Re-finding what we have accessed before is a common behavior in real life. Psychological studies show that context under which information was accessed can serve as a powerful cue for information recall. "Finding the sweet recipe that I read at the hotel on the trip to Africa last year" is a context-based re-finding request example. Inspired by users' recall characteristics and human memory, we present a context memory model, where each context unit links to the data created/accessed before. Context units are organized in a clustering and associative manner, and evolve dynamically in life cycles. Based on the context memory, we build a recall-by-context query model. Two methods are devised to evaluate context-based recall queries. Our experiments with synthetic and real data show that evaluation exploring the use of context associations can get the best response time.

Queries over probabilistic databases lead to probabilistic results. As the process of arriving at these results is based on underlying data probabilities, we believe involving a user in the loop of query processing and leveraging the user's personal knowledge to deal with uncertain data, will enable the system to scrub (correct) and tailor its probabilistic query results towards a better quality from the perspective of the specific user. In this paper, we propose to open the black box of a probabilistic database query engine, and explain to the user how the engine comes up with the probabilistic query result as well as which uncertain tuples in the database the result is derived from. In this way, the user based on his/her knowledge about uncertain information can not only decide how much confidence to be placed on the query engine, but also help clarify some uncertain information so that the query engine can re-generate an improved query result. Two particular issues associated with such a probabilistic database query framework are addressed: (i) how to interact with a user for answer explanation and uncertainty clarification without bringing much burden to the user, and (ii) how to scrub/correct the query result without incurring much computation overhead to the query engine. Our performance study demonstrates the accuracy effectiveness and computational efficiency achieved by the proposed framework.

The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a context-aware middleware for pervasive homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.

We envisage future context-aware applications will dynamically adapt their behaviors to various context data from sources in wide-area networks, such as the Internet. Facing the changing context and the sheer number of context sources, a data management system that supports effective source organization and efficient data lookup becomes crucial to the easy development of context-aware applications. In this paper, we propose the design of a new context data management system that is equipped with query processing capabilities. We encapsulate the context sources into physical spaces belonging to different context spaces and organize them as peers in semantic overlay networks. Initial evaluation results of an experimental system prototype demonstrate the effectiveness of our design.

Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.

In-network sensor query processing is a cross-layer design paradigm in which networked sensor nodes process data acquisitional queries in collaboration with one another. As power efficiency is still one of the most severe constraints in this paradigm, we propose a distributed, cross-layer scheduling scheme for it. In this scheme, each node employs its MAC, routing, and query layers to negotiate with its parent its timing for transmission and constructs a schedule for its query processing. It then follows the schedule to compute, communicate, and sleep in each query processing cycle. This scheduling reduces wasted listening and receiving as well as the switching between active and sleeping modes. Consequently, it results in 50-60% of power saving on real sensor nodes in our experiments. Additionally, it outperforms two existing scheduling schemes both on schedule construction efficiency and on schedule quality.

Many sensor network applications, such as object tracking and disaster monitoring, require effective techniques for event detection. In this paper, we propose a novel event detection mechanism based on matching the contour maps of in-network sensory data distribution. Our key observation is that events in sensor networks can be abstracted into spatio-temporal patterns of sensory data and that pattern matching can be done efficiently through contour map matching. Therefore, we propose simple SQL extensions to allow users to specify common types of events as patterns in contour maps and study energy-efficient techniques of contour map construction and maintenance for our pattern-based event detection. Our experiments with synthetic workloads derived from a real-world coal mine surveillance application validate the effectiveness and efficiency of our approach.

Database queries, in particular, event-driven continuous queries, are useful for many pervasive computing applications, such as video surveillance. In order to enable these applications, we have developed a pervasive query processing framework called Aorta. Unlike traditional database systems, a pervasive query processor requires systems support for managing a large number of networked, heterogeneous devices. In this paper, we present the communication, synchronization, and scheduling mechanisms in Aorta. Even though these techniques have their roots in distributed and parallel systems, we show how these techniques are customized and applied for pervasive query processing. In essence, communication between heterogeneous devices enables network data independence, synchronization on devices protects action atomicity, and scheduling works for adaptive, cost-based multi-query optimization. We have conducted empirical studies on our prototype as well as simulation studies to evaluate the system performance.

In this paper, we demonstrate advanced proxy-caching techniques for function-embedded queries. We term our proxy a function proxy. It does not only service a function-embedded query that is an exact match to a previous one, but also services a query that can be answered by processing results of previous queries. We focus on table-valued functions (which return a set of tuples) as opposed to scalar functions (which return a scalar value), because the former brings additional challenges as well as opportunities for our function proxy.