1194 results about "Data cluster" patented technology

An indexing system and linking method for an assembly of electronic-based informational items stored in and ordered by a plurality of records uniquely identified over a plurality of relational data tables making up a data set wherein each table is assigned a unique domain of unique alphanumeric indicia for assignment and storage in the records. The records are ordered by the alphanumeric indicia whereby each data table acts as its own primary key. The records are bi-directionally linked to each other via a plurality of separate, central linking table indexes wherein each index record is structured with a plurality of linking fields defined to store sets of two or more alphanumeric indicia belonging to a plurality of records in the data set. Each record is further structured to include an internal set of unique indicum for each data field whereby the combination of the unique record indicum and the unique field indicum uniquely identify each record-field over the plurality of relational data tables making up the data set such that each record field may be linked to a plurality of other record-fields or records via the plurality of separate, central linking table indexes. Each central linking table indexes is further generalized to provide multiple arrays of linking indicia in a plurality of indexing fields such that a plurality of the records from a plurality of the data tables may be linked together as a data cluster. The linking values stored in the central linking table indexes may be predefined to automatically structure and link a plurality of unspecified data and changes in the data may alter the linking structure to provide further capabilities.

A data center has several dis-aggregated data clusters that connect to the Internet through a firewall and load-balancer. Each dis-aggregated data cluster has several dis-aggregated compute / switch / disk chassis that are connected together by a mesh of Ethernet links. Each dis-aggregated compute / switch / disk chassis has many processing nodes, disk nodes, and I / O nodes on node cards that are inserted into the chassis. These node cards are connected together by a direct interconnect fabric. Using the direct interconnect fabric, remote I / O and disk nodes appear to the operating system to be located on the local processor's own peripheral bus. A virtual Ethernet controller and a virtual generic peripheral act as virtual endpoints for the local processor's peripheral bus. I / O and disk node peripherals are virtualized by hardware without software drivers. Rack and aggregation Ethernet switches are eliminated using the direct interconnect fabric, which provides a flatter, dis-aggregated hierarchy.

A method and system for monitoring and maintaining the consistency of replicated databases in a shared-nothing database cluster architecture is presented. The method involves the continuous monitoring of the database transaction logs that are maintained by the autonomous database managers that manage the individual database replicas in the cluster. In the event that data discrepancies are detected via the comparison of the transaction logs of the individual database replicas, remedial action is carried out according to configured rules in the main cluster controller. These remedial actions may include, but are not limited to, making a database node inactive within the cluster, or automatically repairing any data discrepancies that are detected among the database replicas in the cluster.

A system and to prevent data corruption due to split brain in shared data clusters includes two or more nodes of a cluster, a shared storage device, and an update manager. The update manager may be configured to maintain a local persistent store corresponding to each node of the cluster. On receiving an update request directed to the shared storage device from a first node, the update manager may be configured to redirect the update to the local persistent store corresponding to the first node. The update manager may be further configured to verify a cluster membership status of the first node, and to transfer the contents of the update from the local persistent store to the shared storage device if the cluster membership verification succeeds.

The present invention improves upon existing systems and methods by providing a passive profile creation method. The data accessible to a financial processor, such as spend level data, is leveraged using sophisticated data clustering and / or data appending techniques. Associations are established among entities (e.g., consumers), among merchants, and between entities and merchants. In one embodiment, a system and method for passively collecting spend level data for a transaction of a first entity, aggregating the collected spend level data for a plurality of entities; and clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity is provided.

The present invention improves upon existing systems and methods by providing a passive profile creation method. The data accessible to a financial processor, such as spend level data, is leveraged using sophisticated data clustering and / or data appending techniques. Associations are established among entities (e.g., consumers), among merchants, and between entities and merchants. In one embodiment, a system and method for passively collecting spend level data for a transaction of a first entity, aggregating the collected spend level data for a plurality of entities; and clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity is provided.

Multidimensional clustered tables are provided for efficient processing and management in a relational database management system. A multidimensional clustered table is one whose data is simultaneously clustered along one or more independent dimensions, or clustering keys, and physically organized into blocks or pages on disk. When such a table is created, one can specify one or more keys as dimensions along which to cluster the table's data. Each of the dimensions can consist of one or more columns. Further provided are efficient query processing and maintenance techniques for use in conjunction with multidimensional clustered tables.

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.

Techniques are disclosed for generating a collection of clusters of related data from a seed. Doing so may generally include retrieving a seed and adding the seed to a first cluster and include retrieving a cluster strategy referencing one or more data bindings. Each data binding specifies a search protocol for retrieving data. For each of the one or more data bindings, data parameters input to the search protocol are identified, the search protocol is performed using the identified data parameters, and data returned by the search protocol is evaluated for inclusion in the first cluster.

