Principles of big data : preparing, sharing, and analyzing complex information

__Principles of Big Data__ helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big Data design and analysis. . Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources. The National Research Council Created, In October 2001, The Committee On Responsibilities Of Authorship In The Biological Sciences, Whose Members Were Chosen From Academe And The Commercial Sector For Their Expertise In The Life Sciences And Medicine And Their Experience With Issues Related To Intellectual Property Rights, Scientific Publishing, Data, Software, Technology Transfer, And The Structure Of The Scientific Enterprise. The Committee Was Given The Following Charge: To Conduct A Study, To Evaluate The Responsibilities Of Authors Of Scientific Papers In The Life Sciences, To Share Data And Materials Referenced In Their Publications. The Study Will Examine Requirements Imposed On Authors By Journals, Identify Common Practices In The Community, And Explore Whether A Single Set Of Accepted Standards For Sharing Exists. The Study Will Also Explore Whether More Appropriate Standards Should Be Developed, Including What Principles Should Underlie Them And What Rationale There Might Be For Allowing Exceptions To Them. 1. Study Overview And Background -- 2. The Purpose Of Publication And Responsibilities For Sharing -- 3. Sharing Data And Software -- 4. Sharing Materials Integral To Published Findings -- 5. Different Interpretations Of Existing Standards -- 6. Encouraging Compliance With And Continuing The Development Of Standards. Committee On Responsibilities Of Authorship In The Biological Sciences, Board On Life Sciences, Division On Earth And Life Studies, National Research Council Of The National Academies. This Study Was Supported By Contract No. N01-od-4-2139, Task Order #88 Between The National Academy Of Sciences And The Department Of Health And Human Services/the National Institutes Of Health; Grant No. Dbi-0127703 Between The National Academy Of Sciences And The National Science Foundation; Agreement No. B2001-47 Between The National Academy Of Sciences And The Sloan Foundation; And The National Research Council Fund--t.p. Verso Includes Bibliographical References (p. 79). Also Available In Electronic Form As Viewed 5/14/2003. Learn how to responsibly design, build, and maintain complex Big Data resources with the simple, but powerful methods detailed in this book to share, integrate, collect and analyze Big Data. Principles of Big Data will help you avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, expert author Jules Berman shows you how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. Methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. Principles of Big Data demonstrates how you can find relationships among data objects held in disparate Big Data resources and explores the importance of semantics so you come away with a comprehensive view of Big Data. You will learn how your data can be integrated with data from other sources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. Avoid the pitfalls in Big Data design and analysis. Understand how to create and use Big Data safely and responsibly with standards for the acquisition, distribution and-integration of Big Data resources. Book jacket

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

• Learn general methods for specifying Big Data in a way that is understandable to humans and to computers.

• Avoid the pitfalls in Big Data design and analysis.

• Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources.

"Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big Data design and analysis. . Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources"--Provided by publisher Content: 1. Providing structure to unstructured data -- 2. Identification, deidentification, and reidentification -- 3. Ontologies and semantics -- 4. Introspection -- 5. Data integration and software interoperability -- 6. Immutability and immortality -- 7. Measurement -- 8. Simple but powerful big data techniques -- 9. Analysis -- 10. Special considerations in big data analysis -- 11. Stepwise approach to big data analysis -- 12. Failure -- 13. Legalities -- 14. Societal issues -- 15. The future. Abstract: Helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. Read more... Biologists communicate to the research community and document their scientific accomplishments by publishing in scholarly journals. This report explores the responsibilities of authors to share data, software, and materials related to their publications. In addition to describing the principles that support community standards for sharing different kinds of data and materials, the report makes recommendations for ways to facilitate sharing in the future. Jules J. Berman. Includes Bibliographical References And Index.