The Yotta is not Enough! / Bruno Jacobfeuerborn
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Dr. Bruno Jacobfeuerborn emphasizes the necessity to rethink information science in the context of big data, which consists of vast datasets that traditional tools cannot manage. He advocates for a new scientific paradigm that shifts from idea-centric to data-centric approaches in order to harness the potential of big data. The document outlines various aspects of data science, including methodologies for data analysis, integration, and the importance of talent in interpreting data effectively.
The Yotta is not Enough! / Bruno Jacobfeuerborn
- 1. 1 The Yotta is not Enough! The Need for Rethinking Information Science. Dr Bruno Jacobfeuerborn Telekom Deutschland GmbH “Information Science in an Age of Change”, 2nd Conference Institute for Information Science and Book Studies, University of Warsaw Warsaw, April 15-16th, 2013
- 2. Yotta (Y) = 1024 or 1 000 000 000 000 000 000 000 000 2© B. Jacobfeuerborn
- 3. Metric Prefixes (ISO) 3© B. Jacobfeuerborn
- 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 Contents ─ Prologue ─ Thesis ─ Big Data ─ 4 Paradigms of Science ─ Data Science ─ Epilogue New Scientific Paradigm Big Data © B. Jacobfeuerborn
- 7. 7 Data is the raw material of the XXI century. Credo © B. Jacobfeuerborn
- 9. 9 A new scientific paradigm emerges. Information science has to face and cope with it! Thesis Source: Cartoonbank.com © B. Jacobfeuerborn
- 10. 10 Big Data. © B. Jacobfeuerborn
- 12. 12 “Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”. --- McKinsey, 2011 Big Data © B. Jacobfeuerborn
- 13. How Big is Big? Today: between Exabytes (1018) and Zettabytes (1021) Tomorrow: over Zettabytes 13© B. Jacobfeuerborn
- 14. Big Data – the Flood Walmart drags a million hourly retail transactions into a database that long ago passed 2.5 petabytes; Facebook processes 2.5 billion pieces of content and 500 terabytes of data each day; and Google, whose YouTube division alone gains 72 hours of new video every minute, accumulates 24 petabytes 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 2.5 quintillion bytes—a tsunami of structured and unstructured data that's growing, in IDC's reckoning, at 60 per cent a year. 14© B. Jacobfeuerborn
- 15. 15 Four Paradigms of Science. © B. Jacobfeuerborn
- 16. Scientific Revolutions T.S. Kuhn 1922 - 1996 16© B. Jacobfeuerborn
- 17. 17 Science has been developing from idea-centricity to data-centricity. Data leverage ideas! My Addendum to Kuhn’s Claim Data Idea © B. Jacobfeuerborn
- 18. The School of Athens, Raphael, 1509 18© B. Jacobfeuerborn
- 19. 1. Platonic Approach In the Greek language science means knowledge. According to Aristotle and Plato science/knowledge is: universal, necessary, certain, and timeless. Deduction is the only allowed way of reasoning. Mathematics is a prototype (model) of science and a language of nature. 19© B. Jacobfeuerborn
- 20. 2. Baconian Approach Francis Bacon’s new methodology of science and knowledge, empiricism, that relayed on observation, collection of data, and experimenting, along with accepting induction as a legal inference method for scientific endeavors can be characterized as data-centric. 20© B. Jacobfeuerborn Francis Bacon, 1561 - 1626
- 21. 3. Computers at Work (Simulation, Modelling) −J.P. Rini “The idea is to use a computer program to perform lengthy computations, and to provide a proof that the result of these computations implies the given theorem. In 1976, the four color theorem was the first major theorem to be verified using a computer program.” http://en.wikipedia.org/wiki/Computer-assisted_proof 21© B. Jacobfeuerborn
- 22. 22 “It is a capital mistake to theorize before one has data.” − Sherlock Holmes, A Study in Scarlett (Arthur Conan Doyle) The Role of Data © B. Jacobfeuerborn
- 23. 23 “We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let algorithms find patterns where science cannot.” –Chris Anderson 4. Big Data at Work © B. Jacobfeuerborn
- 24. 24 Data Science. © B. Jacobfeuerborn
- 25. 25 Data science is a set of scientific theories, methods, tools, and best practices (including hacking!) aimed to analyse and explore big datasets in order to discover hidden knowledge thru inference. Data Science source: Data Science: An Introduction, http://en.wikibooks.org/wiki/Data_Science:_An_Introduction © B. Jacobfeuerborn !
- 27. 27 My Vision Information Science Data Science © B. Jacobfeuerborn
- 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 retrieved hits. − 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 sharing processing 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
- 30. 30 “With too little data, you won’t be able to make any conclusions that you trust. … Big data isn’t about bits, it’s about talent.” –Douglas Merrill http://www.forbes.com/sites/douglasmerrill/ 2012/05/01/r-is-not-enough-for-big-data/ To Remember © B. Jacobfeuerborn
- 31. Thank you for listening!