The concept of social stratification and hierarchy among human dates is back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such "trust hierarchies" is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks.

The worldwide oil & gas industry is one of the world's most complex business networks, and is connected with almost every supply chain branch. It includes international and domestic transportation, materials handling, ordering and inventory visibility and control, import/export facilitation and social network, etc. Traditionally, it has been influenced by big oilfield companies. However, in recent years the industry has been changing into a more heterogeneous and diverse network of businesses, and the oilfields are getting smaller and more diverse. One of the reason could be dwindling the oil reserves and growing specialized companies which are able to extract hydrocarbons; another reason is the restructuring and globalization of the entire business as well as some new technology implementing. Using agent-based modelling and big data technology integrity, we are able to optimize supply chain in oil and gas industries.

Recently, everyday large amount of data continuously is generating from different sources such as social medias, social networks, Internet of things(IoT) devices, online games, healthcare data, and etc. This provide various challenges and opportunities for different businesses and sectors. Apart from challenges, shortage of storages and processing facilities, lack of management platforms to handle such a great volume of data, security and privacy issues, and to name but a few. On the other hand, analysing these data which is called "Big Data" could provide new insight into better understanding the hidden patterns. Big Data technologies and tools would be the appropriate solution to provide data scalability, availability and solve the problem of variety, volume and velocity(3Vs) of data. Twitter is a microblogging website which has been popular amount people to share their thought and idea between other users. Analysing such a valuable data, we are able to find out people's opinion about particular issue. Mining customer opinions from their tweets could help to find strengths and weaknesses about a company's products and services, features, and businesses. There are a number of studies in the respect of twitter sentiment analysis and opinion mining. However, the number of tweets have been used were small, and in term of using such a data in practice, the research are rare. In this paper, we first briefly demonstrate the reason why big data technologies need to be adopted, and then by giving an example, we presented different steps require to collect, store, process and analyse twitter data in large scale using different big data platforms and software.