Mashup-based information retrieval for domain experts

Abstract

In this paper, we tackle the problem of helping domain experts to construct, parameterize and deploy mashups of data and code. We view a mashup as a data processing flow, that describes how data is obtained from one or more sources, processed by one or more components, and finally sent to one or more sinks. Our approach allows specifying patterns of flows, in a language called Cascade. The patterns cover different possible variations of the flows, including variations in the structure of the flow, the components in the flow and the possible parameterizations of these components. We present a tool that makes use of this knowledge of flow patterns and associated metadata to allow domain experts to explore the space of possible flows described in the pattern. The tool uses an AI planning approach to automatically build a flow, belonging to the flow pattern, from a high-level goal, specified as a set of tags. We describe examples from the financial services domain to show the use of flow patterns in allowing domain experts to construct a large variety of mashups rapidly. Copyright 2009 ACM.