Capturing, storing, refining, transforming, governing, securing, and analyzing data, traditionally or as a service, are important topics alsocovered in this book.

From data to actionable business insights using Amazon QuickSight! About This Book * A practical hands-on guide to improving your business with the power of BI and Quicksight * Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services * Packed with real-world examples with Solution Architectures needed for a cloud-powered Business Intelligence service Who This Book Is For This book is for Business Intelligence architects, BI developers, Big Data architects, and IT executives who are looking to modernize their business intelligence architecture and deliver a fast, easy-to-use, cloud powered business intelligence service. What You Will Learn * Steps to test drive QuickSight and see how it fits in AWS big data eco system * Load data from various sources such as S3, RDS, Redshift, Athena, and SalesForce and visualize using QuickSight * Understand how to prepare data using QuickSight without the need of an IT developer * Build interactive charts, reports, dashboards, and storyboards using QuickSight * Access QuickSight using the mobile application * Architect and design for AWS Data Lake Solution, leveraging AWS hosted services * Build a big data project with step-by-step instructions for data collection, cataloguing, and analysis * Secure your data used for QuickSight from S3, RedShift, and RDS instances * Manage users, access controls, and SPICE capacity In Detail Amazon QuickSight is the next-generation Business Intelligence (BI) cloud service that can help you build interactive visualizations on top of various data sources hosted on Amazon Cloud Infrastructure. QuickSight delivers responsive insights into big data and enables organizations to quickly democratize data visualizations and scale to hundreds of users at a fraction of the cost when compared to traditional BI tools. This book begins with an introduction to Amazon QuickSight, feature differentiators from traditional BI tools, and how it fits in the overall AWS big data ecosystem. With practical examples, you will find tips and techniques to load your data to AWS, prepare it, and finally visualize it using QuickSight. You will learn how to build interactive charts, reports, dashboards, and stories using QuickSight and share with others using just your browser and mobile app. The book also provides a blueprint to build a real-life big data project on top of AWS Data Lake Solution and demonstrates how to build a modern data lake on the cloud with governance, data catalog, and analysis. It reviews the current product shortcomings, features in the roadmap, and how to provide feedback to AWS. Grow your profits, improve your products, and beat your competitors. Style and approach This book takes a fast-paced, example-driven approach to demonstrate the power of QuickSight to improve your business' efficiency. Every chapter is accompanied with a use case that shows the practical implementation of the step being explained.

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book * Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 * Develop and deploy efficient, scalable real-time Spark solutions * Take your understanding of using Spark with Python to the next level with this jump start guide Who This Book Is For If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory. What You Will Learn * Learn about Apache Spark and the Spark 2.0 architecture * Build and interact with Spark DataFrames using Spark SQL * Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively * Read, transform, and understand data and use it to train machine learning models * Build machine learning models with MLlib and ML * Learn how to submit your applications programmatically using spark-submit * Deploy locally built applications to a cluster In Detail Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications. Style and approach This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

Exploit the visualization capabilities of Kibana and build powerful interactive dashboards About This Book * Introduction to data-driven architecture and the Elastic stack * Build effective dashboards for data visualization and explore datasets with Elastic Graph * A comprehensive guide to learning scalable data visualization techniques in Kibana Who This Book Is For If you are a developer, data visualization engineer, or data scientist who wants to get the best of data visualization at scale then this book is perfect for you. A basic understanding of Elasticsearch and Logstash is required to make the best use of this book. What You Will Learn * How to create visualizations in Kibana * Ingest log data, structure an Elasticsearch cluster, and create visualization assets in Kibana * Embed Kibana visualization on web pages * Scaffold, develop, and deploy new Kibana & Timelion customizations * Build a metrics dashboard in Timelion based on time series data * Use the Graph plugin visualization feature and leverage a graph query * Create, implement, package, and deploy a new custom plugin * Use Prelert to solve anomaly detection challenges In Detail Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time. In this book, you'll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it's logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin. By the end of this book you will have all the information needed to take your Elastic stack skills to a new level of data visualization. Style and approach This book takes a comprehensive, step-by-step approach to working with the visualization aspects of the Elastic stack. Every concept is presented in a very easy-to-follow manner that shows you both the logic and method of implementation. Real world cases are referenced to highlight how each of the key concepts can be put to practical use.