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.

At least one image of the environment is taken using either an image capturing device of the monitoring system. Based on the image of the environment, at least one background image is generated. The background image, which comprises a plurality of pixels, is divided into pixel blocks. In each pixel block, at least one data cluster is formed using at least one feature of the pixels in the pixel block. The data cluster formed in each pixel block is described as a data distribution having a mean value and a standard deviation from the mean value. After generating the background image, a subsequent image is taken by the monitoring system. Each pixel of the subsequent image is compared with the data cluster of the block of the background image correspond to the pixel, and a first discrepancy value is generated accordingly. The pixel of the subsequent image is further compared with the data distribution of at least another pixel block which is adjacent to the pixel block of the background image corresponding to the pixel, and a second discrepancy value is generated as a result of this comparison. Based on the first and second discrepancy value, the pixel of the subsequent image is determined to be either a background pixel or a foreground pixel. After all the pixels in the subsequent image have been determined as either a background or a foreground pixel, a binary map is generated. The connected foreground pixels in the binary map are marked to form a foreground object, which is the detected objected in the environment according to the invention.

Visualization techniques are provided for a clustered multidimensional dataset. A data set is visualized by obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein the entities are instances of a particular concept and wherein each entity comprises a plurality of features; and generating an icon for at least one of the entities, the icon having a plurality of regions, wherein each region corresponds to one of the features of the at least one entity, and wherein a size of each region is based on a value of the corresponding feature. Each icon can convey statistical measures. A stabilized Voronoi-based icon layout algorithm is optionally employed. Icons can be embedded in a visualization of the multidimensional dataset. A hierarchical encoding scheme can be employed to encode a data cluster into the icon, such as a hierarchy of cluster, feature type and entity.

Techniques are disclosed for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

Disclosed herein are systems and methods for supplementing content-based attributes with collaborative rating attributes for recommending or filtering items. Collaborative rating data may be consolidated into “composite critics” which serve as item quality rating attributes. These attributes may be used in conjunction with content-based attributes to generate user preference models. Composite critics may be formed using data clustering methods such that users with similar tastes may be grouped together. The user preference models may be induced using machine learning processes, such as decision trees, artificial neural networks, support vector machines, and / or statistical techniques. In some embodiments, composite critics may represent a small number of users or professional critics selected for having differing sensibilities and who rate most or all items according to those sensibilities.

Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.

Improved efficiencies of data mining clustering techniques are provided by preprocessing a sample set of data points taken from a complete data set to provide seeds for centroid calculations of the complete data set. Such seeds are generated by selecting a uniform sample set of data points from a set of multi-dimensional data and then seed values for the cluster determination calculation are determined using a centroid analysis on the sample set of data points. The number of seeds calculated corresponds to a number of data clusters expected in the set of multi-dimensional data points. Seed values are determined using subsample elimination techniques.

Improved efficiencies of data mining clustering techniques are provided by preprocessing a sample set of data points taken from a complete data set to provide seeds for centroid calculations of the complete data set. Such seeds are generated by selecting a uniform sample set of data points from a set of multi-dimensional data and then seed values for the cluster determination calculation are determined using a centroid analysis on the sample set of data points. The number of seeds calculated corresponds to a number of data clusters expected in the set of multi-dimensional data points. Seed values are determined using subsample elimination techniques.

Visualization techniques are provided for a clustered multidimensional dataset. A data set is visualized by obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein the entities are instances of a particular concept and wherein each entity comprises a plurality of features; and generating an icon for at least one of the entities, the icon having a plurality of regions, wherein each region corresponds to one of the features of the at least one entity, and wherein a size of each region is based on a value of the corresponding feature. Each icon can convey statistical measures. A stabilized Voronoi-based icon layout algorithm is optionally employed. Icons can be embedded in a visualization of the multidimensional dataset. A hierarchical encoding scheme can be employed to encode a data cluster into the icon, such as a hierarchy of cluster, feature type and entity.

The invention discloses a data cluster management system based on a quasi-realtime platform, which comprise a storage carrier layer and a cluster database access middleware. The storage carrier layer is a cluster database composed of a plurality of child nodes and each child node is a physical database server. The clustering database access middleware comprises a cluster management module, a communication management module, an execution engine module and a connection management module. By combining a plurality of real-time databases and relational databases to form the cluster database, uniform access interfaces are provided for external users, transparent access interfaces to measuring point positions are realized, uniform and transparent data access service is provided for the external users, centralized storage and share of massive data of the quasi-realtime platform are satisfied.

