Database Reference
In-Depth Information
Unlike the world wide web of documents, in which the objects them-
selves are described in terms of a natural lexicon, the objects and data
in the internet of things, are heterogeneous, and may not be naturally
available in a su
ciently descriptive way to be searchable, unless an ef-
fort is made to create standardized descriptions of these objects in terms
of their properties. Frameworks such as RDF provide such a standard-
ized descriptive framework, which greatly eases various functions such
as search and querying in the context of the underlying heterogeneity
and lack of naturally available descriptions of the objects and the data.
Semantic technologies are viewed as a key to resolving the problems
of inter-operability and integration within this heterogeneous world of
ubiquitously interconnected objects and systems [65]. Thus, the
Inter-
net of Things
will become a
Semantic Web of Things
. It is generally
recognized that this interoperability cannot be achieved by making ev-
eryone comply to
too many
rigid standards in ubiquitous environments.
Therefore, the interoperability can be achieved by designing middleware
[65], which acts as a seamless interface for joining heterogeneous com-
ponents together in a particular IoT application. Such a middleware
offers application programming interfaces, communications and other
services to applications. Clearly,
some
data-centric standards are still
necessary, in order to represent and describe the properties of the data
in a homogenous way across heterogeneous environments.
The internet of things requires a plethora of different middlewares, at
different parts of the pipeline for data collection and cleaning, service en-
ablement etc. In this section, we will study the data management issues
at different stages of this pipeline. First, we will start with data cleaning
and pre-processing issues, which need to be performed at data collection
time. We will follow this up with issues of data and ontology representa-
tion. Finally, we will describe important data-centric applications such
as mining with big data analytics, search and indexing.
4.1 Data Cleaning Issues
The data cleaning in IoT technology may be required for a variety
of reasons: (a) When is data is collected from conventional sensors, it
may be noisy, incomplete, or may require probabilistic uncertain mod-
eling [34]. (b) RFID data is extremely noisy, incomplete and redun-
dant because a large fraction of the readings are dropped, and there are
cross-reads from multiple sensor readers. (c) The process of privacy-
preservation may require an intentional reduction of data quality, in
which case methods are required for privacy-sensitive data processing
[6].
