We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs

Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e

. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues

the data is aggregated and stored for offline querying and analysis. This approach has two major drawbacks. First, the user cannot change the behavior of the system on the fly. Second, conservation of battery power is a major design factor, but a central system cannot make use of in-network programming

Hardware for sensor nodes that combine physical sensors, actuators, embedded processors, and communication components has advanced significantly over the last decade, and made the large-scale deployment of such sensors a reality. Applications range from monitoring applications such as inventory maintenance over health care to military applications.

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs

RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.

of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring

proposing new semistructured data models and query languages for this purpose, this paper explores the more conservative approach of using traditional relational database engines for processing XML documents conforming to Document Type Descriptors (DTDs). To this end, we have developed algorithms

Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead