Big Data

– Big Data Analytic

Big data analytics is the process of examining (i.e., data mining) large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. There are two types of big data: data at rest (e.g., collection of what has streamed, web logs, emails, social media, unstructured documents and structured data from disparate system) and data in motion (e.g., twitter/facebook comments, stock market data and sensor data). Dealing effectively with big data despite its volume and variety requires efficient analysis of this data while it is still in motion, not just after it is at rest. Currently, there are essentially three approaches for Big Data Analytics: direct analytics over Massively Parallel Processing Data Warehouses, indirect analytics using Hadoop and direct analytics using Hadoop (which is a framework widely used in academia and industry in order to perform Big Data Analytics). With respect to Big Data Analytics, I am involved in the following projects:

A. Multiplayer Online Game Players’ Movement Prediction: an Effective Solution for Minimizing the Consequences of Poor Internet Quality

B. Social Network-based Data Dessemination in Vehicular Networks

– Big Data Infrastructure

Work Distribution, Service Migration and Replica Placement algorithms and their simulations for MapReduce in the context of the Hadoop framework.

– Big Data Applications: Information Security Issues Pertaining to Big Data in Healthcare

A. A Patient-Oriented Computer-Aided Approach to Improve Chronic Disease Care for Children
B. A Privacy-Protective Method for the Disclosure of Small Geographic Areas in Health Research