International Business Machines Corporation

Jan Vondrak

Mir Hamid Pirahesh

Peter Haas

Fatma Ozcan

Mohamed Eltabakh

John (Yannis) Sismanis

Vitaly Feldman

Embodiments of the present invention relate to elimination of blocks such as splits in distributed processing systems such as MapReduce systems using the Hadoop Distributed Filing System (HDFS). In one embodiment, a method of and computer program product for optimizing queries in distributed processing systems are provided. A query is received. The query includes at least one predicate. The query refers to data. The data includes a plurality of records. Each record comprises a plurality of values in a plurality of attributes. Each record is located in at least one of a plurality of blocks of a distributed file system. Each block has a unique identifier. For each block of the distributed file system, at least one value cluster is determined for an attribute of the plurality of attributes. Each value cluster has a range. The predicate of the query is compared with the at least one value cluster of each block. The query is executed against only those blocks where the predicate is met by at least one value cluster.

Split elimination in mapreduce systems

Split elimination in MapReduce systems

A computing device determines one or more first degree correlations based on valuation information. The computing device determines one or more important variables based on at least a comparison between the absolute value of the one or more first degree correlations to a first threshold value. The computing device determines a valuation function based on at least one or more of the determined one or more important variables.

Machine learning customer valuation functions

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Stanford, CA, United States of America

Jan Vondrak