71 results about "Extensive data" patented technology

A data management system or “DMS” provides a wide range of data services to data sources associated with a set of application host servers. The data management system typically comprises one or more regions, with each region having one or more clusters. A given cluster has one or more nodes that share storage. To facilitate the data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver provides a method and apparatus for capturing real-time data transactions in the form of an event journal that is provided to the data management system. The driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. Using the streams generated in this manner, the DMS offers a wide range of data services that include, by way of example only: data protection (and recovery), disaster recovery (data distribution and data replication), data copy, and data query and access.

A technique is provided for enhancing performance of computer-assisted data operating algorithms in a medical context. Datasets are compiled and accessed, which may include data from a wide range of resources, including controllable and prescribable resources, such as imaging systems. The datasets are analyzed by a human expert or medical professional, and the algorithms are modified based upon feedback from the human expert or professional. Modifications may be made to a wide range of algorithms and based upon a wide range of data, such as available from an integrated knowledge base. Modifications may be made in sub-modules of the algorithms providing enhanced functionality. Modifications may also be made on various bases, including patient-specific changes, population-specific changes, feature-specific changes, and so forth.

A data management system or “DMS” provides a wide range of data services to data sources associated with a set of application host servers. The data management system typically comprises one or more regions, with each region having one or more clusters. A given cluster has one or more nodes that share storage. To facilitate the data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver provides a method and apparatus for capturing real-time data modifications and application state notifications and, in response, generating data transactions in the form of an event journal that is provided to the data management system. The driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. Using the streams generated in this manner, the DMS offers a wide range of data services that include, by way of example only: data protection (and recovery), and disaster recovery (data distribution and data replication).

A general-purpose, low-cost system provides comprehensive physiological data collection, with extensive data object oriented programmability and configurability for a variety of medical as well as other analog data collection applications. In a preferred embodiment, programmable input signal acquisition and processing circuits are used so that virtually any analog and/or medical signal can be digitized from a common point of contact to a plurality of sensors. A general-purpose data routing and encapsulation architecture supports input tagging and standardized routing through modern packet switch networks, including the Internet; from one of multiple points of origin or patients, to one or multiple points of data analysis for physician review. The preferred architecture further supports multiple-site data buffering for redundancy and reliability, and real-time data collection, routing, and viewing (or slower than real-time processes when communications infrastructure is slower than the data collection rate). Routing and viewing stations allow for the insertion of automated analysis routines to aid in data encoding, analysis, viewing, and diagnosis.

Consumer, merchant, and transactional data from a closed loop network and external sources may be leveraged to increase demand of a merchant's inventory during normally low-demand periods. Extensive data mining is used to determine the excess merchant inventory and demand patterns at different times and different locations for merchants and groups of merchants. Similar data mining is used to analyze cardmember demand patterns to identify the cardmember preferences regarding when and where they which to purchase goods and/or services. Cardmembers may also be grouped based on their demand patterns. Using pricing as a lever, cardholders with specific preferences are targeted to shift the demand from peak periods and locations to non-peak periods and locations, and to increase the non-peak demand by location as well as time period. Higher precision may be obtained using product level transaction data from point-of-sale terminals used by merchants wherever applicable.

A digital content distribution scheme allows for monitoring of a wide range of data, such as system and behavior events of audiences and playback devices, as well as environmental factors, such as those present at the playback venue. More “static” data may be considered, such as subscriptions, settings, bandwidth, and the like. Based upon the data considered, real or near-real time changes in the content provided to audiences may be made. The changes may be based upon predictions of audience interest made by prediction engine. The techniques may also allow for time-shifting and adaptation of content based upon playback times.

The current invention discloses methods for transforming a set of relations into multidimensional data cubes. A syntheses process is disclosed that dynamically and with minimal user input eliminates ambiguities when populating a data cube by introducing table-like virtual relations. The methods are generic and applicable to many data warehouse designs. The methods support relational OLAP for a wider variety of data and structures than possible using current relational implementation schemas.

The invention provides a road matching method, a road matching apparatus and a vehicle-mounted map acquisition system, and relates to the field of map making. In the prior art, the existing matching accuracy is not high, and the high accuracy requires a lot of data learning. A purpose of the present invention is to solve the problems in the prior art. The road matching method comprises: acquiring the regularly upload data of a positioning system, and converting the data uploaded by the positioning system into the positioning data in a vector map; according to the positioning data at the current moment, determining the current positioning position, and acquiring the road information within a preset range using the current positioning position as the center as a first set; according to the road matching information determined at the previous moment, searching the road information associated with the determined road matching information in a vector map road network as a second set; according to the first set and the second set, acquiring candidate road matching information; and acquiring the weight value corresponding to each candidate road matching information according to a weight algorithm, and acquiring the road matching information at the current moment. With the technical scheme of the present invention, the matching accuracy is increased, and the extensive data learning is not required.

