Workgreen2012

Building a Data Warehouse for Twitter Stream Exploration

Naseem Ur Rehman · Svetlana Mansmann · Marc H. Scholl

40 citations0/0 files storedIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Big Data and Business Intelligence Advanced Database Systems and Queries Information Retrieval and Data Mining

Abstract

In the recent year Twitter has evolved into an extremely popular social network and has revolutionized the ways of interacting and exchanging information on the Internet. By making its public stream available through a set of APIs Twitter has triggered a wave of research initiatives aimed at analysis and knowledge discovery from the data about its users and their messaging activities. While most of the projects and tools are tailored towards solving specific tasks, we pursue a goal of providing an application in dependent and universal analytical platform for supporting any kind of analysis and knowledge discovery. We employ the well established data warehousing technology with its underlying multidimensional data model, ETL routine for loading and consolidating data from different sources, OLAP functionality for exploring the data and data mining tools for more sophisticated analysis. In this work we describe the process of transforming the original stream into a set of related multidimensional cubes and demonstrate how the resulting data warehouse can be used for solving a variety of analytical tasks. We expect our proposed approach to be applicable for analyzing the data of other social networks as well.

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References

1996The data warehouse toolkit: practical techniques for building dimensional data warehouses 1993Providing OLAP (On-line Analytical Processing) to User-Analysts : An IT Mandate 1998THE DIMENSIONAL FACT MODEL: A CONCEPTUAL MODEL FOR DATA WAREHOUSES 2011Tweets from Justin Bieber's heart 2001Database technology for decision support systems 2007Decision Support System for Managing Educational Capacity Utilization

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