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vertical partitioning approach [1]. This is closely related to the develop-
ment of column-oriented databases for sensor data management [98, 2,
3]. Consider a situation in which we have
m
different properties in the
data. In such a case, a total of
m
two-column tables are created. Each
table contains a subject and object column, and if a subject is related to
multiple objects, this corresponds to the different rows in the table. The
tables may be stored by subject, and this can enable quick location of
a specific subject. Furthermore, each table is sorted by subject, so that
particular subjects can be located quickly, and fast merge-joins can be
used to reconstruct information about multiple properties for subsets of
subjects. This approach is combined with a column-oriented database
system [98] in order to achieve better compression and performance. In
addition, the object columns of the scheme can be indexed with the use
of a B
+
-Tree or any other index. It was argued in [110] that the scheme
in [1] is also not particularly effective, unless the properties appear as
bound variables.
It was observed in [110] that while the work in [1] argued against con-
ventional property-table solutions, their solution turned out to be a spe-
cial variation of property tables, and therefore share all its disadvantages.
The two-column tables of [1] are similar to the multi-valued property ta-
bles introduced in [113], and the real novelty of the work in [1] was to
integrate the column-oriented database systems into two-column prop-
erty tables. Therefore, the work in [110] combines a multiple-indexing
scheme with the vertical partitioning approach proposed in [1] in order
to obtain more effective results. The use of multiple indexes has tremen-
dous potential to be extremely effective for semantic web management,
because of its simultaneous exploitations of different access patterns,
while incorporating the virtues of a vertical approach. Multiple index-
based techniques have also been used successfully for a variety of other
database applications such as join processing [15, 79, 80].
4.4 Real-time and Big Data Analytics for The
Internet of Things
Since RFID and conventional sensors form the backbone of the data
collection mechanisms in the internet of things, the volume of the data
collected is likely to be extremely large. We note that this large size is
not just because of the streaming nature of the collected data, but also
because smart infrastructures typically have a large number of objects
simultaneously collecting data and communicating with one another. In
many cases, the communications and data transfers between the objects
may be required to enable smart analytics. Such communications and