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the b_i variables are scaled by the ratio of the gains K and k. The b_i have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

The present invention relates generally to a method and system for managing a portfolio. More specifically, the present invention includes a variety of means to calculate probabilistic risk/benefit measures of portfolios as well as extensive data visualization tools, several group decision-making tools, powerful optimization and tradeoff analysis tools, and customized simulations of projects and portfolios.

The present invention is a system and method for character recognition using pattern recognition. In one embodiment of the invention, a plurality of representations of a plurality of characters is received by a system. A plurality of cipher labels are assigned to representations in the plurality of representations. The plaintext identity of cipher labels in the plurality of cipher labels is determined using isomorphic pattern recognition. The present invention includes A plaintext isomorphic dictionary containing plaintext words organized by their root isomorphic patterns. Additionally, the present invention includes a Raster-Plaintext Library which contains extensive data on taster letters, and their plaintext identities, from an exhaustive collection of fonts.

The invention belongs to the technical field of remote sensing, and in particular relates to an extraction method of water body thematic information of remote sensing images based on sequential nonlinear filtering. The method comprises the following four steps: preprocessing remote sensing images and completing sequential nonlinear filtering; calibrating filtering result image areas and calculating the feature values of each calibrated area; establishing the selection criteria of water body target areas; and carrying out automatic extraction of water body areas and post-processing of water body thematic extraction information based on morphology so as to generate thematic information vector data meeting the requirements of common GIS software finally. The extraction method, starting with the basic operation of grey-scale mathematical morphology, establishes a sequential nonlinear filtering model to directly carry out nonlinear filtering and extract water body areas in remote sensing images; according to relevant knowledge of water body, feature values of different water body areas extracted through sequential nonlinear filtering are calculated; according to water body imaging characteristics and relevant knowledge of water body areas, the selection criteria of the water body target areas are established to realize automatic and quick extraction of water body thematic information on remote sensing images. The extraction method is less influenced by noise and error and has strong robustness; moreover, the method realizes fully automatic processing of extraction water body without manual intervention, can process extensive data, and has less time consumption.

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the b_i variables are scaled by the ratio of the gains K and k. The b_i have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

An embodiment of the present invention is directed to quantifying the value of an individual log source sent to the SIEM. Through vendor-agnostic measurement, an algorithmic model utilized by a Log Quality Value (LQV) index enables security engineers and incident response (IR) teams to determine which logs provide the most value for security investigations. An embodiment of the present invention recognizes a positive correlation between the LQV index and critical logs used to investigate the attack. An embodiment of the present invention may be extended to evaluate the LQV algorithms against a more extensive dataset from live production environments and to further measure the tool effectiveness through periodically comparing the LQV index to logs used to detect security incidents.

A method for providing independent static and dynamic models in a 000 prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the b_i variables are scaled by the ratio of the gains K and k. The b_i have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

A category-based data analysis system for processing stored data-units and calculating their relevance to a subject domain with exemplary precision over Web and electronic document searches. A central processor parses a search definition comprising queries against target data sources, and a Boolean expression before launching a plurality of search engines. The Boolean expression and subexpressions comprise individual key-phrases and categories of key-phrases. Fine control of natural language matching behavior is controlled by parameters at the category and key-phrase level. The search engine reads data-units from a plurality of data sources, evaluates relevance, and stores metadata with the data-unit comprising relevance data by key-phrase and category. These results can be further analyzed by SQL query engines, spreadsheets, and Business Intelligence tools.

A computer-implemented method for identifying from a broad range of data sources, social entities that perform the function of Social Influencers. The method aggregates relevant results to provide a more comprehensive analysis of a subject domain than can be achieved with a manual search. Search results are presented in the form of web-presences that are logically related webpages, disaggregated and categorized from websites. Web-presences can be clustered by association with a social entity and are ranked to determine their function as Social Influencers. These results can be further analyzed by SQL query engines, spreadsheets, and Business Intelligence tools.

Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.

A server is embedded directly into a storage subsystem. When moving between the storage subsystem domain and the server domain, data copying is minimized. Data management functionality written for traditional servers is implemented within a stand-alone storage subsystem, generally without software changes to the ported subsystems. The hardware executing the storage subsystem and server subsystem can be implemented in a way that provides reduced latency, compared to traditional architectures, when communicating between the storage subsystem and the server subsystem. When using a plurality of clustered controllers, traditional load-balancing software can be used to provide scalability of server functions. One end-result is a storage system that provides a wide range of data management functionality, that supports a heterogeneous collection of clients, that can be quickly customized for specific applications, that easily leverages existing third party software, and that provides optimal performance.