Cognitive Computing for Efficient Knowledge Discovery in Bigdata

Big data is computing process associated with the collection of huge data sets, these data when analyzed can reveal patterns and trends according to the field from which the data is extracted. The use of Internet of Things based devices which sense the information from various devices from mobiles, camera, software usages, remote sensing, Radio frequency readers and many more has increased the growth of data. Many challenges arise when the quantity of data increases rapidly; even the analysts and data scientists may feel the difficulties with the increased data demand. A swift automatic self-learning environment can have the potential to convert this enormous amount of data.   

           

Adopting cognitive computing based approaches can reduce the concerns faced by the problems and challenges in big data. The cognitive based study on big data can give the researchers a better picture towards the evolution of data with futuristic knowledge. The latest improvements in the data processing have made the process of communication very easier for cognitive computing. Cognitive computing with integrated intelligence and machine learning can do analytical wonders in big data analytics. There are many impressive models already available with built in neural networks and natural language processing. However, using a real time cognitive computing environment can lessen the work process by reduction in energy utilization and privacy measures.

Cognitive Computing with big data can create a system which will be more insightful and urges the discovery of more knowledge based experiences. Since, the increase in the need of information management and data based analytics in the areas of big data; cognitive based analytic solution can be used in building an intelligent infrastructure. When computers can augment a human knowledge and create an intelligent system which thinks similar to a human being, accurate predictions and decision making is possible with cognitive computing models.