Database Reference
In-Depth Information
resulted in a) a collection of ontologies for describing Earth science data
and knowledge, and b) an ontology-aided search tool to demonstrate
the use of these ontologies. The set of keywords in the NASA Global
Change Master Directory (GCMD) (Global Change Master Directory,
2003) form the starting point for the SWEET ontology. This collec-
tion includes both controlled and uncontrolled keywords. The controlled
keywords include approximately 1000 Earth science terms represented
in a subject taxonomy. Several hundred additional controlled keywords
are defined for ancillary support, such as: instruments, data centers,
missions, etc. The controlled keywords are represented as a taxonomy.
The uncontrolled keywords consist of 20,000 terms submitted by data
providers. These terms tend to be more general than or synonymous
with the controlled terms. Examples of frequently submitted terms in-
clude: climatology, remote sensing, EOSDIS, statistics, marine, geology,
vegetation, etc.
Some of the SWEET ontologies represent the Earth realm and phe-
nomena and/or physical aspects and phenomena. These include the
“Earth Realm” ontology which has elements related to “atmosphere”,
“ocean” etc., Physical aspects ontologies represent things like substances,
living elements and physical properties. However the ontologies most
relevant to sensor data are those representing (i) Units, (ii) Numerical
entities, (iii) Temporal entities, (iv) Spatial entities, and (v) Phenomena.
4.2.2 Query Languages.
While RDF, OWL and other for-
malisms serve the purpose of data and knowledge representation, one
also needs a mechanism for querying any data and knowledge stored.
SPARQL (SPARQL Protocol and RDF Query Language) [88] is an RDF
query language for querying and manipulating data stored in the RDF
format. SPARQL allows writing queries over data as perceived as triples.
It allows for a query to consist of triple patterns, conjunctions, disjunc-
tions, and optional patterns. SPARQL closely follows SQL syntax. As a
result, its query processing mechanisms are able to inherit from standard
database query processing techniques. A simple example of an SPARQL
query, which returns the name and email of every person in a data set is
provided in
Figure 12.7
.
Significantly, this query can be distributed to
multiple SPARQL endpoints for computation, gathering and generation
of results. This is referred to as a
Federated Query
.
SPARQLstream
[89] is an extension of SPARQL that facilitates query-
ing over RDF streams. This is particularly valuable in the context of
sen-
sor data
, which is generally stream-based. An RDF stream is defined as a
sequence of pairs (
T
i
,i
)where
T
i
is an RDF triple
<h
si
;
p
i
;
o
ii
>
and
i
is a
time-stamp which comes from a monotonically non-decreasing sequence.
