A primary challenge to large-scale data integration is creating semantic equivalences between elements from different data sources that correspond to the same real-world entity or concept. Dataspaces propose a pay-as-you-go approach: automated mechanisms such as schema matching and reference reconciliation provide initial correspondences, termed candidate matches, and then user feedback is used to incrementally confirm these matches. The key to this approach is to determine in what order to solicit user feedback for confirming candidate matches.

Data captured from the physical world through sensor devices tends to be noisy and unreliable. The data cleaning process for such data is not easily handled by standard data warehouse-oriented techniques, which do not take into account the strong temporal and spatial components of receptor data. We present Extensible receptor Stream Processing (ESP), a declarative query-based framework designed to clean the data streams produced by sensor devices.

The emergence of large-scale receptor-based systems has enabled applications to execute complex business logic over data generated from monitoring the physical world. An important functionality required by these applications is the detection and response to complex events, often in real-time. Bridging the gap between low-level receptor technology and such high-level needs of applications remains a significant challenge.We demonstrate our solution to this problem in the context of HiFi, a system we are building to solve the data management problems of large-scale receptor-based systems. Specifically, we show how HiFi generates simple events out of receptor data at its edges and provides high-functionality complex event processing mechanisms for sophisticated event detection using a real-world library scenario.

We are developing a distributed query processor called PIER, which is designed to run on the scale of the entire Internet. PIER utilizes a Distributed Hash Table (DHT) as its communication substrate in order to achieve scalability, reliability, decentralized control, and load balancing. PIER enhances DHTs with declarative and algebraic query interfaces, and underneath those interfaces implements multihop, in-network versions of joins, aggregation, recursion, and query/result dissemination. PIER is currently being used for diverse applications, including network monitoring, keyword-based filesharing search, and network topology mapping. We will demonstrate PIER's functionality by showing system monitoring queries running on PlanetLab, a testbed of over 300 machines distributed across the globe.