A. SEWBOK

According to the book of System Analysis and Design in a changing world by Satzinger, there are six core process of the Systems Development life Cycle (SDLC) and the idea is each system gets evolved over several iterations and each iteration consists of the following six core processes:

1.      Identify the problem or need and obtain approval to proceed.

2.      Plan and monitor the project- what to do, how to do it, and who does it.

3.      Discover and understand the details of the problem or the need.

4.      Design the system components that solve the problem or satisfy the need.

5.      Build, test, and integrate system components.

6.      Complete system tests and then deploy the solution.

 

There is also a defined process known as The Software Engineering Body of Knowledge (SWEBOK) that promotes a consistent view of Software Engineering and is a guide to the main broken Knowledge Areas. According to SWEBOK, there are 10 Knowledge Areas that each contains a reasonable topic list presenting sound information about Software Engineering.

The first 5 Knowledge Areas cover the repeatable processes (Requirement, Design, Construction, Testing, and Maintenance). And there are five more knowledge areas that are about managed processes including (Software Configuration Management, Software Engineering Management, Software Engineering Process, Software Engineering Tools and Methods, Software Quality).

 

B. INDUSTRIAL INTERNET

 It's the convergence of the global industrial system with the power of advanced computing, analytics, low-cost sensing and new levels of connectivity permitted by the internet. And with it, there's major transformation taking place in the industry. Managing equipment assets with big-data analytics increases uptime, production, efficiency, and throughput. Using analtyics for operational efficiencies can drive even greater profitability for industrial machines.

·        Assess where your business is today on the spectrum of expected maturity or capabilities in terms of adopting the Industrial Internet

·        Determine where you want to be in the future

·        And understand the best solution pathway to help you achieve your business priorities

 

 

C. CYBER-PHYSICAL SYSTEMS

 

In the context of CPS, tools are software systems for specifying requirements or designs, simulating systems, and analyzing designs. Cyber-physical systems are intrinsically concurrent. At a minimum, the cyber and the physical subsystems coexist in time, but even within these subsystems, concurrent processes are common. Models of concurrency in the physical world (coexisting physical dynamics in a time continuum) are very different from models of concurrency in software (arbitrary interleaving of sequences of atomic actions), and very different from models of concurrency in networks (asynchronous, partially-ordered discrete actions or clock-driven time slots). Reconciling these divergent models of concurrency, and ensuring interoperability and communication between components that have divergent models of concurrency, is a central problem in CPS.

 

D. BIG DATA

 

Big data analytics is the process of examining large data sets containing a variety of data types -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs.

 

E. WOLFARM

·        Automatically preprocess data, including missing-values imputation, normalization, and feature selection with built-in machine learning capabilities

·        Semantically import and structure your data

·        Create, schedule, and share interactive reports in the cloud automatically

·        Build customized analytical tools

·        Fit data to common models or to newly developed theoretical models

·        Study voting patterns or other social statistics

·        Merge built-in economic data with your customer data to understand how changes in sales are linked to broader events

·        Make statistical visualizations with a high level of algorithm automation and computational aesthetics

·        Easily create interactive tools for analyzing your data

·        Develop original, sophisticated algorithms for data science

·        Import, analyze, visualize, and publish in a single end-to-end data science environment