In an Artificial Intelligence world, Agent-based technology is one of the most vibrant and important areas of R&D, emerging in Information Technology in the industry in recent years. Intelligent Agent (IA) is an autonomous entity which observes, analyses and responds to an environment appropriate to achieve the expected objective. The IA posses several categories such as Coordination, Integration, Read More

Graph analytics, which is an analytics alternative that uses an abstraction called a graph model. The simplicity of this model allows for rapidly absorbing and connecting large volumes of data from many sources in ways that finesse limitations of the source structures (or lack thereof, of course). Graph analytics is an alternative to the traditional data warehouse model as a framework for absorbing both structured and unstructured data from various sources to enable anal... Read More

The best way to predict the future is to study past behavior. This is the underlying idea behind Big Data Analytics. The 2008 Obama election campaign was one of the first to take advantage of data-driven methods in the race to an elected office. The Obama campaign had a data analytics team of 100 people. This shows how deeply data analytics impacts the world. Read More

Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics. A data lake could be a merging point of new and historic data, thereby drawing correlations across all data using advanced analytics. A data lake can support the self-service data practices. Read More

Nowadays organisations are starting to realise the importance of using more data in order to support decision for their strategies. The size of data in the world is growing day by day. Data is growing because of vast use of internet, smart phone and social network. Big data is a collection of data sets which is very large in size as well as complex. Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse... Read More

The biggest challenge of genetic research lies in significant and intellectual analysis of the large and complex data sets generated by the cutting edge techniques like massively parallel DNA sequencing and genome wide analysis. Statistical analyses are the most important of such experimental data. When the data are not normally distributed and using non numerical (rank, categorical) data then use the nonparametric test for exact result of research hypothesis. Order stat... Read More

This paper discusses the role of the data scientist, what a data scientist is, and the set of skills needed to become one. Read More

Ultimately, the widespread adoption of automation and robotics, as well as the rise of artificial intelligence will have profound impacts on the world, particularly in terms of human employment and even the global economy, such as the questions asked by Martin Ford, in his book, Rise of the Robots. Martin Ford suggests the outlook is bleak for millions of workers who define their self-worth in terms of their employment. Increasingly workers will no longer be exploited by... Read More

This paper focuses on how AI is disrupting the insurance sector. Starting from the conventional insurance process, we will move through the specific novelties AI is introducing in the field in order to understand how AI is completely changing the way people buy and think of insurance products. Finally, an eight-group classification is proposed for the insurtech ecosystem. Read More

Big data is often approached as a mere technical problem, while many times projects fail because of lack of strategic focus. Implementing the right process may help companies to efficiently run analytics works and for this reason, the paper proposes a few concepts that can support the organizational development of a big data blueprint. Three main ideas will be outlined here: the data lean approach, the maturity map, and different organization models for a data team. Read More