In the age of Big Data and Internet of Things the integration of traditional and contemporary intelligent computing techniques continues to play an increasingly vital role in data analysis, real-time control and operation, decision making, and evaluation and forecasting. Many intelligent techniques, such as fuzzy logic and genetic algorithms, were initially proposed to deal with a certain type of datasets, whose success then led to the generalization of many algorithms that can deal with common types of datasets. With the rapid advancement in pervasive computing and integration with big datasets, digital datasets in different formats have been exponentially collected from different organisations and projects. People and organisations often need to deal with these heterogeneous datasets nowadays; hence expect integrated data-centric computing algorithms and/or systems to meet the needs of their business activities. Therefore, integrated data-centric intelligent computing and systems are finding increasingly more applications from community based business transactions to intelligent transport systems.