Title: Graph of Virtual Actors (GOVA): a Big Data Analytics Architecture for IoT,
Abstract: With the emergence of cloud computing and sensor technologies, Big Data
analytics for the Internet of Things (IoT) has become the main force behind
many innovative solutions for our society's problems. This paper provides
practical explanations for the question "why is the number of Big Data
applications that succeed and have an effect on our daily life so limited,
compared with all of the solutions proposed and tested in the literature?",
with examples taken from Smart Grids. We argue that "noninvariants" are the
most challenging issues in IoT applications, which can be easily revealed if we
use the term "invariant" to replace the more common terms such as
"information", "knowledge", or "insight" in any Big Data for IoT research. From
our experience with developing Smart Grid applications, we produced a list of
"noninvariants", which we believe to be the main causes of the gaps between Big
Data in a laboratory and in practice in IoT applications. This paper also
proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture
for IoT applications, which not only can solve the noninvariants issues, but
can also quickly scale horizontally in terms of computation, data storage,
caching requirements, and programmability of the system.