Internet of Things (IoT) is booming. The “Software for the Internet of
Things (IoT) Developer Survey” report, published by Embarcadero
Technologies last month, shows that 77% of development teams will have IoT
solutions in active development in 2015 with almost half (49%) of IoT
developers anticipating their solutions will generate business impacts by the
end of this year.
IoT Maturity Model (IoTMM) is a qualitative method to gauge the growth and
increasing impact of IoT capabilities in an IT environment from both business
and technology perspectives. It comprises a set of criteria, parameters and
factors that can be used to describe and measure the effectiveness of the IoT
adoption and implementation.
Five levels of maturity are defined: Advanced, Dynamic, Optimized, Primitive,
and Tentative (ADOPT). The definitions of these 5 levels are specified below:
Level Desc... (more)
I will present a tutorial on the service-oriented model-driven architecture
design for cloud solutions in the upcoming International Conference on Web
Services (ICWS 2009). Please join the session to explore the state-of-the-art
approach to effectively developing cloud services in a systematic fashion.
Contact Tony Shan (email@example.com) for more info.
Though the industry focus has shifted from SOA to cloud computing in the
recent years, SOA still serves as the foundation of disciplined IT
solutioning, particularly in the software-centric space. It is necessary to
identify the commonalities between SOA and cloud.
For instance, both are intended to yield increased agility, enable faster
time-to-market, drive more cost savings, lead to reduced integration, and
facilitate easier outsourcing.
More importantly, it is critical to differentiate these two items from a
variety of perspectives. The highlights of the key differences are
Nontraditional databases have grown tremendously in the past few years. Now
we have literally a few hundred Big Data stores and more are coming. The
upside is that all of these drive innovations and lower the cost for
customers. The downside is that it is easy for an end-user to get swamped
with so many choices and sometimes become lost. It is important to classify
these Big Data stores in such a way that one can sort them out and find the
best match swiftly in the selection process.
A matrix is developed to group various options available in the market. It
has 2 dimensions: Hori... (more)
IDG Enterprise's 2015 Big Data and Analytics survey shows that the number of
organizations with deployed/implemented data-driven projects has increased by
125% over the past year. The momentum continues to build.
Big Data as a concept is characterized by 3Vs: Volume, Velocity, and Variety.
Big Data implies a huge amount of data. Due to the sheer size, Big Data tends
to be clumsy. The dominating implementation solution is Hadoop, which is
batch based. Not just a handful of companies in the market merely collect
lots of data with noise blindly, but they don't know how to cleanse it,... (more)