40 results about "Denormalization" patented technology

The invention relates to a method for migrating data from a non-relational database to a relational database. The method comprises the following steps: connecting the non-relational database needing data migration, and obtaining table information in the database; selecting a table needing data migration and a database mode definition method, wherein the selectable database mode definition method comprises user definition, union set compatibility, intersection compatibility, and first article data; guiding the information from the non-relational database to a defined xml document; converting the database design mode of the non-relational database to a design mode of the corresponding relational database; connecting the corresponding relational database, and setting up a corresponding data table according to the database design mode in the xml document; setting up a corresponding index according to index information in the index element. The method for migrating data solves the problems of a denormalization design, non-modeling and special data types when data are migrated from the non-relational database (such as a MonogDB) to the relational database.

A schema modeler for generating an efficient database schema. Provides intelligent choices for schema structure, generates efficient schemas while minimizing the amount of experience required by a database designer. Architectural elements of a schema design are proposed based on field inputs such as field type or relationship. Schema information is manually entered or imported. Configured for structural compatibility and semantic compatibility checking on fields and relationships for data integrity due to nested structure denormalization, inspection of lookup tables that can hold an unlimited number of records, inspection of taxonomy defined on a non-main table, and inspection of the schema for the existence of a main table. Provide suggested field types or schema structures that allow for a more efficient schema to be generated. Field types may include qualifier, multi-lingual, calculation and may include family or attribute table suggestions as well. Generation of validations and data profiling ensure efficient results.

The invention relates to the technical field of network security evaluation, in particular to a network security situation prediction method based on a combination of the chaos theory and a neural network. The method comprises the following steps: carrying out processing of normalized network security situation value sequence sets through the mutual information method and the cao method to obtain the optimum embedded dimensions of network security situation sample values, carrying out phase-space reconstruction, and analyzing the maximum Lyapunov exponent of reconstructed samples to determine whether the evaluated samples have chaos predictability or not; determining the numbers of nodes of an output layer and a hidden layer of a BPNN according to characteristics of a nonlinear time sequence and experience; carrying out parameter optimization through an improved firefly algorithm, so as to determine network weights and offset values and establish a network security situation prediction model; and inputting test set samples into the BP neutral network for prediction, and carrying out denormalization of obtained prediction values. The method provided by the invention has the advantages that a network security situation can be more precisely predicted, and the network security situation prediction convergence rate can be increased.

The invention discloses a denormalization strategy selection method on a data set and particularly relates to a system for reasonably managing establishment, allocation and withdrawing of the software licenses in the enterprise internal network range. The software license management system comprises a main server, agent servers and clients. The software license management system has the advantages that through the mechanism that a part of clients share a small part of software licenses, the demanded quantity of software licenses of an enterprise is effectively reduced, the agent servers are arranged in all departments to manage the issuing of the software licenses of the departments, meanwhile, the main server correspondingly controls the number of the software licenses on each agent server, reasonable allocation of the software licenses in the enterprise is achieved, tasks of allocating the software licenses are distributed to all the agent servers through a layering idea, therefore, the load of the main server can be effectively reduced, meanwhile, flow of the enterprise internal network is saved, and excessive system communication information is prevented from flowing into an enterprise backbone network.

The invention discloses a sewage pump station water level prediction method base on neural network. The method comprises the steps of: firstly selecting prediction model variables including input variables and output variables, wherein input variables include an upstream pump station lifting capacity, forebay liquid level variable quantity, pump station discharge rate and switch pump liquid level control setting quantity and the output variables include a pump station forebay water level; secondly normalizing the data; then setting up a BP neural network framework and training the BP neural network; and finally carrying out denormalization on the data of the neural network. The method has higher prediction accuracy compared with the traditional steady flow computing method.

The invention discloses a denormalization strategy selection method based on the frequent item set mining algorithm and particularly relates to a denormalization strategy selection method for mass data sets based on the frequent item set mining algorithm. The frequent pattern mining method is applied to guiding database denormalization for the first time; based on the frequent pattern mining algorithm of a concise tree, a brand-new process of establishing the concise tree and a correct counting method, serving for database denormalization selection, are provided. The denormalization strategy selection method based on the frequent item set mining algorithm has the advantages that through the frequent item set mining algorithm of association rules, important association or relation of item sets in mass data is discovered to guide DBA and the like to select and build denormalization strategies of databases, and the problem of performance bottleneck caused by mass table joins in the mass data is solved.

The present invention relates to systems and methods for storing, navigating and retrieving information. In particular, the present invention is concerned with systems and methods for storing data in, for retrieving data from, and for navigating large and/or complex datasets. The systems and methods of the present invention in particular are concerned with the materialization/denormalization of complex data sets comprising a plurality of large, interconnected but distinct data record collections. The materialization/denormalization of such data sets can be performed in a precomputation phase, prior to a browsing/searching operation.

A data processor includes a first bit field of a first plurality of bits representing a mantissa of a floating point number and a second bit field of a second plurality of bits representing an exponent of the floating point number. The first plurality of bits is partitioned into a plurality of regions, each of the plurality of regions comprises more than one bit of the first plurality of bits. A leading zero anticipator or other type of leading bit indication circuit is coupled to each region and determines a position of a leading bit of the first plurality of bits. A normalizer is coupled to receive a region of the plurality of regions that contains the leading bit, the normalizer may normalize or denormalize the region to produce a normalized or denormalized floating point number.

A method is disclosed that normalizes a telephone number from a number of input sources into standardized E.164 “+” format so that the telephone number can be represented in a consistent format relative to the user's own mobile number MSISDN and used uniformly across different functional components in a communications system. The disclosure also describes a method for denormalizing a telephone number in the E.164 “+” format into a dial digit phone number sequence that can be used to successfully establish calls from a user terminal device and to route calls via optimal routes through a voice termination network.

Methods and apparatus are disclosed for handling floating point exceptions in a processor that executes single-instruction multiple-data (SIMD) instructions. In one embodiment a numerical exception is identified for a SIMD floating point operation and SIMD micro-operations are initiated to generate two packed partial results of a packed result for the SIMD floating point operation. A SIMD denormalization micro-operation is initiated to combine the two packed partial results and to denormalize one or more elements of the combined packed partial results to generate a packed result for the SIMD floating point operation having one or more denormal elements. Flags are set and stored with packed partial results to identify denormal elements. In one embodiment a SIMD normalization micro-operation is initiated to generate a normalized pseudo internal floating point representation prior to the SIMD floating point operation when it uses multiplication.

Methods and apparatus are provided for efficiently normalizing and denormalizing data for cryptography processing. The normalization and denormalization techniques can be applied in the context of a cryptography accelerator coupled with a processor. Hardware normalization techniques are applied to data prior to cryptography processing. Context circuitry tracks the shift amount used for normalization. After cryptography processing, the processed data is denormalized using the shift amount tracked by the context circuitry.

The invention discloses a processing method, apparatus and system for positioning information. The processing method comprises the steps of: according to at least one wireless access point (AP) arranged in an application scene, acquiring position information of terminals accessed to the AP in the application scene; according to the position information of the terminals in the application scene, determining a position of each terminal in the application scene; and according to the position of each terminal in the application scene, carrying out processing. The processing method and system for the positioning information, which are provided by the embodiment of the invention, can be applied to a business area system, and solve the problems of denormalization, non management and non intelligence in the existing business area system; a business area manager can manage and monitor terminal users in real time; and a service level and development of a business area are improved and promoted.

A spectral image processing method is capable of reducing noise while maintaining necessary information. The spectral image processing method performs processing on a spectral image of a specimen, including a step of normalizing spectra (=spectral brightness curves) of respective pixels constituting the spectral image such that their brightness levels become equal, a step of smoothing the normalized spectra in spatial directions of the respective pixels, and a step of denormalization of multiplying spectra of the respective pixels obtained by the smoothing by either one of brightness levels of the pixels corresponding the spectra and values corresponding to the brightness levels. Consequently, the noise can be reduced while information on brightness distribution on the image is maintained.

The present invention discloses a BP neural network based GFET modeling method. The method comprises six steps of: collecting data; preprocessing the data; determining a BP network GFET model structure to be used; building and training a GFET model; performing denormalization on an output electric current predicted by the GFET model to obtain a model output value; and implementing Verilog-A of the GFET mode. The method of the present invention has the advantages of being short in calculation time, high in accuracy, requiring no complete theory knowledge of devices and being applicable to circuit design and simulation.

Systems and methods are provided for creating abstracted, normalized, and reuseable and combinable representations of information contained in multiple documents and information of any supported format, and allowing for exporting of information in any other desired and supported format. Further the system and methods provide for uploading documents based on a known template, where the data members can be automatically recognized and the document stored in normalized format without end-user or developer intervention. Normalization of data is achieved transparently on upload and denormalization performed transparently on download. Further, embodiments provide for the reuse and recombination of data members to create entirely new representations.

A method and an apparatus for establishing a model for a cement pre-grinding process, and application thereof are provided. The method comprises: determining each parameter; determining a qualitativerelationship between the nodes by taking each parameter as a node, and establishing a preliminary fuzzy cognitive map; solving a correlation parameter value; and substituting the correlation parametervalue into the preliminary fuzzy cognitive map to acquire a fuzzy cognitive model. The apparatus comprises: a parameter determining unit, a preliminary fuzzy cognitive map establishing unit, an arithmetic unit and a fuzzy cognitive model acquiring unit. The application is that the fuzzy cognitive model is applied to the parameter prediction in the cement combined grinding pre-grinding process, and comprises the steps of: using actual production data at the time t, a qualitative relationship of the model, a correlation parameter value and a conversion function to calculate a state value of each node of the model at the time t+1; and after denormalization, obtaining a predicted value of each parameter on site at the time t+1. According to the technical scheme of the present invention, the problem that the requirement of the cement plant for improving the quality cannot be satisfied due to ignoring certain parameters in actual production is solved.

The invention discloses a voice signal reestablishment method based on a deep autoencoder. The method comprises the following steps of S101, obtaining encoded data and inputting the encoded data intoa decoding unit; S102, processing the encoded data by the decoding unit through utilization of a deep decoder neural network, and outputting decoded data; S103, carrying out denormalization on the decoded data; S104, carrying out inverse discrete Fourier transform on the data processed by the S103; S, 105, carrying out overlapping-addition on the data processed by the S104, thereby obtaining reestablished voice signals, wherein the coded data is obtained through utilization of the following steps of S201, framing original voice signals; S202, carrying out discrete Fourier transform on the framed data; S203, carrying out normalization on the data processed by the S202; S204, inputting the normalized data into the coding unit; and S205, processing the data normalized by the S203 by an encoding unit through utilization of a deep encoder neural network, wherein obtaining the coded data.

A control device for a machine tool includes an oscillation command generation part which generates an oscillation command for a fees axis, a first adding part which adds an oscillation command to a position deviation between a position command and the detected position of the fees axis to generate a resultant command, a normalization part for normalizing the resultant command, a learning control part that obtains a correction amount of the resultant command based on an oscillation phase obtained from the oscillation command and a normalized resultant command and which adds the correction amount to the resultant command, a denormalization part which denormalizes an output from the learning control part, and a second adding part for adding a denormalized output from the denormalization part to the resultant command.