The Yotta is not Enough! / Bruno Jacobfeuerborn

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II Konferencja Naukowa : Nauka o informacji (informacja naukowa) w okresie zmian, Warszawa, 15-16.04.2013 r. Instytut Informacji Naukowej i Studiów Bibliologicznych, Uniwersytet Warszawski

The 2nd Scientific Conference : Information Science in an Age of Change, April 15-16, 2013. Institute of Information and Book Studies, University of Warsaw

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The Yotta is not Enough! / Bruno Jacobfeuerborn

  1. 1. 1The Yotta is not Enough!The Need for Rethinking Information Science.Dr Bruno JacobfeuerbornTelekom Deutschland GmbH“Information Science in an Age of Change”, 2nd ConferenceInstitute for Information Science and Book Studies, University of WarsawWarsaw, April 15-16th, 2013
  2. 2. Yotta (Y)=1024 or 1 000 000 000 000 000 000 000 0002© B. Jacobfeuerborn
  3. 3. Metric Prefixes (ISO)3© B. Jacobfeuerborn
  4. 4. Dr. Bruno Jacobfeuerborn- Moved to Deutsche Telekom in 1989.- Head of Radio and Transmission Department in Hanover , 1991.- Regional Director in Leipzig, 1991.- Regional Director Technology and later Regional Director Business, responsible for Sales,Marketing and Technology, Hanover, 1995.- T-Mobile; the acquisition of the GSM license in Poland, 1996.- Technical Director T-Mobile Netherlands and Member of the Management Board, 2002.- Head of Service Management Europe in the T-Mobile International, 2004.- Technical Director PTC and Member of the Management Board, 2007.- Director and Management Board Member responsible for technology (fixed and mobile)in Germany at Telekom Deutschland GmbH, 2009.- Invited speaker to international conferences and coach of workshops.- MOST Foundation General Assembly member.4
  5. 5. 5Contents─ Prologue─ Thesis─ Big Data─ 4 Paradigms of Science─ Data Science─ EpilogueNew ScientificParadigmBig Data© B. Jacobfeuerborn
  6. 6. 6Prologue.© B. Jacobfeuerborn
  7. 7. 7Data is the raw material of the XXI century.Credo© B. Jacobfeuerborn
  8. 8. 8Thesis.© B. Jacobfeuerborn
  9. 9. 9A new scientific paradigm emerges.Information science has to face and cope with it!ThesisSource: Cartoonbank.com© B. Jacobfeuerborn
  10. 10. 10Big Data.© B. Jacobfeuerborn
  11. 11. 11© B. Jacobfeuerborn
  12. 12. 12“Big data refers to datasets whose size is beyond theability of typical database software tools to capture,store, manage, and analyze”.--- McKinsey, 2011Big Data© B. Jacobfeuerborn
  13. 13. How Big is Big?Today: between Exabytes (1018) and Zettabytes (1021)Tomorrow: over Zettabytes13© B. Jacobfeuerborn
  14. 14. Big Data – the FloodWalmart drags a million hourly retail transactions into a database thatlong ago passed 2.5 petabytes; Facebook processes 2.5 billion pieces ofcontent and 500 terabytes of data each day; and Google, whose YouTubedivision alone gains 72 hours of new video every minute, accumulates 24petabytes of data in a single day.− David Rowan, Editor, WIRED UK,http://www.edge.org/response-detail/23859“Each day, according to IBM,we collectively generate 2.5quintillion bytes—a tsunami ofstructured and unstructureddata thats growing, in IDCsreckoning, at 60 per cent ayear.14© B. Jacobfeuerborn
  15. 15. 15Four Paradigms of Science.© B. Jacobfeuerborn
  16. 16. Scientific RevolutionsT.S. Kuhn1922 - 199616© B. Jacobfeuerborn
  17. 17. 17Science has been developingfrom idea-centricity to data-centricity.Data leverage ideas!My Addendum to Kuhn’s ClaimDataIdea© B. Jacobfeuerborn
  18. 18. The School of Athens, Raphael, 150918© B. Jacobfeuerborn
  19. 19. 1. Platonic ApproachIn the Greek language science meansknowledge. According to Aristotle andPlato science/knowledge is:universal, necessary, certain, andtimeless. Deduction is the onlyallowed way of reasoning.Mathematics is a prototype (model) ofscience and a language of nature.19© B. Jacobfeuerborn
  20. 20. 2. Baconian ApproachFrancis Bacon’s new methodology ofscience and knowledge, empiricism,that relayed on observation,collection of data, and experimenting,along with accepting induction as alegal inference method for scientificendeavors can be characterized asdata-centric.20© B. JacobfeuerbornFrancis Bacon, 1561 - 1626
  21. 21. 3. Computers at Work (Simulation, Modelling)−J.P. Rini“The idea is to use a computer programto perform lengthy computations, and toprovide a proof that the result of thesecomputations implies the given theorem.In 1976, the four color theorem was thefirst major theorem to be verified using acomputer program.”http://en.wikipedia.org/wiki/Computer-assisted_proof21© B. Jacobfeuerborn
  22. 22. 22“It is a capital mistake totheorize before one hasdata.”− Sherlock Holmes,A Study in Scarlett(Arthur Conan Doyle)The Role of Data© B. Jacobfeuerborn
  23. 23. 23“We can stop looking for models. Wecan analyze the data withouthypotheses about what it might show.We can throw the numbers into thebiggest computing clusters the worldhas ever seen and let algorithms findpatterns where science cannot.”–Chris Anderson4. Big Data at Work© B. Jacobfeuerborn
  24. 24. 24Data Science.© B. Jacobfeuerborn
  25. 25. 25Data science is a set of scientific theories, methods, tools, and bestpractices (including hacking!) aimed to analyse and explore bigdatasets in order to discover hidden knowledge thru inference.Data Sciencesource: Data Science: An Introduction,http://en.wikibooks.org/wiki/Data_Science:_An_Introduction© B. Jacobfeuerborn!
  26. 26. 26© B. Jacobfeuerborn
  27. 27. 27My VisionInformationScienceDataScience© B. Jacobfeuerborn
  28. 28. 28− Definitions of data, information, and knowledge.− Data structures and databases.− Big data and analytics trends.− Elements of logics and non-standard inference mechanisms for big data.− Assorted methods of knowledge representation.− Elements of machine learning and artificial intelligence.− Methods of browsing and retrieval of big data, with a focus on methods to fast delivery of the retrievedhits.− Methods and tools to create metadata.− Data integration.− Deep data analysis: statistics and data mining technologies.− Architecture of scalable big data systems.− Cloud computing; methods of physical storage of big data; virtualization technologies for sharingprocessing power and memory.− Security and privacy within big data infrastructures.− Big data case studies (e.g. social networking, governance, marketing, health).Data Science Curriculum for Information Science Students© B. Jacobfeuerborn
  29. 29. 29Epilogue.© B. Jacobfeuerborn
  30. 30. 30“With too little data, you won’t beable to make any conclusionsthat you trust. …Big data isn’t about bits, it’sabout talent.”–Douglas Merrillhttp://www.forbes.com/sites/douglasmerrill/2012/05/01/r-is-not-enough-for-big-data/To Remember© B. Jacobfeuerborn
  31. 31. Thank you for listening!

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