With the dramatic increase in the speed and availability of computer networks, a significant proportion of all economic activities is now conducted electronically. In particular, the field of financial trading has seen an unprecedented increase in both the number of participants and the volume of trades conducted via electronic markets. As a result, high frequency data has become increasingly available for historical analysis by researchers in fields like econometrics, finance and accounting. Analyzing such datasets requires - at least - expert domain knowledge (e.g. in finance and microeconomics), experience, and IT skills. In addition to being able to identify suitable data sources and specify the right search criteria, users must be able to perform a wide range of analysis functions (statistical, data mining, language processing) and present results in a suitable form (e.g. through visualization or report creation). Analysis processes cannot be determined in advance as users tend to perform tasks in a piecemeal fashion. When the type of analysis is complex, users spend a lot of effort cleaning, reading and interpreting the data, converting datasets from one format to another, copying some results from one file to another, and merging datasets with different semantics. These activities increase analysis time and the risk of errors. In this project, an environment will be developed that supports a Service-Oriented Architecture (SOA), making it possible to define re-usable and interoperable software components as web services which can manipulate the elements of an underlying, event-based data model. Such a model allows a coherent representation of market activities - particularly high-frequency market and news data - as events.