In environments that require the use of software applications, intervals where application functionality, tools, methods and technical systems are changing are often very short. The process of searching for relevant information about a specific issue is frequently executed and time-consuming. Because of the availability of a nearly unlimited amount of data, people spend a lot of time in formulating search queries and evaluating the relevance of the search results. In this paper, we describe a generic approach that improves the information search and retrieval process of different activities with the use of context information. One main goal is the integration of the crowd€ at different stages of this process by combining collective intelligence concepts with context-aware systems. This combination can be used to automatically reduce information overflow by filtering irrelevant data. Furthermore, a real-time information retrieval process without manual search im pulses is provided. We also present a prototype as a proof of concept in order to validate feasibility and benefit.

This paper discusses the combination of context injection, metadata enrichment and information broking with current semantic web and community oriented concepts in order to optimize the search and retrieval process of relevant information. The presented approach focuses the shift from information pull to information push, and thus reduces the effort on finding the needed information for a specific issue in a concrete context.