890 results about "Clustered data" patented technology

A computer system for generating data structures for information retrieval of documents stored in a database. The computer system includes: a neighborhood patch generation system for defining patch of nodes having predetermined similarities in a hierarchy structure. The neighborhood patch generation subsystem includes a hierarchy generation subsystem for generating a hierarchy structure upon the document-keyword vectors and a patch definition subsystem. The computer system also comprises a cluster estimation subsystem for generating cluster data of the document-keyword vectors using the similarities of patches.

A text mining system for collecting business intelligence about a client, as well as for identifying prospective customers of the client, for use in a lead generation system accessible by the client via the Internet. The text mining system has various components, including a data acquisition process that extracts textual data from Internet web sites, including their logs, content, processes, and transactions. The system compares log data to content and process data, and relates the results of the comparison to transaction data. This permits the system to provide aggregate cluster data representing statistics useful for customer lead generation.

A distributed network storage system provides capability to send and receive storage information from multiple network storage servers in a storage area network using iSCSI commands. A storage server system comprising at least two data storage servers stores one or more logical volumes of data. A host computer receives a storage command from a host application, and determines one or more data storage servers has information to complete the storage command. The host computer generates one or more iSCSI network commands to carry out the storage command, and transmits the iSCSI network commands directly to each data storage server having necessary information. The storage servers receive iSCSI network commands, and return a response to the host. The host and storage servers verify the configuration of the storage network and are capable of correcting or updating the configuration as required.

A system and method for identifying properties of virtual resources to efficiently perform backups and restores of cluster data. A cluster of nodes is coupled to a data storage medium. A node receives a request for a backup or a restore of cluster data. In response to this request, the node queries a cluster subsystem and a virtual subsystem of all other cluster nodes for identification of VMs, a subset of corresponding stored data, and an identification of VMs which are highly available (HA). In response to receiving query responses, the node aggregates the results and sends them to a backup server. These aggregated results may then be used to schedule subsequent backup and restore operations. In addition, the node may use the results to complete the current backup or restore operation.

A distributed storage and parallel mining method for state monitoring data includes the steps: defining function service models of a remote substation state monitoring unit and a state monitoring communication front-end processor by means of Web service description language, and exchanging the state monitoring data of electric power equipment in an electric power wide area network environment by a simple object access protocol; storing large-scale state monitoring data redundancy in a distributed file system, creating an index table for a state monitoring data file, inserting the index table into a large-scale structural data table and querying the state monitoring data according to a query request; and generating basic data and multi-dimensional analytical data by extracting, converting and loading to built a data warehouse, and parallelly executing association rules, classification and clustered data mining algorithm by means of MapReduce task decomposition and result summary. The distributed storage and parallel mining method can be used for effectively realizing distributed data exchange, redundant storage and rapid parallel processing for state monitoring information of the mass electric power equipment in an intelligent power network environment.

The invention provides system and method wherein the system collects user activity data including call log information from network equipment, handset and other context specific user activity data including time of call and location information to enable various applications to use the information collected and build social network. In accordance with the method of the invention, the user activity data collected is used to form individual social networks. The networks are formed based on clusters identified by mining the data collected. Furthermore, various applications are provided access to the clustered data to assist in individual social networking. The system of the invention comprises of an application server comprising a centralized data center providing social networking services through a plurality of networks, the networks in-turn connecting a plurality of users through their individual network terminal stations to the application server.

Described herein are systems and methods for normalizing data without the use of external controls. Also described herein are systems and methods for analyzing cluster data, such as genotyping data, using an artificial neural network.

A system and method for analyzing entertainment venue data for improved venue management, comprising conventional computer hardware and including compatible software application for compiling and manipulating event consumer and sales data as well as third party consumer demographic data, including classification of event data into a plurality of event-type clusters, correlation of consumer data and event cluster data, and performing manipulations of said data in response to user queries and displaying query results. In a preferred embodiment, associative query logic database technology is used to create a data cloud of event venue consumer and sales data as well as third-party sourced demographic data.

A system and method for identifying properties of virtual resources to efficiently perform backups and restores of cluster data. A cluster of nodes is coupled to a data storage medium. A node receives a request for a backup or a restore of cluster data. In response to this request, the node queries a cluster subsystem and a virtual subsystem of all other cluster nodes for identification of VMs, a subset of corresponding stored data, and an identification of VMs which are highly available (HA). In response to receiving query responses, the node aggregates the results and sends them to a backup server. These to aggregated results may then be used to schedule subsequent backup and restore operations. In addition, the node may use the results to complete the current backup or restore operation.

Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

Described herein are systems and methods for normalizing data without the use of external controls. Also described herein are systems and methods for analyzing cluster data, such as genotyping data, using an artificial neural network.

