Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized order statistics technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated decimal fraction hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum arithmetic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data by exploiting an underlying relationship between LogLog counting estimation techniques and order statistics-based estimation techniques. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained by a computing device. A compound data object is populated by the computing device with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed utilizing a processor with a full-spectrum unified estimation operation that can accurately determine a cardinality estimate for the obtained multiset, utilizing considerably less resources when compared to traditional and state of the art techniques.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized LogLog counting technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum harmonic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized LogLog counting technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum harmonic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data by exploiting an underlying relationship between LogLog counting estimation techniques and order statistics-based estimation techniques. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained by a computing device. A compound data object is populated by the computing device with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed utilizing a processor with a full-spectrum unified estimation operation that can accurately determine a cardinality estimate for the obtained multiset, utilizing considerably less resources when compared to traditional and state of the art techniques.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized order statistics technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated decimal fraction hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum arithmetic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Content may be categorized by accessing a URL associated with the content, determining a set of n-grams contained in the URL, and determining a category of the content based on the set of n-grams.