IEEE Geoscience and Remote Sensing Magazine - September 2017 - 66

JIANCHENG SHI
◗ "Microwave Remote Sensing of Land Surface Properties:
From Modeling to Observations"
VALERY ZAVOROTNY
◗ "Remote Sensing Using GNSS Bistatic Radar of Opportunity"
◗ "Measurements of Soil Moisture, Snow, and Vegetation
with GPS Interferometric Reflectometry"
NEW LECTURER BIOGRAPHIES AND ABSTRACTS
PETER BAUMANN
A faculty member at Jacobs University in Bremen, Germany,
Peter Baumann researches scalable multidimensional array
databases (data cubes) and their application in science and
engineering. He is a professor of computer science, an inventor, and an entrepreneur. With his work on algebra, query
languages, and efficient architectures culminating in the
rasdaman array database management system, Prof. Baumann has pioneered the research field of array databases.
He has published more than 130 book chapters and journal
and conference papers; in addition, he holds international
patents on array database technology and has received numerous international innovation awards for his work. The
rasdaman technology is in operational use on petabytescale spatiotemporal databases.
Prof. Baumann is an active and often leading contributor
to standardization in both the Open Geospatial Consortium
(OGC) and International Organization for Standardization
(ISO) bodies, having served as editor of a series of adopted
international standards. For the OGC, he chairs the Working
Group on Big Geo Data. In particular, with both the OGC
and ISO, he is the editor of the big data cube standards coverage implementation schema (CIS) and web coverage service
(WCS), as well as ISO Structured Query Language/multidimensional arrays. As part of the Research Data Alliance, he
cofounded and cochairs the Big Data and Geospatial Interest
Groups and the Array Database Assessment Working Group.
In 2014, the OGC honored his contributions to big data standardization with the prestigious Kenneth Gardels Award.
"DATA CUBES AS A NOVEL PARADIGM FOR
MASSIVE EARTH DATA ANALYSIS: CONCEPTS
AND IMPLEMENTATION"
In the past, researchers focused on data as they are delivered from satellites or other sensors. By providing access
to hyperspectral scenes, however, services were tied to the
instrument characteristics rather than offering users a comprehensive, unified data view. Data cubes change this radically: as seamless maps caused a revolution in map access
for nonexperts, images in data cubes are combined along
time, height, or both. The result is a single homogeneous
data object-the data cube that represents analytics-ready
data (ARD) along space and time. On the server side, data
cube operations can be parallelized and distributed across
66

clusters and clouds and even form cross-data-center federations, allowing on-demand extraction, analysis, aggregation, and fusion on data cubes.
This lecture introduces the data cube concept as a service
paradigm and discusses the data cube standards available
from the OGC, ISO, and Infrastructure for Spatial Information in Europe (INSPIRE). Real-life examples, demonstrated live, show how to operate on petascale data cubes
while exploiting cloud parallelization and heterogeneous
hardware. Participants can reproduce and vary most of the
examples on their Internet-connected laptops.
"BIG DATA STANDARDS IN OGC, ISO, AND INSPIRE:
OVERVIEW, CONCEPTS, AND USE"
In OGC standards terminology, coverages (i.e., digital representations of space-time varying phenomena) represent
the data category contributing most to big data. In terms
of practical application, coverages resemble spatiotemporal sensors, images, image time series, simulations, and
statistics data. The OGC offers both an abstract definition
and a concrete definition, which establishes interoperability down to the level of single pixels, manifested through
free and open conformance tests. The ISO already has the
mirror standard 19123 and is currently adopting OGC CIS
and the corresponding service model, WCS. The European
Spatial Data Infrastructure initiative, INSPIRE, likewise
has adopted coverages and WCS for spatiotemporal data.
In particular, the OGC web coverage processing service, as
part of WCS, defines a spatiotemporal data cube query language suitable for operating on analysis-ready data. Hence,
a fruitful convergence of standards can be observed that
benefits implementers, service providers, and users alike.
This seminar introduces the coverage data and service
model, starting from simple extraction requests and moving up to on-demand analytics. It places the WCS suite in
context with related standards and illustrates the state of
standardization as well as future trends. The seminar relies
on real-life multidimensional data sets, which allow participants to run most of the examples on their Internet-connected laptops and modify them for exploration.
JOSÉ MANUEL BIOUCAS DIAS
José Manuel Bioucas Dias received his E.E., M.Sc., Ph.D., and
habilitation degrees in electrical and computer engineering
from the Instituto Superior Técnico (IST), Universidade Técnica de Lisboa (now Universidade de Lisboa), Portugal, in
1985, 1991, 1995, and 2007, respectively. Since 1995, he has
been with the Department of Electrical and Computer Engineering at IST, where he is an associate professor and teaches
inverse problems in imaging and electric communications.
He is also a senior researcher with the Pattern and Image
Analysis group of the Instituto de Telecomunicações, a private nonprofit research institution.
Prof. Boucas Dias's research interests include inverse
problems, signal and image processing, pattern recognition,
optimization, and remote sensing. He has authored or
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

SEPTEMBER 2017

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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2017