A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this text we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems. The aims of bioinformatics are threefold. First, at its simplest bioinformatics organises data in a way that allows researchers to access existing information and to submit new entries as they are produced, e.g. the Protein Data Bank for 3D macromolecular structures . While data-curation is an essential task, the information stored in these databases is essentially useless until analysed. Thus the purpose of bioinformatics extends much further. The second aim is to develop tools and resources that aid in the analysis of data. For example, having sequenced a particular protein, it is of interest to compare it with previously characterised sequences. This needs more than just a simple text-based search and programs such as FASTA and PSI-BLAST must consider what comprises a biologically significant match. Development of such resources dictates expertise in computational theory as well as a thorough understanding of biology. The third aim is to use these tools to analyse the data and interpret the results in a biologically meaningful manner. Traditionally, biological studies examined individual systems in detail, and frequently compared them with a few that are related. In bioinformatics, we can now conduct global analyses of all the available data with the aim of uncovering common principles that apply across many systems and highlight novel feature.

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book * Use Apache Spark for data processing with these hands-on recipes * Implement end-to-end, large-scale data analysis better than ever before * Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn * Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. * Solve real-world analytical problems with large data sets. * Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. * Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. * Learn about numerical and scientific computing using NumPy and SciPy on Spark. * Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book * Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data * Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images * A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn * Acquire, format, and visualize your data * Build an image-similarity search engine * Generate meaningful visualizations anyone can understand * Get started with analyzing social network graphs * Find out how to implement sentiment text analysis * Install data analysis tools such as Pandas, MongoDB, and Apache Spark * Get to grips with Apache Spark * Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Description

Modern vehicles have electronic control units (ECUs) to control various subsystems such as the engine, brakes, steering, air conditioning, and infotainment. These ECUs (or simply 'controllers') are networked together to share information, and output directly measured and calculated data to each other. This in-vehicle network is a data goldmine for improved maintenance, measuring vehicle performance and its subsystems, fleet management, warranty and legal issues, reliability, durability, and accident reconstruction. The focus of Data Acquisition from HD Vehicles Using J1939 CAN Bus is to guide the reader on how to acquire and correctly interpret data from the in-vehicle network of heavy-duty (HD) vehicles. The reader will learn how to convert messages to scaled engineering parameters, and how to determine the available parameters on HD vehicles, along with their accuracy and update rate. Written by two specialists in this field, Richard (Rick) P. Walter and Eric P. Walter, principals at HEM Data, located in the United States, the book provides a unique road map for the data acquisition user. The authors give a clear and concise description of the CAN protocol plus a review of all 19 parts of the SAE International J1939 standard family. Pertinent standards are illuminated with tables, graphs and examples. Practical applications covered are calculating fuel economy, duty cycle analysis, and capturing intermittent faults. A comparison is made of various diagnostic approaches including OBD-II, HD-OBD and World Wide Harmonized (WWH) OBD. Data Acquisition from HD Vehicles Using J1939 CAN Bus is a must-have reference for those interested to acquire data effectively from the SAE J1939 equipped vehicles.

Become an expert at using Python for advanced statistical analysis of data using real-world examples About This Book * Clean, format, and explore data using graphical and numerical summaries * Leverage the IPython environment to efficiently analyze data with Python * Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed. What You Will Learn * Read, sort, and map various data into Python and Pandas * Recognise patterns so you can understand and explore data * Use statistical models to discover patterns in data * Review classical statistical inference using Python, Pandas, and SciPy * Detect similarities and differences in data with clustering * Clean your data to make it useful * Work in Jupyter Notebook to produce publication ready figures to be included in reports In Detail Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning. Style and approach This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.