The embodiment of the invention discloses a method, a system and a device for estimating time difference of arrival TDOA. The method for estimating the TDOA comprises the following steps that: time of arrival of data clustering estimated by a positioning reference point is received, and the time of arrival of the data clustering is detected at regular time by the positioning reference point according to a system precursor symbol, and is acquired by carrying out timing error estimation on an orthogonal frequency division multiplexing OFDM symbol containing a service LBS sequence based on positioning; and the TDOA is calculated according to the time of arrival of the data clustering estimated by different positioning reference points. Through the embodiment of the invention, the positioning reference point estimates the time of arrival of the data clustering, and a base station calculates the TDOA according to the time of arrival of the data clustering estimated by different positioning reference points so as to acquire specific position coordinates of a mobile station, thereby solving the problems of low accuracy and high complexity of the prior positioning algorithm.

The invention provides a method for detecting intrusion by multiple neuronic network confusion, which comprises the following steps: data from external network is subject to packet sniffing and analyzing; the processed network data is simultaneously transmitted to an analytical database and a SGNG exception detection classifier, and the SGNG exception detection classifier is trained offline by normal categorical data acquired by a close network; the SGNG exception detection classifier identifies the detected exceptional data, carries out system alarm and stores the exceptional data into the analytical database; a data set which is identified as exception in the analytical database is provided for a PCSOM exception cluster analyzer for exceptional data cluster analysis; the exceptional data detected by the SGNG exception detection classifier is input to a plurality of parallel PCANN misuse detector respectively according to classifications; the PCANN misuse detector carries out concrete intrusion classification alarm on the detected exceptional data, simultaneously, all the exceptional data filtered by the PCANN misuse detector is identified and stored in the analytical database.

Methods and devices to detect analyte in body fluid are provided. Embodiments include processing sampled data from analyte sensor, determining a single, fixed, normal sensitivity value associated with the analyte sensor, estimating a windowed offset value associated with the analyte sensor for each available sampled data cluster, computing a time varying offset based on the estimated windowed offset value, and applying the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.

A method and apparatus for mapping concepts and attributes to distance fields via rvachev-functions. The steps including generating, for a plurality of objects, equations representing boundaries of attributes for each respective object, converting, for a plurality of objects, the equations into greater than or equal to zero type inequalities, generating, for a plurality of objects, a logical expression combining regions of space defined by the inequalities into a semantic entity, and substituting, for a plurality of objects, the logical expression with a corresponding rvachev-function such that the resulting rvachev-function is equal to 0 on a boundary of the semantic entity, greater then 0 inside a region of the semantic entity, and less then 0 outside the region of the semantic entity. Also included is the step of generating a composite rvachev-function representing logical statements corresponding to the plurality of objects using the respective rvachev-functions of the objects.

The present invention improves upon existing systems and methods by providing a passive profile creation method. The data accessible to a financial processor, such as spend level data, is leveraged using sophisticated data clustering and / or data appending techniques. Associations are established among entities (e.g., consumers), among merchants, and between entities and merchants. In one embodiment, a system and method for passively collecting spend level data for a transaction of a first entity, aggregating the collected spend level data for a plurality of entities; and clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity is provided.

A system and associated method for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

The invention relates to a data mining-based plate shape control key process parameter optimization system, and belongs to the technical field of automatic control of cold continuous rolling plate shape. The data mining method is adopted for acquiring key process parameter optimization settings which can meet the good cold continuous rolling plate shape. The system is characterized by comprising the following functional modules: an actual data acquisition and storage module, a process data pre-processing module, a process data storage module, a process data correlation analysis module, a process data clustering analysis module, a process data association rule analysis module, an optimization result generating module and an optimization result application module. The system has the advantage that the good plate shape can be obtained by applying the plate shape control system. The method avoids endless theoretical research on plate shape control and fully utilizes actual plate shape control process data containing successful operation experience of field operators, and the plate shape process parameter settings which can obtain the good plate shape are acquired by data mining, so the plate shape qualification rate and the finished product rate of cold continuous rolled strip steel are improved.

The present invention improves upon existing systems and methods by providing a passive profile creation method. The data accessible to a financial processor, such as spend level data, is leveraged using sophisticated data clustering and / or data appending techniques. Associations are established among entities (e.g., consumers), among merchants, and between entities and merchants. In one embodiment, a system and method for passively collecting spend level data for a transaction of a first entity, aggregating the collected spend level data for a plurality of entities; and clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity is provided.