Wow! eBook: Managing Data in Motion - 5 new eBooks |  |
- Managing Data in Motion
- Analyzing the Social Web
- Computation and Storage in the Cloud
- Brainstorming and Beyond
- Measuring Data Quality for Ongoing Improvement
| Posted: 27 May 2013 07:15 AM PDT
Book DescriptionManaging Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise’s computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the “data in motion” in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and “big data” applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects.
Table of Contents Part 2: Batch Data Integration Part 3: Real Time Data Integration Part 4: Big, Cloud, Virtual Data Book Details
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| Posted: 27 May 2013 07:11 AM PDT
Book DescriptionAnalyzing the Social Web provides a framework for the analysis of public data currently available and being generated by social networks and social media, like Facebook, Twitter, and Foursquare. Access and analysis of this public data about people and their connections to one another allows for new applications of traditional social network analysis techniques that let us identify things like who are the most important or influential people in a network, how things will spread through the network, and the nature of peoples’ relationships. Analyzing the Social Web introduces you to these techniques, shows you their application to many different types of social media, and discusses how social media can be used as a tool for interacting with the online public.
Table of Contents Book Details
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| Computation and Storage in the Cloud Posted: 27 May 2013 07:07 AM PDT
Book DescriptionComputation and Storage in the Cloud is the first comprehensive and systematic work investigating the issue of computation and storage trade-off in the cloud in order to reduce the overall application cost. Scientific applications are usually computation and data intensive, where complex computation tasks take a long time for execution and the generated datasets are often terabytes or petabytes in size. Storing valuable generated application datasets can save their regeneration cost when they are reused, not to mention the waiting time caused by regeneration. However, the large size of the scientific datasets is a big challenge for their storage. By proposing innovative concepts, theorems and algorithms, this book will help bring the cost down dramatically for both cloud users and service providers to run computation and data intensive scientific applications in the cloud.
Table of Contents Appendix A. Notation Index Book Details
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| Posted: 27 May 2013 07:00 AM PDT
Book DescriptionBrainstorming and Beyond describes the techniques for generating ideas verbally, in writing, or through sketches. The first chapter focuses on brainstorming, the foundation method for ideation, which is a complex social process building off of social psychology principles, motivational constructs, and corporate culture. Brainstorming is commonly portrayed as an easy way to generate ideas, but in reality, it is a complex social process that is often flawed in ways that are not self-evident. Chapter 2 discusses Brainwriting, which is a variation on brainstorming in which each person writes ideas down on paper and then passes the paper to a new person who reads the first set of ideas and adds new ones. Since there is no group shouting out of ideas, strong facilitation skills are not required, and more often than not, Brainwriting results greatly exceed those of group brainstorming in a shorter time because ideas are generated in a parallel, rather than serial, fashion. Brainwriting is useful when time is limited, groups are hostile, or you are dealing with a culture where shouting out wild or divergent ideas might be difficult. Finally, in Chapter 3, readers learn about Braindrawing, a method of visual brainstorming that helps practitioners generate ideas for icons, other graphics, user interface layouts, or Web page designs. Each of these methods provides readers with ways to generate, present, and evaluate ideas so they can begin building a strong foundation for product success.
Table of Contents Book Details
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| Measuring Data Quality for Ongoing Improvement Posted: 27 May 2013 06:57 AM PDT
Book DescriptionThe Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies.
Table of Contents Section Two: DQAF Concepts and Measurement Types Section Three: Data Assessment Scenarios Section Four: Applying the DQAF to Data Requirements Section Five: A Strategic Approach to Data Quality Section Six: The DQAF in Depth Appendix A. Measuring the Value of Data Book Details
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