Leading by Game-Changing Cloud, Big Data and IoT Innovations

Tony Shan

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Top Stories by Tony Shan

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)

Service-Oriented Model-Driven Architecture Design for Cloud Solutions

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 (tonycshan@gmail.com) for more info. ... (more)

Why SOA and Cloud Combined?

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 i... (more)

Taxonomy of Big Data Stores

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)

Big Data Is Really Dead | @ThingsExpo #BigData #IoT #InternetOfThings

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)