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A Database Language More Suitable
for the Earth System Sciences
Dimitar Misev and Peter Baumann
Abstract Multidimensional array data, including satellite images and weather
simulations in the Earth Science, confocal microscopy and CAT scans in the Life
Science, as well as telescope and cosmological observations in Space science, is
traditionally the type of data seriously contributing to
. Traditionally, the
database community has neglected this, with the effect that ad hoc implementations
prevail. With the advent of NewSQL in recent years, however, the database scope
has broadened, and array modelling and query support is seriously considered.
Hence, we address integration of array queries into SQL by proposing a generic
model, ASQL, for modelling and querying multi-dimensional arrays in ISO SQL.
The model integrates concepts from the three major array models seen today:
rasdaman, SciQL, and SciDB. ASQL has been implemented and is currently being
discussed in ISO for extending standard SQL.
“
Big Data
”
1 Introduction
“
is an extremely popular buzzword in recent years, and for a good
reason. Hard, seemingly unsolvable problems are being tackled by employing Big
Data tools and techniques on vast amounts of data: natural disaster prediction and
management, optimizing the work of various systems
Big Data
”
from household appliances,
to vehicles, to whole cities and countries, monitoring the real-time spread of dis-
eases and public health, airfare prediction and comparison, etc. Essentially, Big
Data strives to turn data into insight, by exploiting the ever-increasing observation
resolution and variety.
Data voluminosity and variety is perhaps nowhere more prominent than it is in
—
the
field of Earth System Science. Multidimensional array data produced by a wide
variety of sensors (Waldrop and Lippel
2008
), accompanied by
“
meta data
”
,is
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