MapReduce

JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY SCHOOL OF COMPUTING AND INFORMATION TECHNOLOGY Optimized Dynamic Latent Topic Model for Big Text Data Analytics NAME: Geoffrey Mariga Wambugu REGISTRATION NUMBER: CS481-4692/2014 LECTURER: Prof. Waweru Mwangi A thesis proposal submitted in partial fulfilment of the requirement for the Unit SCI 4201 Advanced Research Methodology of the degree of Doctor of Philosophy in Information Technology at the School of Computing and Information Technology

Survey on graph databases XIAOTONG FU Informatics, University of Edinburgh Abstract. Graph databases, also called graph-oriented database, is a type of not only SQL (NoSQL) database based on graph theory that can store, map and query data relationships. Because this kind of database ensures its robust performance in processing graph-like data, it has been widely used in industry, for instance, Facebook and Twitter are using graph databases to store and analyze their user proles. This paper re- viewed

Development of Business Analytics Curricula - A Research Paper Apoorva Mathur Department of Operations Management & Information Systems, College of Business, Northern Illinois University Dekalb, IL 60115 USA Abstract The world is changing around us rapidly. In today’s marketplace, the main focus of any organization is to keep up the impact of effect of quick changes, monetary movements, expanding asset shortage, urbanization, aggressive competitors and innovation leap forward. They depend on the

Introduction With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity;

Introduction With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity;

Special Publication 800-146 DRAFT Cloud Computing Synopsis and Recommendations Recommendations of the National Institute of Standards and Technology Lee Badger Tim Grance Robert Patt-Corner Jeff Voas NIST Special Publication 800-146 DRAFT Cloud Computing Synopsis and Recommendations Recommendations of the National Institute of Standards and Technology Lee Badger Tim Grance Robert Patt-Corner Jeff Voas C O M P U T E R S E C U R I T Y Computer Security Division Information Technology

SECURITY GUIDANCE FOR CRITICAL AREAS OF FOCUS IN CLOUD COMPUTING V3.0 SECURITY GUIDANCE FOR CRITICAL AREAS OF FOCUS IN CLOUD COMPUTING V3.0 INTRODUCTION The guidance provided herein is the third version of the Cloud Security Alliance document, “Security Guidance for Critical Areas of Focus in Cloud Computing,” which was originally released in April 2009. The permanent archive locations for these documents are: http://www.cloudsecurityalliance.org/guidance/csaguide.v3.0.pdf (this document)