Multidimensional Data Model Research Articles

A Multidimensional Financial Data Model for User Interface with Process Mining Systems

A Multidimensional Financial Data Model for User Interface with Process Mining Systems

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis

This paper explores the design and optimization of multidimensional data models to enhance the query performance and data analysis capabilities of OLAP (Online Analytical Processing) systems. It delves into three prominent dimensional modeling techniques: Star Schema, Snowflake Schema, and Galaxy Schema, analyzing their impact on query complexity, data redundancy, storage requirements, and ease of maintenance. Additionally, it examines three aggregation strategiesPre-Aggregation, Dynamic Aggregation, and Hybrid Aggregationfocusing on their effectiveness in balancing query response time, storage efficiency, flexibility, and computational cost. The study further investigates performance optimization techniques, including query optimization, partitioning, and materialized views, providing case studies and experimental data to illustrate their benefits and challenges. The findings underscore the importance of tailored optimization strategies in OLAP systems to meet varying business needs and query patterns, highlighting the trade-offs between performance gains, storage requirements, and implementation complexity

Spectrum Situation Awareness for Space-Air-Ground Integrated Networks Based on Tensor Computing.

The spectrum situation awareness problem in space-air-ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space-air-ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.

On the capabilities of technologies for multidimensional representation of data in decision support processes

The authors show the objectivity of the problem of expanding the sphere of influence and increasing the complex power of anthropogenic impacts on natural ecosystems with the absolute and relative exhaustion of resource support for anthropogenic activity in the biosphere. The article proposes a solution based on a multidimensional representation of information (multidimensional data model), which allows analyzing multivariate information about the subject area in the decision-making process.

Extrapolation and Interpolation of Information in Computer Science

Proposed method is dealing with multi-dimensional data modeling, extrapolation and interpolation using the set of high dimensional feature vectors. Identification of handwriting, signature, faces or fingerprints need data modeling and each model of the pattern is built by a choice of characteristic key points and multi-dimensional modeling functions. Novel modeling via nodes combination and parameter γ as N-dimensional function enables data parameterization and interpolation for feature vectors. Multi-dimensional data is modelled and interpolated via different functions for each feature: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

A Data Quality Multidimensional Model for Social Media Analysis

AbstractSocial media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.

GPUTucker: Large-Scale GPU-Based Tucker Decomposition Using Tensor Partitioning

Tucker decomposition is used extensively for modeling multi-dimensional data represented as tensors. Owing to the increasing magnitude of nonzero values in real-world tensors, a growing demand has emerged for expeditious and scalable Tucker decomposition techniques. Several graphics processing unit(GPU)-accelerated techniques have been proposed for Tucker decomposition to decrease the decomposition speed. However, these approaches often encounter difficulties in handling extensive tensors owing to their huge memory demands, which exceed the available capacity of GPU memory. This study presents an expandable GPU-based technique for Tucker decomposition called GPUTucker. The proposed method meticulously partitions sizable tensors into smaller sub-tensors, which are referred to as tensor blocks, and effectively implements the GPU-based data pipeline by handling these tensor blocks asynchronously. Extensive experiments demonstrate that GPUTucker outperforms state-of-the-art Tucker decomposition methods in terms of the decomposition speed and scalability.

Study and analysis of wastewater treatment in the refining and chemical industry based on multidimensional data analysis

Abstract To improve the efficiency of wastewater treatment in the refining chemical industry, this paper designs a wastewater treatment process in the refining chemical industry based on multidimensional data analysis to guarantee the optimal operation of wastewater treatment operations. In the process of multidimensional data analysis, triangular polynomials are introduced to calculate the robust distance of high-dimensional data and construct a multidimensional data model. The optimal multidimensional clustering levels are divided using data operations to obtain high-dimensional data sequences in the multidimensional data set to accelerate data clustering. A parallel coordinate system realizes the visual representation of the wastewater treatment process stems to improve the capacity representation of wastewater treatment data. Simulation analysis was conducted to verify the treatment effect of the wastewater treatment process based on multidimensional data analysis in the refining and chemical industries. The results showed that the process designed in this paper resulted in a dissolved organic matter fraction concentration of 16.28, and the humic acid-like fraction increased by 36.2%, which was significantly higher than the protein-like fraction of 21.6%. And among all protein-like fractions, only C4 was positively and significantly correlated with fluorescent organic matter at the P < 0.05 level. It can be seen that the multidimensional data analysis model is conducive to promoting the development of wastewater treatment technology in the refining chemical industry and ensuring that the refining chemical industry steps into a circular and sustainable development track.

Data Warehouse Model Design PT. Pos Indonesia

This study aims to determine the role of the data warehouse, data warehouse design, and analyze case studies from the of PT. Pos Indonesia. The data warehouse has an important role for a company in terms of storing and processing data automatically to assist companies in terms of finding data, managing data, making quality decisions and data consistency. As the need for practical data develops and is able to handle data with very large results and display historical data, it is therefore necessary to develop a database for PT. Pos Indonesia. This study is a file design activity from the information provided by the PT. Pos Indonesia. The research method used in this study is a quantitative method which is divided into several stages. The planning phase of the warehouse starts with the subject data warehouse, defines the data source, and steps to implement capabilities to create multidimensional data models in the form of star schemas, snowflake schematics and constellation diagram experts, ETL (Extract, Transform and Load) processes and report analysis. Reporting on the data warehouse information analysis process at the Post Shop PT. Pos Indonesia compiles the best sales information based on product type, the highest sales volume per customer and the highest sales volume based on time. The report analysis results are very useful for making data from 2014 and 2015

Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning

With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice.

