3 DO THESE BUZZWORDS SHARE A COMMON CORE? Digital Earth as a multi-thematic and multi-perspective knowledge archive e-science as an infrastructure to share data and models and to enable collaboration Smart Dust as self-organizing, ubiquitous sensor networks Linked Data as a global graph connecting raw data to make them accessible and re-usable Is Big Data a paradigm shift towards data-intensive, collaborative science in which models and data are shared?
4 THE THREE V S OF BIG DATA Volume: The size of the involved data, their multi-dimensional nature, as well as their inter-linkage which creates a global graph. E.g., Volunteered Geographic Information, Location-based Social Networks, sensor networks, high resolution remote sensing data, complex transportation simulations. V Variety: The number of sources and type of data is increasing as well. Combining social media with authoritative sources and integrating different formats such as video, audio, photo, and text allows a more holistic analysis but raises new issues in data integration. V Velocity: Big Data is not only about large amounts of data but also the speed at which data is created and updated. A rapidly increasing number of data sources deliver near real-time data which poses new challenges for stream reasoning and rule systems, which data do we keep? V
5 S OME T HOUGHTS ON B IG D ATA IN GIS CIENCE... K NOWLEDGE AS A N ETWORK The global Linked Data graph may be a promising knowledge infrastructure for Big Data. B IG D ATA GIS CIENCE? K RZYSZTOF J ANOWICZ, UCSB (F EB )
6 NSF EARTH CUBE NSF seeks [...] approaches to create integrated data management infrastructures across the Geosciences. [...] recognize the multifaceted challenges of modern, data-intensive science and education and envision an environment where low adoption thresholds and new capabilities act together to greatly increase the productivity and capability of researchers and educators [...]. (http://www.nsf.gov/geo/earthcube/)
7 IARPA FINDER CHALLENGE Estimate the location of pictures and videos without any explicit geolocation information.
8 IM2GPS Estimating geographic information from a single image Purely data-driven scene matching (low-level features) Big Data Check Volume: 6 million (out of 6 billion) of Flickr photos V Velocity: in theory, new pictures every second V Variety: single type of data V
9 S OME T HOUGHTS ON B IG D ATA IN GIS CIENCE... D IA L OC : N EW D ATA S OURCES F OR O LD Q UESTIONS market food street narrow dense populated asia economy air conditioning smog fog humid warm building construction skyscrapers skyline shipping export channel harbor transportation tram city advertisement Variety: Plain text, not image features as data source B IG D ATA GIS CIENCE? V K RZYSZTOF J ANOWICZ, UCSB (F EB )
10 DIALOC: NEW DATA SOURCES FOR OLD QUESTIONS 0.5 Cape Norman Santa Barbara Volume: > 500,000 Wikipedia articles & travel blog entries. V Velocity: in theory, new travel blog entries every minute V IM2GPS and DiaLoc each exclude 99.9% of the land-surface of the Earth, what if we combine them. 0 City Lake Valley Mountain HistoricPlace Town WorldHeritageSite ProtectedArea Village Cave Island Museum Stream Park Theatre Lighthouse Stadium Hotel Restaurant Airport Hospital
11 FUTURICT A BIG SCIENCE PROJECT EU flagship proposal > 300 teams 10 years 1 billion Euro Knowledge accelerator
14 SO MAYBE THEY ARE JUST ADDRESSING OTHER PROBLEMS?
15 S OME T HOUGHTS ON B IG D ATA IN GIS CIENCE... S O M AYBE T HEY A RE JUST A DDRESSING O THER P ROBLEMS?... which role do we play? B IG D ATA GIS CIENCE? K RZYSZTOF J ANOWICZ, UCSB (F EB )
16 THE PANELISTS Mark Gahegan, University of Auckland, New Zealand Professor at the School of Environment Mark Schildhauer, University of California, Santa Barbara, USA Director of Computing at National Center for Ecological Analysis and Synthesis Isabel Cruz, University of Illinois, USA Professor of Computer Science Werner Kuhn, University of Muenster, Germany Professor for Geographic Information Science Pascal Hitzler, Wright State University, USA Assistant Professor of Computer Science Mike Goodchild, University of California, Santa Barbara, USA Jack and Laura Dangermond Professor of Geography Big Data is a big topic, this panel will focus on the variety component.
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