This paper takes Twitter as the framework and intended to propose an optimum approach for classification of Twitter data on the basis of the contextual and lexical aspect of tweets. It is a dire need to have optimum strategies for offensive content detection on social media because it is one of the most primary modes of communication, and any kind of offensive content transmitted through it may harness its benefits and give rise to various cyber-crimes such as cyber-bullying and even all content posted during the large even on twitter is not trustworthy. In this research work, various facets of assessing the credibility of user generated content on Twitter has been described, and a novel real-time system to assess the credibility of tweets has been proposed by assigning a score or rating to content on Twitter to indicate its trustworthiness. A comparative study of various classifying techniques in a manner to support scalability has been done and a new solution to the limitations present in already existing techniques has been explored.

Ontology can be defined as hierarchical representation of classes, sub classes, their properties and instances. It has led to understanding the concepts of given domain, deriving relationships and representing them in machine interpretable language. Ontologies are associated with different languages that are used in mapping of multiple ontologies. Several applications of ontologies have led towards realization of semantic web. The current web (2.0) is approaching towards semantic web (3.0) that performs intelligent search and stores results in distributed databases.
The paper makes readers aware of various aspects of ontology like types of ontology, ontology development life cycle phases, activities involved in ontology development and ontology engineering tools. Ontology engineering contributes to meaningful search and provides with open source tools for deploying and building ontologies.

The influx of Big Data on the Internet has become a question for many businesses of how they can benefit from big data and how to use cloud computing to make it happen. The magnitude at which data is getting generated day by day is hard to believe and is beyond the scope of a human’s capability to view and analyze it and hence there is an imperative need for data management and analytical tools to leverage this big data. Companies require a fine blend of technologies to collect, analyze, visualize, and process large volume of data. Big Data initiatives are driving urgent demand for algorithms to process data, accentuating challenges around data security with minimal impact on existing systems. In this paper, we present many existing cloud storage systems and query processing techniques to process the large scale data on the cloud. The paper also explores the challenges of big data management on the cloud and related factors that encourage the research work in this field.