@inproceedings{DBLP:conf/amw/MeliouGH10,
    Abstract = {In this work we present in-network techniques to improve the
    efficiency of spatial aggregate queries. Such queries are very common in a
    sensornet setting, demanding more targeted techniques for their handling.
    Our approach constructs and maintains multi-resolution cube hierarchies
    inside the network, which can be constructed in a distributed fashion. In
    case of failures, recovery can also be performed with in-network
    decisions. In this paper we demonstrate how in-network cube hierarchies
    can be used to summarize sensor data, and how they can be exploited to
    improve the efficiency of spatial aggregate queries. We show that query
    plans over our cube summaries can be computed in polynomial time, and we
    present a PTIME algorithm that selects the minimum number of data requests
    that can compute the answer to a spatial query. We further extend our
    algorithm to handle optimization over multiple queries, which can also be
    done in polynomial time. We discuss enriching cube hierarchies with extra
    summary information, and present an algorithm for distributed cube
    construction. Finally we investigate node and area failures, and
    algorithms to recover query results.},
    Author = {Alexandra Meliou and Carlos Guestrin and Joseph M. Hellerstein},
    Booktitle = {Proceedings of the 4th Alberto Mendelzon International Workshop on Foundations of Data Management (AMW)},
    Title = {\href{http://arxiv.org/abs/1007.3781}{Multiresolution Cube Estimators for Sensor Network Aggregate Queries}},
    Venue = {AMW},
    address = {Buenos Aires, Argentina},
    month = may,
    Year = {2010}
}