Web sources often provide different and even conflicting in-formation. Simple voting-based strategies have already shown limitations at identifying the correct answer to a user query with the presence of unreliable sources. In order to identify the correct answer, corroboration techniques have been pro-posed and proved to be effective for such tasks. In this paper, we investigate the corroboration problem in which most or all facts have only affirmative statements from sources. A fact is either true or false, and an affirmative statement from a source indicates its support for a fact being true. Unfor-tunately, state-of-the-art corroboration techniques rely on conflicting information to differentiate the trustworthiness of the sources and we demonstrate their limitations in our scenario. Different from existing techniques that consider a single trust score for each source, we propose a novel algo-rithm that utilizes a multi-value trust score toward different subsets of facts. By considering the information entropy of the unknown facts, our algorithm incrementally evaluates facts and updates the estimates on the trust scores for the sources. We conduct experiments using both synthetic and real-world datasets and demonstrate that our algorithm sig-nificantly outperforms existing approaches in precision and accuracy. 1.

We consider the problem of efficiently finding the top- k answers for join queries over web-accessible databases. Classical algorithms for finding top- k answers use branch-and-bound techniques to avoid computing scores of all candidates in identifying the top- k answers. To be able to apply such techniques, it is critical to efficiently compute (lower and upper) bounds and expected scores of candidate answers in an incremental fashion during the evaluation. In this paper, we describe novel techniques for these problems. The first contribution of this paper is a method to efficiently compute bounds for the score of a query result when tuples in tables from the "FROM" clause are discovered incrementally, through either sorted or random access. Our second contribution is an algorithm that, given a set of partially evaluated candidate answers, determines a good order in which to access the tables to minimize wasted efforts in the computation of top- k answers. We evaluate our algorithms on a variety of queries and data sets and demonstrate the significant benefits they provide.

Top-k monitoring is important to many wireless sensor applications. This paper exploits the semantics of top-k query and proposes an energy-efficient monitoring approach called FILA. The basic idea is to install a filter at each sensor node to suppress unnecessary sensor updates. Filter setting and query reevaluation upon updates are two fundamental issues to the correctness and efficiency of the FILA approach. We develop a query reevaluation algorithm that is capable of handling concurrent sensor updates. In particular, we present optimization techniques to reduce the probing cost. We design a skewed filter setting scheme, which aims to balance energy consumption and prolong network lifetime. Moreover, two filter update strategies, namely, eager and lazy, are proposed to favor different application scenarios. We also extend the algorithms to several variants of top-k query, that is, order-insensitive, approximate, and value monitoring. The performance of the proposed FILA approach is extensively evaluated using real data traces. The results show that FILA substantially outperforms the existing TAG-based approach and range caching approach in terms of both network lifetime and energy consumption under various network configurations.

Top-k monitoring is important to many wireless sensor applications. This paper exploits the semantics of top-k query and proposes a novel energy-efficient monitoring approach, called FILA. The basic idea is to install a filter at each sensor node to suppress unnecessary sensor updates. The correctness of the top-k result is ensured if all sensor nodes perform updates according to their filters. We show via simulation that FILA outperforms the existing TAGbased approach by an order of magnitude.

A lot of research efforts have been devoted to improving energy efficiency for wireless sensor networks by exploring distributed data storage and in-network query processing techniques. In this paper, we present a generic two-tier data storage strategy for answering precision-constrained approximate queries in a sensor network. The basic idea is to keep two versions of data in the network. A highprecision version is kept at the sensor node that captures the data while a low-precision version is maintained at the base station. We develop query processing and node refreshment strategies for various types of approximate queries under the two-tier storage. Our extensive experiments show that the two-tier storage strategy outperforms the basic centralized storage scheme by an order of magnitude in terms of network lifetime under various system configur