Composing Pervasive Data Using iQL

The emergence of pervasive networked data sources, such as web services, sensors, and mobile devices, enables context-sensitive, mobile applications. We have developed a programming model for writing such applications, in which entities called composers accept data from one or more sources, and act as sources of higher-level data. We have defined and implemented a nonprocedural language, iQL, specifying the behavior of composers. An iQL programmer expresses requirements for data sources rather than identifying specific sources; a runtime system discovers appropriate data sources, binds to them, and rebinds when properties of data sources change. The language has powerful operators useful in composition, including operators to generate, filter, and abstract streams of values. We are witnessing explosive growth in pervasive net-worked data sources, such as web services, fixed sensors measuring traffic or weather, and mobile devices reporting position. These data sources enable context-sensitive, mobile applications, such as location monitoring, fleet management, and emergency notification. Such an application must specify how the raw data provided by net-worked data sources is composed into the higher-level data that it needs. We have developed a programming model and a language, named iQL, for specifying data-composition rules. We have implemented the language and a runtime system that frees the application developer from many of the details that must be addressed when dealing with such data sources, including the management of widely varying protocols and formats, the discovery of appropriate data sources, and the replacement of data sources that have failed or become unreachable. Our programming model is motivated by special characteristics of pervasive networked sources, the data they provide, and ways in which that data is used: • Some data sources take the initiative in supplying data, while others do not report a value unless asked to do so. • Pervasive data sources may fail unexpectedly, or provide inconsistent quality of service or information. • There are often alternative ways of retrieving or deducing the same data, perhaps with different quality of service or information, from different data sources. • Pervasive networked data sources use a wide variety of access protocols, data rates, and formats. • Raw, low-level, voluminous data, closely aligned with the characteristics of the data source, passes through a hierarchy of data-reduction transformations such as aggregation, summarization, and filtering, resulting in refined, abstract, filtered data, closely aligned with the concerns of the application. • The generation of a value by a data source can …