Lecture Administration and visualization: Chapter 1 - Introduction to data management and visualization

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Lecture "Administration and visualization: Chapter 1 - Introduction to data management and visualization" provides students with content about: Big data in 2008; Big data sources; Data is the new oil; Introduction to data management;... Please refer to the detailed content of the lecture!

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Nội dung Text: Lecture Administration and visualization: Chapter 1 - Introduction to data management and visualization

Chapter 1 Introduction to data management and visualization
How big is big data? 3
How big is big data? 4
Data science: The 4th paradigm for scientific discovery 5
Big data in 2008 6
Big data sources • E-commerce • Social networks • Internet of things • Data-intensive experiments (bioinformatics, quantum physics, etc) 7
Data is the new oil 8
Big data 5'V Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them (wikipedia) 9
Big data – big value 10 source: wipro.com
Introduction to data management
What is Data Management • Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner
Poor Data Management • 94% of companies suffering from a catastrophic data loss do not survive – 43% never reopen and 51% close within two years. (University of Texas) • 7 out of 10 small firms that experience a major data loss go out of business within a year. (DTI/Price Waterhouse Coopers) • 50% of all tape backups fail to restore. (Gartner) • 25% of all PC users suffer from data loss each year (Gartner)
Why Data Management: Foundation to Advance Science • Data is a valuable asset – it is expensive and time consuming to collect • Data should be managed to: o maximize the effective use and value of data and information assets o continually improve the quality including data accuracy, integrity, integration, timeliness of data capture and presentation, relevance and usefulness o ensure appropriate use of data and information o facilitate data sharing o ensure sustainability and accessibility in long term for re-use in science
A new image processing technique reveals something not before seen in this Hubble Space Telescope image taken 11 years ago: A faint planet (arrows), the outermost of three discovered with ground-based telescopes last year around the young star HR 8799.D. Lafrenière et al., Astrophysical Journal Letters D. Lafrenière et al., ApJ Letters “Planet hidden in Hubble archives” Science News (Feb. 27, 2009) “The first thing it tells you is how valuable maintaining long-term archives can be. Here is a major discovery that’s been lurking in the data for about 10 years!” comments Matt Mountain, director of the Space Telescope Science Institute in Baltimore, which operates Hubble. “The second thing its tells you is having a well calibrated archive is necessary but not sufficient to make breakthroughs — it also takes a very innovative group of people to develop very smart extraction routines that can get rid of all the artifacts to reveal the planet hidden under all that telescope and detector structure.”
Data Management Facilitates Sharing and Re-use…
Where a majority of data end up now…
Imagine if data were more accessible….
Data Life Cycle Plan Analyze Collect Integrate Assure Discover Describe Preserve
Planning • Consider data management before you collect data • What kind of data will be collected? • Which methods will be used (sensors, samples, etc.)? • What data formats/standards are appropriate? • How will the data be used? • How will you share the data? • Will your methods satisfy • Funding requirements • Policies for access, sharing, reuse • Budget – most of the time tihis is overlooked! • Output • Formal document