No abstract

Big Data constituting from the information shared in the various social network sites have great relevance for research to be applied in diverse fields like marketing, politics, health or disaster management. Social network sites like Facebook and Twitter are now extensively used for conducting business, marketing products and services and collecting opinions and feedbacks regarding the same. Since data gathered from these sites regarding a product/brand are up-to-date and are mostly supplied voluntarily, it tends to be more realistic, massive and reflects the general public opinion. Its analysis on real time can lead to accurate insights and responding to the results sooner is undoubtedly advantageous than responding later.  In this paper, a cloud based system for real time targeted advertising based on tweet sentiment analysis is designed and implemented using the big data processing engine Apache Spark, utilizing its streaming library. Application is meant to promote cross selling and provide better customer support.

Disseminated histoplasmosis is a relentlessly progressive granulomatous disease which can mimic many other granulomatous diseases including tuberculosis. A 48-year-old male was referred to us with 11 months history of multiple subcutaneous swellings and ulcerations over the upper and lower limbs and fever for 2 months. He was evaluated outside for several months and received anti tubercular drugs for about 2 years in the past for a granulomatous infection of bone and soft tissue identified by various biopsies, without any improvement. When he was evaluated and fresh biopsies were taken, they were stained for fungus and disseminated histoplasmosis was detected. This case confirms the importance of considering fungal infections as a possibility while treating disseminated granulomatous infections, even in immune-competent patients, especially if response to treatment is inadequate.

Big Data Analytics has been gaining much focus of attention lately as researchers from industry and academia are trying to effectively extract and employ all possible knowledge from the overwhelming amount of data generated and received. Traditional data analytic methods stumble in dealing with the wide variety of data that comes in huge volumes in a short period of time, demanding a paradigm shift in storage, processing and analysis of Big Data. Owing to its significance, several agencies including U.S. government have released huge funds for research in Big Data and allied fields in recent years. This paper presents a brief overview of research progress in various areas associated to Big Data Processing and Analytics and conclude with a discussion on research directions in the same area.