1192 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 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.

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.

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.

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.

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 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.

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.