1187 results about "Data cluster" patented technology

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 system, method, and software product provide for unsupervised identification of complex, nonlinear subspaces in high dimensional data. The system includes a vector quantization module, a weighted topology representing graph module, and an encoding module. The vector quantization module takes vector data inputs and extracts a group of inputs about a number of cluster centers, using a globally optimized clustering process. The weighted topology representing graph module creates a weighted graph of the vector space, using the cluster centers as nodes, weighting edges between nodes as a function of the density of the vectors between the linked nodes. The encoding module uses the weighted graph to recode the input vectors based on their proximity to the cluster centers and the connectedness of the graph. The recoded vectors are reinput into the vector quantization module, and the process repeated until termination, for example at a limited number of cluster centers. Upon termination, the clusters thus identified may be highly nonlinear in the original data space.

Techniques for creating training sets for predictive modeling are provided. In one aspect, a method for generating training data from an unlabeled data set is provided which includes the following steps. A small initial set of data is selected from the unlabeled data set. Labels are acquired for the initial set of data selected from the unlabeled data set resulting in labeled data. The data in the unlabeled data set is clustered using a semi-supervised clustering process along with the labeled data to produce data clusters. Data samples are chosen from each of the clusters to use as the training data. The selecting, presenting, clustering and choosing steps are repeated with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.

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.

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.

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.

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.

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.