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    GECCO
    2005
    Springer
    141views Optimization» more  GECCO 2005»
    Adaptive isolation model using data clustering for multimodal function optimization
    16 years 2 days ago
    Adaptive isolation model using data clustering for multimodal function optimization
    Download  www.cs.bham.ac.uk
    Shin Ando, Jun Sakuma, Shigenobu Kobayashi
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    CLUSTER
    2010
    IEEE
    98views Distributed And Parallel Com...» more  CLUSTER 2010»
    Service-oriented execution model supporting data sharing and adaptive query processing
    14 years 10 months ago
    Service-oriented execution model supporting data sharing and adaptive query processing
    Download  grid.tsinghua.edu.cn
    Yongwei Wu, Jia Liu, Gang Chen, Qiming Fang, Guang...
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    ICDE
    1999
    IEEE
    113views Database» more  ICDE 1999»
    Parallel Algorithms for Computing Temporal Aggregates
    16 years 8 months ago
    Parallel Algorithms for Computing Temporal Aggregates
    Download  www.cs.arizona.edu
    The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and response time requirements has outpaced advan...
    Jose Alvin G. Gendrano, Bruce C. Huang, Jim M. Rod...
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    PKDD
    2007
    Springer
    113views Data Mining» more  PKDD 2007»
    Statistical Model for Rough Set Approach to Multicriteria Classification
    16 years 23 days ago
    Statistical Model for Rough Set Approach to Multicriteria Classification
    Download  www.cs.put.poznan.pl
    Krzysztof Dembczynski, Salvatore Greco, Wojciech K...
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    ICCSA
    2007
    Springer
    126views Applied Computing» more  ICCSA 2007»
    Provably Secure Framework for Information Aggregation in Sensor Networks
    16 years 24 days ago
    Provably Secure Framework for Information Aggregation in Sensor Networks
    Download  www.ist-ubisecsens.org
    Information aggregation is an important operation in wireless sensor networks executed for the purpose of monitoring and reporting of the environmental data. Due to the performance...
    Mark Manulis, Jörg Schwenk
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