14 results about "Multiple dimension" patented technology

The invention relates to the technical field of software, in particular to a judicial big model effect evaluation method based on multiple dimensions and a program product. Comprising the following steps: obtaining a generation result of a large model and a corresponding reference answer; and calculating the similarity between the generated result and the reference answer from a word dimension, a statement dimension, reinforcement learning and a human feedback dimension, then carrying out normalization operation on the three numerical values, and then carrying out weighted summation to obtain a final evaluation score. According to the method, the generation effect of the large model is evaluated from multiple dimensions, and by using the evaluation, a better training effect can be obtained in the large model training process.

This invention relates to the field of data processing and discloses a method, system, and electronic device for generating multimodal data from multiple dimensions. The method includes: acquiring raw data from at least two dimensions; integrating and synchronizing the raw data from each dimension; annotating the data characteristics of different dimensions using corresponding analysis methods; extracting semantic information and structured features of each dimension to obtain annotation results for each dimension; analyzing each annotation result using a corresponding analysis model to obtain modal analysis results; assigning appropriate weights to each modal analysis result based on the credibility, historical accuracy, and importance of the raw data; fusing the modal analysis results using a weighted fusion method to generate multimodal data; and generating final multimodal data after iteration. This application achieves the technical effects of multimodal data alignment, annotation, fusion, and generation of new data.

The invention provides a multi-dimensional model processing method, device, system and equipment. The method comprises the steps that a query command sent by a client side is received, the query command comprises an index and is used for querying dimension members in a multi-dimensional model, the multi-dimensional model comprises multiple dimensions, each dimension comprises one or more dimension members, each dimension member comprises an index field, and the index field is used for searching the dimension members in the multi-dimensional model; the length of the index field of the same dimension is determined according to the length obtained by compressing the original index field of each dimension member of the dimension, and matching the index fields of the plurality of dimension members of the multi-dimensional model according to the index to obtain a first dimension member with the index field the same as the index. According to the scheme, the dimension members of the multi-dimensional model can be compressed, so that the query efficiency of the dimension members in the multi-dimensional model is improved.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may generate a visualization comprising a set of data from a data model. The set of data comprises a set of measure values categorized according to a plurality of dimensions. The program may receive a request to rank the set of measure values based on a first dimension in the plurality of dimensions and a second dimension in the plurality of dimensions. The program may in response to receiving the request, generating a query for a subset of the set of data for the visualization. The query comprises a set of conditions. The program may send the query to a computing system for processing. The program may receive, from the computing system, the subset of the set of data. The program may update the visualization to include the subset of the set of data.

The present application provides a data analysis method, data analysis device, and data analysis system for multidimensional data. The method includes: obtaining a target data table, analyzing the target data table to obtain a fact table and multiple dimension tables; analyzing the fact table and each dimension table to obtain multiple dimension fields and multiple metric fields; analyzing each dimension field to determine a first association type between each dimension field and combining the dimension fields according to the first association type to obtain multiple first dimension groups; adding the metric fields corresponding to the dimension fields in each first dimension group to the corresponding first dimension group to obtain multiple second dimension groups, and constructing corresponding data models based on each second dimension group to obtain multiple cubes; and performing data analysis on each cube to obtain multiple data analysis results. This method solves the problem in the prior art of excessive computational complexity and excessive resource consumption caused by combining all dimensions when performing data analysis on a data model.

Compressing files is disclosed. An input file to be compressed is first aligned. When the file has multiple axes or dimensions, the file is aligned along each of the axes. Aligning the file includes splitting the file into sequences that can be aligned along each of the axes or dimensions. Aligning the file generates a compression tensor, where each row or dimensional space of the compression tensor corresponds to part of the file. A consensus tensor is determined from the compression tensor. Using the consensus tensor, pointer lists are generated. Each pointer lists identifies a subsequence or portion of the consensus tensor. The compressed file includes the pointer lists and the consensus tensor.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may generate a visualization comprising a set of data from a data model. The set of data comprises a set of measure values categorized according to a plurality of dimensions. The program may receive a request to rank the set of measure values based on a first dimension in the plurality of dimensions and a second dimension in the plurality of dimensions. The program may in response to receiving the request, generating a query for a subset of the set of data for the visualization. The query comprises a set of conditions. The program may send the query to a computing system for processing. The program may receive, from the computing system, the subset of the set of data. The program may update the visualization to include the subset of the set of data.

The embodiment of the invention discloses a resource configuration method and device, electronic equipment and a computer readable storage medium. The method comprises the steps that the weight of each attribute in multiple dimensions is acquired; the resources corresponding to the attributes in the same dimension are different; respectively combining the attributes in the plurality of dimensions to obtain a plurality of hierarchies; each hierarchy comprises one attribute in each dimension; determining the priority of the plurality of levels according to the weight of each attribute included in the plurality of levels; according to the sequence of the priorities of the plurality of levels from high to low, retrieving resources corresponding to each attribute included in the plurality of levels to obtain a preferentially returned target resource; and performing configuration according to the target resource. Therefore, according to the scheme, the resources can be configured more finely and flexibly.

Dimension decomposition convolution systems and techniques are described. The system receives a tensor having a first dimension. The system processes the variants of the tensor using a convolution function having a second dimension to generate a processed tensor. The first dimension is greater than the second dimension. A plurality of dimensions from a first dimension of the tensor are grouped into dimensions of variants of the tensor to reduce the dimensions of the variants of the tensor to a second dimension.

The embodiment of the application discloses a data fluctuation attribution analysis method, system and related device, the method comprises the following steps: obtaining the current data of each sub-dimension under each dimension participating in statistics, obtaining historical statistical data different from the target statistical data collection time, calculating the data fluctuation measurement value corresponding to the current data of each sub-dimension under each sub-dimension, and determining the root cause dimension from the multiple dimensions according to the data fluctuation measurement value. Considering that the current data of a certain sub-dimension may be shared by other sub-dimensions under the same dimension, the application can not only quantitatively reflect the data changes of each sub-dimension level, but also feedback the influence of the current data of a certain sub-dimension on the current data of other sub-dimensions under the same dimension, so that the data mutual exclusion or data overlap scene between sub-dimensions can be finally analyzed, thereby accurately inferring which dimension in the upper layer is the root cause dimension causing the data fluctuation, and promoting the process of subsequent application.

A database system comprising an interface and a processor may perform the techniques described in this disclosure. The interface may receive a query to access a first dataset and a second dataset, the query identifying a plurality of dimensions of the first dataset and the second dataset. The first dataset may include a first dimension and the second dataset may include a second dimension but excludes the first dimension. The processor may convert the query into a structured query conforming to a database query language, the structured query defining a join between the first dataset and the second dataset across the second dimension. The processor may also send the structured query to a database and receive an indication in response to the structured query that a virtual table has been created joining the first dataset and the second dataset across the second dimension of the second dataset.