In the recent years, the development of information systems andpractices has brought new emergent paradigms to the scene, mainly in intelligent and social informatics. Cloud computing, internet of things (IoTs) and big data are recent trends in this domain and gaining greater attention equally from academics and industrialists.
Cloud computing focus on distributed information systems based on service models to provide IT services, applications, platforms, storage toward anything in a Pay-as-you-go manner service. No limit to what you can buy/rent on the cloud, it is scalable, configurable and easy to use and deploy compared with the old IT paradigms.
IoTs is an idea focus on machine-to-machine communication toward connecting “everything” to internet forboth continuous and discrete connection; the important here is to have a footprint for “everything” on the internet.Later the footprints can be analysed and fed back as a new insight to serve particular purposes. With the increasing number of social network sites on the internet where people started to enrich the internet content, in 2012, people have created 2.5 quintillion bytes of data every day. Most of these data which were generated by both machines and human characterised as unstructured, heterogeneous and distributed. Thus, have enriched research in the area of big data analysis, institutes intentto know how they can make the maximum benefits of the available big data. Relevant applications can be widely spread in any domain area (finance, security, government, social studies, marketing, product development and manufacturing, etc.). However, the real picture of technology advancement is not that flashing as commercial technologies providers try to describe, where I believe there is still lot have to be done.In the following I will list a number of fruitful research directions of these paradigms which yet not have been widely investigated and of interest to me:
– Big data and ontology: how to bridge the gap between machine and human is an old question, which I believe the answer becomes more visible nowadays. Ontology can ensure interoperability, while big data are there and stored in many ways, ontology can enhance the query of this data by providing semantic, improve integrity between systems and generate meaning from data. Integrating ontology to heterogeneous data sets will be a next step challenge.
– Designing for big data: big data especially the data come from machines/sensors need to be planned and designed carefully. Collecting data from multiple sources and sensors in the real time is a challenging mission; careful design of data semantic, synchronization and labelling is needed for both technical and methodological development.
– Cloud of IoTs as a complex adaptive system: cloud of IoTs can be seen as a complex adaptive system (CAS), these types of systems characterised by uncertainty, nonlinearity, dynamics, self-organization and evolvable, how we can benefits from theory to understand/predict the nature and future of cloud of IoTs systems` network.
– The right analysis for the right purpose: Analysts facing challenges in knowing how they can better analyse and extract insight out of big data, enormous number of analysis tools and mechanisms are available and the question here is how we can choose the right type of analysis mechanism or algorithm to each particular big data analysis purpose. Also, how we can select and blend big data from multiple sources for effective analysis.
– Business-IT alignment: business process modelling is widely regarded technique to model and align business activities. Also, it is considered a great technique for requirements and business-IT alignment. Further improvement is possible in order to align context, business and technology, we need to consider domain ontology development integrated to business processes, this will make business process models more “context aware”, while business process models can be easily aligned to IT (e.g.: BPMN → BPEL → software services → Software applications), it seems quite challenging to integrate it to ontology. I am sure development in this direction will insure full vertical and horizontal alignment of the enterprise activities.
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