Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A technique includes determining a statistic for values associated with at least one column of a database based at least in part on a first sample of the values; and determining a degree of change in a second sample of the values relative to the first sample; and estimating a count of unique values for the column(s). The estimation of the count of unique values includes selectively incrementally updating the statistic using the second sample based at least in part on the determined degree of change; and basing estimation of the count at least in part on the updated statistic. The technique further includes processing a query to the database based at least in part on the count.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method of managing a database including creating an initial counting bloom filter (CBF) instance having an array of counters and hash functions that map an inserted value to the array of counters, and designating the initial CBF instance as a current CBF instance, and sequentially inserting each value of a sample data set of a table column into the hash functions of the current CBF instance and incrementing counters of the array of counters to which the value is mapped. The method further includes, prior to inserting each value into the hash functions of the current CBF instance, when a number of counters of the array of counters having non-zero values is at least at a threshold level, designating the current CBF instance as an old CBF instance, creating a new CBF instance having an array of counters and hash functions that map an inserted value to the array counters, and designating the new CBF instance as the current CBF instance.

Abstract: Incrementally updating statistics includes sampling rows from a database column in a database to generate a first sample, sampling a subset of modified rows from the database column after generating the first sample to generate a second sample, determining whether distribution changes occurred to the database column based on the first and second samples, and updating a database statistic about the database column in response to determining that a distribution change exists.

Abstract: A technique includes determining a statistic for values associated with at least one column of a database based at least in part on a first sample of the values; and determining a degree of change in a second sample of the values relative to the first sample; and estimating a count of unique values for the column(s). The estimation of the count of unique values includes selectively incrementally updating the statistic using the second sample based at least in part on the determined degree of change; and basing estimation of the count at least in part on the updated statistic. The technique further includes processing a query to the database based at least in part on the count.

Abstract: In a method for generating a query plan for estimating a number of unique entry counts of an attribute in a database, a sample of entries in the database is identified, at least one of a sampling percent and a coefficient of variance of the entries in the sample is identified, and a skewness of the entries in the sample is calculated. In addition, at least one of a plurality of estimators is selected based upon the skewness of the entries and at least one of the sampling percent and the coefficient of variance of the entries in the sample. Moreover, a query plan is generated from the selected at least one of the plurality of estimators. A query optimizer for performing the method is provided.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method of managing a database including creating an initial counting bloom filter (CBF) instance having an array of counters and hash functions that map an inserted value to the array of counters, and designating the initial CBF instance as a current CBF instance, and sequentially inserting each value of a sample data set of a table column into the hash functions of the current CBF instance and incrementing counters of the array of counters to which the value is mapped. The method further includes, prior to inserting each value into the hash functions of the current CBF instance, when a number of counters of the array of counters having non-zero values is at least at a threshold level, designating the current CBF instance as an old CBF instance, creating a new CBF instance having an array of counters and hash functions that map an inserted value to the array counters, and designating the new CBF instance as the current CBF instance.

Abstract: A method of estimating a number of unique entry counts of an attribute in a database comprises, with a processor: identifying a sample of entries from an attribute database, determining frequencies of a number of input observations of the sample of entries, determining a number of high frequency values of the sample of entries, and estimating a number of unique entry counts of an attribute within the attribute database using a counting Bloom filter and based on the frequencies of the input observations and the high frequency values.

Abstract: Incrementally updating statistics includes sampling rows from a database column in a database to generate a first sample, sampling a subset of modified rows from the database column after generating the first sample to generate a second sample, determining whether distribution changes occurred to the database column based on the first and second samples, and updating a database statistic about the database column in response to determining that a distribution change exists.

Abstract: An example method for anomaly detection in streaming data includes applying statistical analysis to streaming data in a sliding window. The method also includes extracting a feature. The method also includes determining class assignment for the feature using class conditional probability densities and a threshold.

Abstract: A method determines a number of unique values in a sample of a list of values and estimates a number of the unique values for an unsampled portion of the list of values. The method estimates a number of the unique values in the list by adding the number of unique values in the sample to the number of the unique values in the unsampled portion.

Abstract: Probable anomalies associated with at least one data metric may be detected across a series of windows of time series data by comparison of data to a threshold. An estimated probability of anomalies for each of the windows of time series data may be determined based on the detected probable anomalies and the threshold. The windows of time series data may be ranked based on the estimated probabilities. Probable anomalies associated with highest ranked windows of time series data may be output to a user.

Abstract: A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities.

Abstract: A technique includes clustering a plurality of electronic patient records (PRs) based on related diagnostic codes into a plurality of clusters, and analyzing one of the plurality of clusters to determine variations in resource usage within the cluster.

Abstract: Sensor data is received from one or more sensors. The sensor data is organized within a hierarchy. The sensor data is organized within a hierarchy that is non-dyadic. A processor of a computing device generates a discrete wavelet transform, based on the sensor data and based on the hierarchy of the sensor data, to compress the sensor data. The sensor data, as has been compressed via generation of the discrete wavelet transform, is processed.

Abstract: A method of estimating a number of unique entry counts of an attribute in a database comprises, with a processor: identifying a sample of entries from an attribute database, determining frequencies of a number of input observations of the sample of entries, determining a number of high frequency values of the sample of entries, and estimating a number of unique entry counts of an attribute within the attribute database using a counting Bloom filter and based on the frequencies of the input observations and the high frequency values.