  Nowadays, there is a rapid increase in the number of sensor data generated by
a wide variety of sensors and devices. Data semantics facilitate information
exchange, adaptability, and interoperability among several sensors and devices.
Sensor data and their meaning can be described using ontologies, e.g., the
Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched,
the size of semantic sensor data is substantially larger than raw sensor data.
Moreover, some measurement values can be observed by sensors several times, and
a huge number of repeated facts about sensor data can be produced. We propose a
compact or factorized representation of semantic sensor data, where repeated
measurement values are described only once. Furthermore, these compact
representations are able to enhance the storage and processing of semantic
sensor data. To scale up to large datasets, factorization based, tabular
representations are exploited to store and manage factorized semantic sensor
data using Big Data technologies. We empirically study the effectiveness of a
semantic sensor's proposed compact representations and their impact on query
processing. Additionally, we evaluate the effects of storing the proposed
representations on diverse RDF implementations. Results suggest that the
proposed compact representations empower the storage and query processing of
sensor data over diverse RDF implementations, and up to two orders of magnitude
can reduce query execution time.