Master the art of getting the maximum out of your machine data using Splunk About This Book * A practical and comprehensive guide to the advanced functions of Splunk,, including the new features of Splunk 6.3 * Develop and manage your own Splunk apps for greater insight from your machine data * Full coverage of high-level Splunk techniques including advanced searches, manipulations, and visualization Who This Book Is For This book is for Splunk developers looking to learn advanced strategies to deal with big data from an enterprise architectural perspective. It is expected that readers have a basic understanding and knowledge of using Splunk Enterprise. What You Will Learn * Find out how to develop and manage apps in Splunk * Work with important search commands to perform data analytics on uploaded data * Create visualizations in Splunk * Explore tweaking Splunk * Integrate Splunk with any pre-existing application to perform data crunching efficiently and in real time * Make your big data speak with analytics and visualizations using Splunk * Use SDK and Enterprise integration with tools such as R and Tableau In Detail Master the power of Splunk and learn the advanced strategies to get the most out of your machine data with this practical advanced guide. Make sense of the hidden data of your organization - the insight of your servers, devices, logs, traffic and clouds. Advanced Splunk shows you how. Dive deep into Splunk to find the most efficient solution to your data problems. Create the robust Splunk solutions you need to make informed decisions in big data machine analytics. From visualizations to enterprise integration, this well-organized high level guide has everything you need for Splunk mastery. Start with a complete overview of all the new features and advantages of the latest version of Splunk and the Splunk Environment. Go hands on with uploading data, search commands for basic and advanced analytics, advanced visualization techniques, and dashboard customizing. Discover how to tweak Splunk to your needs, and get a complete on Enterprise Integration of Splunk with various analytics and visualization tools. Finally, discover how to set up and use all the new features of the latest version of Splunk. Style and approach This book follows a step by step approach. Every new concept is built on top of its previous chapter, and it is full of examples and practical scenarios to help the reader experiment as they read.

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenarios About This Book * Perform real-time data analytics and visualizations, on streaming data, using Kibana * Build beautiful visualizations and dashboards with simplicity and ease without any type of coding involved * Learn all the core concepts as well as detailed information about each component used in Kibana Who This Book Is For Whether you are new to the world of data analytics and data visualization or an expert, this book will provide you with the skills required to use Kibana with ease and simplicity for real-time data visualization of streaming data. This book is intended for those professionals who are interested in learning about Kibana,its installations, and how to use it . As Kibana provides a user-friendly web page, no prior experience is required. What You Will Learn * Understand the basic concepts of elasticsearch used in Kibana along with step by step guide to install Kibana in Windows and Ubuntu * Explore the functionality of all the components used in Kibana in detail, such as the Discover, Visualize, Dashboard,and Settings pages * Analyze data using the powerful search capabilities of elasticsearch * Understand the different types of aggregations used in Kibana for visualization * Create and build different types of amazing visualizations and dashboards easily * Create, save, share, embed, and customize the visualizations added to the dashboard * Customize and tweak the advanced settings of Kibana to ensure ease of use In Detail With the increasing interest in data analytics and visualization of large data around the globe, Kibana offers the best features to analyze data and create attractive visualizations and dashboards through simple-to-use web pages. The variety of visualizations provided, combined with the powerful underlying elasticsearch capabilities will help professionals improve their skills with this technology. This book will help you quickly familiarize yourself to Kibana and will also help you to understand the core concepts of this technology to build visualizations easily. Starting with setting up of Kibana and elasticsearch in Windows and Ubuntu, you will then use the Discover page to analyse your data intelligently. Next, you will learn to use the Visualization page to create beautiful visualizations without the need for any coding. Then, you will learn how to use the Dashboard page to create a dashboard and instantly share and embed the dashboards. You will see how to tweak the basic and advanced settings provided in Kibana to manage searches, visualizations, and dashboards. Finally, you will use Kibana to build visualizations and dashboards for real-world scenarios. You will quickly master the functionalities and components used in Kibana to create amazing visualizations based on real-world scenarios. With ample screenshots to guide you through every step, this book will assist you in creating beautiful visualizations with ease. Style and approach This book is a comprehensive step-by-step guide to help you understand Kibana. It's explained in an easy-to-follow style along with supporting images. Every chapter is explained sequentially , covering the basics of each component of Kibana and providing detailed explanations of all the functionalities of Kibana that appeal.

Proprietare und monolithische Softwaresysteme haben die Wirtschaft in den letzten Jahrzehnten gepragt. Ohne die digitale Unterstutzung der Geschaftsprozesse ist kein Unternehmen wettbewerbsfahig. Die Revolution wird durch den Consumer Bereich vorangetrieben, die Geschaftsprozesse werden mobil und ubiquitar. Konsumenten im B2C und Unternehmen im B2B werden zukunftig permanent in Kontakt treten konnen, die one-to-one Kommunikation wird durch einen many-to-many Informationsaustausch ersetzt werden. "