Data Analysis and Patterns in Data

The existence of easily accessible big data sets and the ability to extract meaningful information from them will shape the future in many research areas. From the analysis of the history of financial data, coupled with the history of the sentiment extracted from the web, one may try to understand better market reaction to the economic figures announcement.  From the analysis of household expenditure one might be able to better predict responses to regulatory change in mortgage markets, and ultimately understand the stability of the financial system. If one could quickly determine from the huge flow of data in an exchange that a flash crash was in process this would have value.  Large heterogeneous data sets demand development of novel methodology to better describe, and detect patterns.

OMI houses many important sources of data and provides support for researchers doing data driven research.  In addition it has a number of teams with deep and varied experience at extracting information from complex data. Computer scientists extract information from the web, engineers bring machine learning, statisticians bring classical data mining tools and mathematicians bring novel understanding and new tools: for example using rough paths theory to classify points in the data sets and use this focussed classification to concisely and easily extract useful information.


Meet the OMI members whose research relates to Data Analysis and Patterns in Data.