Archive | 2019
Multidimensional Analysis of Big Data
Abstract
Data warehousing and multidimensional analysis go side by side. Data warehouses provide clean and partially normalized data for fast, consistent, and interactive multidimensional analysis. With the advancement in data generation and collection technologies, businesses and organizations are now generating big data (defined by 3Vs; i.e., volume, variety, and velocity). Since the big data is different from traditional data, it requires different set of tools and techniques for processing and analysis. This chapter discusses multidimensional analysis (also known as on-line analytical processing or OLAP) of big data by focusing particularly on data streams, characterized by huge volume and high velocity. OLAP requires to maintain a number of materialized views corresponding to user queries for interactive analysis. Precisely, this chapter discusses the issues in maintaining the materialized views for data streams, the use of special window for the maintenance of materialized views and the coupling issues of stream processing engine (SPE) with OLAP engine. Multidimensional Analysis of Big Data