We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. The resulting network- and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures.