Abstract: A method for storing database information includes storing a table having data values in a column major order. The data values are stored in a list of blocks. The method also includes assigning a tuple sequence number (TSN) to each data value in each column of the table according to a sequence order in the table. The data values that correspond to each other across a plurality of columns of the table have equivalent TSNs. The method also includes assigning each data value to a partition based on a representation of the data value. The method also includes assigning a tuple map value to each data value. The tuple map value identifies the partition in which each data value is located.

Abstract: A method for storing database information, including: storing a table having data values in a column major order, wherein the data values are stored in a list of blocks, assigning a tuple sequence number (TSN) to each data value in each column of the table according to a sequence order in the table, wherein data values that correspond to each other across a plurality of columns of the table have equivalent TSNs; assigning each data value to a partition based on a representation of the data value; and assigning a tuple map value to each data value, wherein the tuple map value identifies the partition in which each data value is located.

Abstract: Embodiments of the invention relate to a multi-level database compression technique to compress table data objects stored in pages. A compact dictionary structure is encoded that represents frequent values of data at any level of granularity. More than one level of compression is provided, wherein input to a finer level of granularity is an output of a coarser level of granularity. Based upon the encoded dictionary structure, a compression technique is applied to a stored page to compress each row on the page. Similarly, a de-compression technique may be applied to decompress the compressed data, utilizing the same dictionary structures at each level of granularity.

Abstract: A computer program product that includes a computer useable storage medium to store a computer readable program that, when executed on a computer, causes the computer to perform operations, including operations to receive a query for which a query execution plan (QEP) is to be computed, divide a search space into a plurality of subproblems for which constituent QEPs are to be created, partition the plurality of subproblems into a plurality of partitions, and allocate each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture. Possible QEPs describe a search space. Each subproblem references one or more quantifiers and each of the subproblems within a partition references the same number of quantifiers. A partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers.

Abstract: Some aspects of the invention provide methods, systems, and computer program products for inserting an encrypted problem signature into a symptom database. A problem signature is first provided, which is then used to derive a hash value. The problem signature is then encrypted using the hash value. The hash value is further used to indicate the location in the database to insert the problem signature. The problem signature is then inserted at the location in the database indicated by the location.

Abstract: A computer program product that includes a computer useable storage medium to store a computer readable program that, when executed on a computer, causes the computer to perform operations, including operations to receive a query for which a query execution plan (QEP) is to be computed, divide a search space into a plurality of subproblems for which constituent QEPs are to be created, partition the plurality of subproblems into a plurality of partitions, and allocate each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture. Possible QEPs describe a search space. Each subproblem references one or more quantifiers and each of the subproblems within a partition references the same number of quantifiers. A partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers.

Abstract: A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lowe

Abstract: A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lowe

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for clustering data points in a multidimensional dataset in a multidimensional image space. The method comprises generating a multidimensional image from the multidimensional dataset; generating a pyramid of multidimensional images having varying resolution levels by successively performing a pyramidal sub-sampling of the multidimensional image; identifying data clusters at each resolution level of the pyramid by applying a set of perceptual grouping constraints; and determining levels of a clustering hierarchy by identifying each salient bend in a variation curve of a magnitude of identified data clusters as a function of pyramid resolution level.

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for obtaining a shape interpolated representation of shapes of clusters in an image of a clustered dataset. The method comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the associated cluster to form a shape interpolated representation.

Abstract: A method for representing a dataset comprises clustering the dataset using an unsupervised, non-parametric clustering method to generate a set of clusters each comprising a set of data points in an image; clustering the data points of each cluster using a supervised, partitional clustering method to partition each cluster into a specified number of sub-clusters; generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each sub-cluster using a kernel density function; identifying a maximum density estimate value and a sub-cluster associated with the maximum density estimate value for the grid point; adding each grid point for which the maximum density estimate value exceeds a specified threshold to the sub-cluster associated with the maximum density estimate value; and, for each cluster, merging the sub-clusters of the cluster into a corresponding cluster region in the image.

Abstract: A method for representing a dataset comprises clustering the dataset using an unsupervised, non-parametric clustering method to generate a set of clusters each comprising a set of data points in an image; clustering the data points of each cluster using a supervised, partitional clustering method to partition each cluster into a specified number of sub-clusters; generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each sub-cluster using a kernel density function; identifying a maximum density estimate value and a sub-cluster associated with the maximum density estimate value for the grid point; adding each grid point for which the maximum density estimate value exceeds a specified threshold to the sub-cluster associated with the maximum density estimate value; and, for each cluster, merging the sub-clusters of the cluster into a corresponding cluster region in the image.

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for obtaining a shape interpolated representation of shapes of clusters in an image of a clustered dataset. The method comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the associated cluster to form a shape interpolated representation.

Abstract: Exemplary embodiments of the present invention relate to enhanced faceted search support for OLAP queries over unstructured text as well as structured dimensions by the dynamic and automatic discovery of dimensions that are determined to be most “interesting” to a user based upon the data. Within the exemplary embodiments “interestingness” is defined as how surprising a summary along some dimensions is from a user's expectation. Further, multi-attribute facets are determined and a user is optionally permitted to specify the distribution of values that she expects, and/or the distance metric by which actual and expected distributions are to be compared.

Abstract: A method executed on a computer for determining a hierarchical clustering of a multidimensional dataset in a multidimensional image space comprises receiving a pyramid of multidimensional images of the multidimensional dataset in which the images of the pyramid representing a first multidimensional image of the multidimensional dataset at successively lower resolution levels; identifying data clusters at each resolution level of the pyramid by applying a set of perceptual grouping constraints; plotting a variation curve of a magnitude of data clusters identified at each resolution level of the pyramid as a function of resolution level; and generating a clustering hierarchy for the multidimensional dataset by identifying the resolution level at each salient bend in the variation curve as a level of the clustering hierarchy.

Abstract: Exemplary embodiments of the present invention relate to enhanced faceted search support for OLAP queries over unstructured text as well as structured dimensions by the dynamic and automatic discovery of dimensions that are determined to be most “interesting” to a user based upon the data. Within the exemplary embodiments “interestingness” is defined as how surprising a summary along some dimensions is from a user's expectation. Further, multi-attribute facets are determined and a user is optionally permitted to specify the distribution of values that she expects, and/or the distance metric by which actual and expected distributions are to be compared.

Abstract: An cost based optimizer optimizes access to at least a portion of hierarchically-organized documents, such as those formatted using eXtensible Markup Language (XML), by estimating a number of results produced by the access of the hierarchically-organized documents. Estimating the number of results comprises computing the cardinality of each operator executing query language expressions and further computing a sequence size of sequences of hierarchically-organized nodes produced by the query language expressions. Access to the hierarchically-organized documents is optimized using the structure of the query expression and/or path statistics involving the hierarchically-organized data. The cardinality and the sequence size are used to calculate a cost estimation for execution of alternate query execution plans. Based on the cost estimation, an optimal query execution plan is selected from among the alternate query execution plans.

Abstract: A method for obtaining a shape interpolated representation of shapes of one or more clusters in an image of a dataset that has been clustered comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster in the image using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value of each grid point; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the cluster associated with the maximum density estimate value for the grid point to form a shape interpolated representation of the one or more clusters.

Abstract: A method is provided clustering data points in a multidimensional dataset in a multidimensional image space that comprises generating a multidimensional image from the multidimensional dataset; generating a pyramid of multidimensional images having varying resolution levels by successively performing a pyramidal sub-sampling of the multidimensional image; identifying data clusters at each resolution level of the pyramid by applying a set of perceptual grouping constraints; and determining levels of a clustering hierarchy by identifying each salient bend in a variation curve of a magnitude of identified data clusters as a function of pyramid resolution level.

Abstract: Some aspects of the invention provide methods, systems, and computer program products for inserting an encrypted problem signature into a symptom database. A problem signature is first provided, which is then used to derive a hash value. The problem signature is then encrypted using the hash value. The hash value is further used to indicate the location in the database to insert the problem signature. The problem signature is then inserted at the location in the database indicated by the location.