A system and method for performance modeling for an information technology (IT) system having a server(s) for performing a number of types of transactions includes receiving data for system topology and transaction flows and receiving performance measurement data for the IT system. The measurement data is clustered into multiple regimes based on similarities. Service demand and network delay parameters may be inferred based on clustered data.

A method and apparatus for clustering data, particularly regarding an image, that constructs a graph in which each node of the graph represents a pixel of the image, and every two nodes represent neighboring pixels associated by a coupling factor. Block pixels are selected with unselected neighboring pixels coupled with a selected block to form aggregates. The graph is coarsened recursively by performing iterated weighted aggregation to form larger blocks (aggregates) and obtain hierarchical decomposition of the image while forming a pyramid structure over the image. Saliency of segments is detected in the pyramid, and by computing recursively, a degree of attachment of every pixel to each of the blocks in the pyramid. The pyramid is scanned from coarse to fine starting at the level a segment is detected, to lower levels and rebuilding the pyramid before continuing to the next higher level. Relaxation sweeps sharpen the boundaries of a segment.

The invention discloses a Hadoop-based mass stream data storage and query method and a Hadoop-based mass stream data storage and query system. The method comprises the following steps of: constructing a segmented column cluster type storage structure; sequentially storing stream data as column cluster records, compressing the column cluster records from front to back to obtain compressed data pages, writing each compressed data page into a piece of column cluster data, and simultaneously additionally writing the page outline information of the compressed data pages into the tail ends of the column cluster data to obtain an integrated data segment; and in the process of executing query statements, constructing a scan table according to filtering restraints by utilizing the page outline information at the tail ends of data segments to quickly filter the data.

A method enables cluster-level backup and restore functionality of all Virtual Input / Output Server (VIOS) configuration data within a VIOS cluster and the data of a shared VIOS cluster database. The method comprises: performing, via a backup / restore utility of a VIOS partition, a cluster level backup, which creates a first VIOS cluster configuration backup file having configuration information about hardware, logical and virtual devices of each VIOS partition within a VIOS cluster and all cluster data from the shared VIOS database of the VIOS cluster; storing the VIOS cluster configuration backup file within a storage location; and responsive to receipt of a VIOS restore command at a VIOS partition: retrieving the configuration backup file from the storage location; restoring a configuration of the hardware, logical and virtual devices of each VIOS within the VIOS cluster to prior state; and restoring the shared VIOS database with the backed-up cluster data.

A system, software module, and computer program product for performing clustering based data mining that improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module for performing clustering based data mining in an electronic data processing system comprises: a model setup block operable to receive client input including information specifying a setup of a clustering data mining models, generate the model setup, and generate parameters for the model setup based on the received information, a modeling algorithms block operable to select and initialize a clustering modeling algorithm based on the generated model setup, a model building block operable to receive training data and build a clustering model using the training data and the selected clustering modeling algorithm and a model scoring block operable to receive scoring data and generate predictions and / or recommendations using the scoring data and the clustering model.

Described herein are systems and methods for normalizing data without the use of external controls. Also described herein are systems and methods for analyzing cluster data, such as genotyping data, using an artificial neural network.

The present invention relates to a method, system and computer program product for clustering data points and its application to text summarization, customer profiling for web personalization and product cataloging. The method for clustering data points with defined quantified relationships between them comprises the steps of obtaining lead value for each data point either by deriving from said quantified relationships or as given input, ranking each data point in a lead value sequence list in descending order of lead value, assigning the first data point in said lead value sequence list as the leader of the first cluster, and considering each subsequent data point in said lead value sequence list as a leader of a new cluster if its relationship with the leaders of each of the previous clusters is less than a defined threshold value or as a member of one or more clusters where its relationship with the cluster leader is more than or equal to said threshold value. The said relationships between data points are symmetric or asymmetric. Similarly, system and computer program product have also been claimed

In a networked computer system that includes clusters, each cluster is provided with a resource database and an agent that scans the systems in that cluster and collects storage resource details along with capacity information from the multiple systems that are members of that cluster. During the scanning process, this information is checked for completeness and integrity and stored in the resource database. Depending on the scan context, individual resources may be marked for reporting. The information in the database is then used to report consistent scan results back to the management or scanner software.

There is provided a method for obtaining at least one calibration filter for a Mass Spectrometry (MS) instrument system. Measured isotope peak cluster data in a mass spectral range is obtained for a given calibration standard. Relative isotope abundances and actual mass locations of isotopes corresponding thereto are calculated for the given calibration standard. Mass spectral target peak shape functions centered within respective mass spectral ranges are specified. Convolution operations are performed between the calculated relative isotope abundances and the mass spectral target peak shape functions to form calculated isotope peak cluster data. A deconvolution operation is peformed between the measured isotope peak cluster data and the calculated isotope peak cluster data after the convolution operations to obtain the at least one calibration filter.

