Abstract: Disclosed herein are system, method, and computer program product embodiments for summarizing large histograms. In an embodiment, a client device may not have access to a full dataset stored in a secure system due to privacy or confidentiality restrictions. The secure system, however, may grant the client device access to a histogram related to the dataset as confidentiality may be maintained. Using this histogram, the client device may summarize the dataset to more efficiently utilize memory resources and/or more quickly execute queries. In an embodiment, the client device summarizes the original histogram into a form having fewer buckets than the original histogram. The client device also calculates new bucket boundaries using pairwise comparison and/or maxdiff algorithms.

Abstract: A method and system to determine a q, ?-optimal histogram comprising a plurality of buckets over a data distribution where for any cardinality estimate made using the histogram the cardinality estimate is constrained to obey an acceptability criteria parameterized by q and ? that bounds a ratio error between the cardinality estimate and a true value of the cardinality, q being a factor by which the estimate deviates, at most, from a true value of the cardinality and ? being a threshold value which the cardinality does not exceed, wherein a maximum number of possible query intervals generated in determining the acceptability of the q, ?-optimal histogram is less than quadratic in the number of values.

Abstract: A method and system to determine a q, ?-optimal histogram comprising a plurality of buckets over a data distribution where for any cardinality estimate made using the histogram the cardinality estimate is constrained to obey an acceptability criteria parameterized by q and ? that bounds a ratio error between the cardinality estimate and a true value of the cardinality, q being a factor by which the estimate deviates, at most, from a true value of the cardinality and ? being a threshold value which the cardinality does not exceed, wherein a maximum number of possible query intervals generated in determining the acceptability of the q, ?-optimal histogram is less than quadratic in the number of values.

Abstract: A query plan predictor receives a query instance according to a query template having a set of parameters. The query plan predictor determines a plan space point for the query instance which is based upon the set of parameters. The query plan predictor predicts a predicted query plan for the plan space point, which is selected from a map of query plans according to a confidence threshold from a plan cache that includes at least one histogram. Each histogram is representative of a cached query plan in the plan cache combined with a previously-selected locality-sensitive hashing function. The histogram stores a frequency distribution of a mapping of sample plan points for the cached query plan by the locality-sensitive hashing function.

Abstract: A query plan predictor receives a query instance according to a query template having a set of parameters. The query plan predictor determines a plan space point for the query instance which is based upon the set of parameters. The query plan predictor predicts a predicted query plan for the plan space point, which is selected from a map of query plans according to a confidence threshold from a plan cache that includes at least one histogram. Each histogram is representative of a cached query plan in the plan cache combined with a previously-selected locality-sensitive hashing function. The histogram stores a frequency distribution of a mapping of sample plan points for the cached query plan by the locality-sensitive hashing function.