National Academies Press: OpenBook
« Previous: 10. SUMMARY
Suggested Citation:"REFERENCES." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.
×
Page 325

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

A STREAM PROCESSOR FOR EXTRACTING USAGE INTELLIGENCE FROM HIGH-MOMENTUM INTERNET DATA 325 to other data-reduction processes and commitment to storage and yield significant cost and latency reductions as well. Key paradigm shifts that we have had to make (and are still making) in our own thinking have been in areas such as dynamic interaction with stream-oriented programming languages, distributed stream processing architectures and visualization of streams. ACKNOWLEDGMENTS I would like to thank some key individuals who have supported me and contributed to this program: Leland Wilkinson, Senior VP, SPSS, who has personally given me strong encouragement to get this material published; Eric Buatois, HP VP, who helped fund early research on these concepts; Jeff Meyer, HP Chief Architect for IUM, who has been the thought leader and creator of many of the key concepts of the IUM platform; Ying He, HP Software developer, who has always been open to changes and yet more changes and has contributed extensively to the DNA server architecture; Eric Peterson, HP Software developer, a great communicator and developer who is primarily responsible for the DNA front-end architecture; Scott Lamons, HP R&D Project Manager, who has been a tremendous asset to the smooth workings of our team and a strong supporter of the program. Cisco IOS® NetFlow is a patented technology of Cisco Systems, Inc. (http://www.cisco.com). sFlow® is a registered mark of InMon Corporation (http:// www.inmon.com). [Received April 2003. Revised October 2003.] REFERENCES Cormen, T.H., Leiserson, C.E., and Rivest, R.L. (1999), Introduction to Algorithms, Cambridge, MA: The MIT Press. Hellerstein, J.M., et al. (1997), “The New Jersey Data Reduction Report,” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 20, 4. Knuth, D.E. (1973), The Art of Computer Programming (vol. 3), Reading, MA: Addison-Wesley. McGarty, T.P. (2002), The Imminent Collapse of the Telecommunications Industry, The Merton Group, http://www.mertongroup.com/ Collapse%20of%20Telecom%2002.pdf. Sidak, J.G. (2003), “The Failure of Good Intentions: The WorldCom Fraud and the Collapse of American Telecommunications After Deregulation,” Yale Journal on Regulation, http://www.aei.org/docLib/ 20030403_SSRN_ID335180_code021001500.pdf. Wilkinson, L. (1999), The Grammar of Graphics, New York: Springer-Verlag.

Next: Pedro Domingos A General Framework for Mining Massive Data Streams »
Statistical Analysis of Massive Data Streams: Proceedings of a Workshop Get This Book
×
 Statistical Analysis of Massive Data Streams: Proceedings of a Workshop
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Massive data streams, large quantities of data that arrive continuously, are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.

READ FREE ONLINE

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!