Interesting Problems in Semantic Integration and Interoperability

We report on the issues discussed at the breakout session held at the Dagstuhl Seminar on Semantic Interoperability and Integration on September 23, 2004

Declarative Data Analytics: a Survey

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.Comment: 36 pages, 2 figure

XML Tuple Algebra

XML data management requires an algebraic approach. In this work, we describe a unified algebraic approach for modeling computations described by XML queries expressed in an specific, expressive XQuery subset

Another Outlier Bites the Dust: Computing Meaningful Aggregates in Sensor Networks

Abstract — Recent work has demonstrated that readings pro-vided 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 straightfor-ward 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. I

Outlier-Aware Data Aggregation in Sensor Networks

Abstract- In this paper we discuss a robust aggregation framework that can detect spurious measurements and refrain from incorporating them in the computed aggregate values. Our framework can consider different definitions of an outlier node, based on a specified minimum support. Our experimental evaluation demonstrates the benefits of our approach. I

Describing and using query capabilities of heterogeneous sources

Information integration systems have to cope with the di erent and limited query interfaces of the underlying information sources. First, the integration systems need descriptions of the query capabilities of each source, i.e., the set of queries supported by each source. Second, the integration systems need algorithms for deciding how a query can be answered given the capabilities of the sources. Third, they need to translate a query into the format that the source understands. We present two languages suitable for descriptions of query capabilities of sources and compare their expressive power. We also describe algorithms for deciding whether a query \matches " the description and show their application to the problem of translating user queries into source-speci c queries and commands. Finally, we propose new improved algorithms for the problem of answering queries using these descriptions.