Abstract-Recent work has demonstrated that readings provided by commodity sensor nodes are often of poor quality. In order to provide a valuable sensory infrastructure for monitoring applications, we first need to devise techniques that can withstand "dirty" and unreliable data during query processing. In this paper we present a novel aggregation framework that detects suspicious measurements by outlier nodes and refrains from incorporating such measurements in the computed aggregate values. We consider different definitions of an outlier node, based on the notion of a user-specified minimum support, and discuss techniques for properly routing messages in the network in order to reduce the bandwidth consumption and the energy drain during the query evaluation. In our experiments using real and synthetic traces we demonstrate that: (i) a straightforward evaluation of a user aggregate query leads to practically meaningless results due to the existence of outliers; (ii) our techniques can detect and eliminate spurious readings without any application specific knowledge of what constitutes normal behavior; (iii) the identification of outliers, when performed inside the network, significantly reduces bandwidth and energy drain compared to alternative methods that centrally collect and analyze all sensory data; and (iv) we can significantly reduce the cost of the aggregation process by utilizing simple statistics on outlier nodes and reorganizing accordingly the collection tree.

We address the problem of query rewriting for TSL, a language for querying semistructured data. We develop and present an algorithm that, given a semistructured query q and a set of semistructured views V , finds rewriting queries, i.e., queries that access the views and produce the same result as q . Our algorithm is based on appropriately generalizing containment mappings , the chase , and query composition — techniques that were developed for structured, relational data. We also develop an algorithm for equivalence checking of TSL queries. We show that the algorithm is sound and complete for TSL, i.e., it always finds every non-trivial TSL rewriting query of q , and we discuss its complexity. We extend the rewriting algorithm to use some forms of structural constraints (such as DTDs) and find more opportunities for query rewriting.

tsimmis 1 OverviewIn order to access information from a variety of heterogeneous information sources, one has to be able to translate queries and data from one data model into another.This functionality is provided by so-called (source) wrappers [4,8] which convert queries into one or more commands/queries understandable by the underlying source and transform the native results into a format understood by the application. As part of the TSIMMISproject [1,6] we have developed hard-coded wrappers for a variety of sources (e.g., Sybase DBMS, W WW pages, etc.) including legacy systems (Folio). However, anyone who has built a wrapper before can attest that a lot of effort goos into developing and writing such a wrapper. In situations where it is important or desirable to gain access to new sources quicldy, this is a major drawback. Furthermore, we have also observed that only a relatively small part of the code deals with the specific access details of the source. The rest of the code is either common among wrappers or implements query and data transformation that could be expressed in a high level, declarative fashion.Based on these observations, we have developed a wrapper implementation toolkit [7] for quickly building wrappers. The toolkit contains a library for commonly used functions, such as for receiving queries from the application and packaging results. It also ' Permission to make digitellhard copy of part or all this work for personal or clacsroom use is granted without fee provided that contains a facility for translating queries into sourcespecific commands, and for translating results into a model useful to the application.The philosophy behind our "template-baaed" translation methodology is as follows. The wrapper implementor specifies a set of templates (rules) written in a high level declarative language that describe the queries accepted by the wrapper as well as the objects that it returns. If an application query matches a template, an implementorprovided action associated with the template is executed to rovide the native query for the underly-F ing source . When the source returns the result of the query, the wrapper transforms the answer which is represented in the data model of the source into a representation that is used by the application. Using this toolkit one can quicldy design a simple wrapper with a few templates that cover some of the desired functionality, probably the one that is most urgently needed. However, templates can be added gradually as more functionality is required later on.Another important use of wrappers is in extending the query capabilities of a source. For instance, some sources may not be capable of answering queries that have multiple predicates. In such cases, it is necessary to pose a native query to such a source using only predicates that the source is capable of handling. The rest of the predicates are automatically separated from the user query and form a jilter query.When the wrapper receives the results, a poet-processing engine applies the filter query, ...

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