A system for clustering data objects includes a module for calculating an importance value of at least one member in a first data object represented as a variable length vector of 0 to N members and a clustering module for dynamically forming a plurality of clusters containing one or more data objects. The clustering module is configured to associate the first data object with at least one of the plurality of clusters in dependence upon the at least one member's similarity value in comparison to members in other data objects. The clustering module may be configured to cluster the first data object into a plurality of clusters if it has at least two members and each member belongs to a different cluster.

A system, method and computer program product for synchronizing updates to shared mutable data in a clustered data processing system. A data element update operation is performed at each node of the cluster while preserving a pre-update view of the shared mutable data, or an associated operational mode, on behalf of readers that may be utilizing the pre-update view. A request is made for detection of a grace period, and grace period detection processing is performed for detecting when the cluster-wide grace period has occurred. When it does, a deferred action associated with the update operation it taken, such as removal of a pre-update view of the data element or termination of an associated mode of operation.

The invention provides a data backup method of a distributed file system. The method includes: setting up a thread pool by a synchronous control node, distributing source files to each thread according to a copy list, and parallelly conducting metadata synchronization of each source file and the corresponding target file; judging content consistency of each file block in the source files and the target files by each thread of the synchronous control node to analyze difference between each distributed source file and the corresponding target file; judging content consistency of each chunk in the source files and the target files by a source data node to analyze difference between the source file blocks and the target file blocks; duplicating data of the source file blocks to the corresponding target file blocks by a target data node according to the difference analyzing results of the source file blocks and the target file blocks. Data transmission among trans-cluster data nodes can be reduced by effectively using existing data of the target files of a target file system, and data backup execution time is shortened since file backup is completed by taking a file block as a unit.

There is provided a method for obtaining at least one calibration filter for a Mass Spectrometry (MS) instrument system. Measured isotope peak cluster data in a mass spectral range is obtained for a given calibration standard. Relative isotope abundances and actual mass locations of isotopes corresponding thereto are calculated for the given calibration standard. Mass spectral target peak shape functions centered within respective mass spectral ranges are specified. Convolution operations are performed between the calculated relative isotope abundances and the mass spectral target peak shape functions to form calculated isotope peak cluster data. A deconvolution operation is performed between the measured isotope peak cluster data and the calculated isotope peak cluster data after the convolution operations to obtain the at least one calibration filter.

A cluster implements a virtual disk system that provides each node of the cluster access to each storage device of the cluster. The virtual disk system provides high availability such that a storage device may be accessed and data access requests are reliably completed even in the presence of a failure. To ensure consistent mapping and file permission data among the nodes, data are stored in a highly available cluster database. Because the cluster database provides consistent data to the nodes even in the presence of a failure, each node will have consistent mapping and file permission data. A cluster transport interface is provided that establishes links between the nodes and manages the links. Messages received by the cluster transports interface are conveyed to the destination node via one or more links. The configuration of a cluster may be modified during operation. Prior to modifying the configuration, a reconfiguration procedure suspends data access requests and waits for pending data access requests to complete. The reconfiguration is performed and the mapping is modified to reflect the new configuration. The node then updates the internal representation of the mapping and resumes issuing data access requests.

A method and system for optimizing a database query. A database table populated with data is received and scanned. Statistics and single column histograms associated with single columns of the table are determined. Cardinality based on the statistics and histograms is estimated. All possible correlations among multiple columns are determined by performing clustering data mining that partitions data in the table into clusters. Top ranked columns based on the correlations are determined. The difference between the estimated cardinality and a support count of a cluster is determined to exceed a threshold, and in response, multiple column histograms based on the top ranked columns are determined. An optimal query plan based on the multiple column histograms is generated.

The present invention specifies database security at a row level and, optionally, at a column and table level. The systems and methods cluster one or more sets of rows with similar security characteristics and treat them as a named expression, wherein clustered data is accessed based on associated row-level security. The systems and methods specify a syntax that invokes row(s), column(s) and / or table(s) security via programming statements. Such statements include arbitrary Boolean expressions (predicates) defined over, but not restricted to table columns and / or other contextual data. These statements typically are associated with query initiators, incorporated into queries therefrom, and utilized while querying data. Rows of data that return “true” when evaluated against an aggregate of associated security expressions are said to “satisfy” the security expressions and enable access to the data stored therein. Such security expressions can be created and invoked via the Structured Query Language (SQL) database programming language.

A method and apparatus for partitioning a data set for clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. A Potts spin is assigned to each data point and an interaction between neighboring points is introduced, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; i.e. all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters.

A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.