Research on education management system based on machine learning and multidimensional data modeling

Abstract With the rapid development of modern information technology, especially the continuous improvement of computer network technology, the application of education management system in teaching is becoming more and more extensive. Therefore, education management system and machine learning will become an important combination direction of education. First, design and implement a complete network education management system based on B/S architecture, and design from the overall system design, detailed design and database design. Among them, the computer language combined with the SQL Server database realizes the network teaching function and the education system management function. Then, PSO-SVM machine learning is adopted to make personalized learning course recommendation for students. Multi-dimensional data analysis and feature extraction. Finally, the PSO-SVM proposed in this paper is applied to the education management system for modeling training, and compared with other traditional machine learning personalized recommendation accuracy and likeness of learning course recommendation. The experimental results show that the PSO-SVM proposed in this paper is superior to other traditional machine learning models in terms of personalized learning course recommendation and favorability, with an accuracy rate of 94.7%.

Construction of the Impact Analysis Platform of Grain Import and Export Trade Based on the Multi-Dimensional Data Perception Model

The article analyzes from the perspective of the principles of international economics, the main influencing factors of the changes in the import and export trade volume of my country's agricultural products are: exchange rate, international food prices, etc. In order to further promote the healthy development of my country’s grain trade, my country should increase food policy support, increase investment in agricultural production technology, drive by multi-dimensional data models, design model analysis engines, and output model examples for specific platforms to build a data warehouse. Actively look for food export measures to cope with price advantages. Further opening up the grain trade market and increasing grain imports by an appropriate amount will become a basic trend.

Review: Research on product surface quality inspection technology based on 3D point cloud

In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

Editorial: Tensor numerical methods and their application in scientific computing and data science

Editorial: Tensor numerical methods and their application in scientific computing and data science

Model-based clustering for multidimensional social networks

Abstract Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterised by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low-dimensional social space. We propose the infinite mixture latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian non-parametric framework that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on extensive simulated data experiments. It is also employed to investigate the presence of communities in two multidimensional workplace social networks recording relations of different types among colleagues.

Analysis of Substation Joint Safety Control System and Model Based on Multi-Source Heterogeneous Data Fusion

As the number of substations continues to increase globally and the market demand continues to rise, the current workload of maintenance and daily operation of substations in power grids cannot meet the current demand if only relying on manual work, and the design and implementation of intelligent safety control solutions for substations is imperative. Therefore, this paper proposes a joint safety control system and model analysis for substations based on multi-source heterogeneous data fusion. Firstly, a three-dimensional visualization substation efficient interactive operation platform is realized, which realizes the functions of substation scene roaming, system login, information management, equipment parameters, status viewing and operation ticket pushing; after that, a variety of intelligent hardware devices for data collection, such as multi-dimensional terminal sensors, intelligent wearable devices, intelligent pre-built positioning installation measure rod, and substation intelligent inspection robots are designed to greatly improve the substation inspection efficiency and realize real-time monitoring and data interaction in the inspection process. Finally, we propose an Attention-LSTM-based prediction model for substation multidimensional data, which can predict power equipment spatio-temporal data in the short term, and the prediction results can be combined with intelligent devices for joint diagnosis. The Attention-LSTM prediction model is well-trained in transformer oil temperature experiments, and the experimental results show that this model can provide early warning for the abnormal state of substation power equipment. In summary, this thesis describes a set of complete and practically feasible intelligent safety control methods for substations. The joint safety control system and model analysis of the substation based on multi-source heterogeneous data fusion designed in this paper is mainly oriented to the substation as an electric power workplace, which has quite a vast application prospect for energy equipment.

Estimation and Inference for a Multi-dimensional Panel Data Model with Multilevel Factors

Estimation and Inference for a Multi-dimensional Panel Data Model with Multilevel Factors

A system based on an artificial neural network of the second generation for decision support in especially significant situations

Currently, specialized system models implemented on the basis of decision support in exceptional (emergency) situations (states) using machine learning, artificial intelligence (including using neural networks) to reproduce, predict and prevent (or minimize the risk of consequences) in exceptional situations are useful and are becoming increasingly popular. Floods also fall under such exceptional situations and states, on the basis of which the task of early forecasting of an exceptional situation arises, using the example of rising water levels at stationary hydrological posts in order to prevent (or minimize the risk) the transition of the territory management system under consideration to an exceptional state (emergency situation). To solve this problem, a decision support system is proposed for early prediction of water rise levels, based on a neural network (intelligent) analysis of retrospective data (code of a stationary hydrological post / automatic station, date, water level at a stationary hydrological post / automatic station, atmospheric pressure, wind speed, snow cover thickness, amount of precipitation, time and air temperature) in order to calculate the values of water levels for 5 days in advance. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network (ANN) learning rate increase factor. A mathematical model of the subject area is constructed in the form of a multidimensional space and an integrated multidimensional data model. A single integrated model describes the subject area quite fully and can be used as a single source of information, which provides system information support, defined as a set of distributed databases. All this makes it possible to improve the accuracy of forecast values of water levels at stationary hydrological posts and automatic stations. The main difficulty in early forecasting is a sharp rise in water levels over a short period of time (usually 1 day) for various reasons. Our neural network model takes this into account and allows us to give more accurate results. The results of the analysis of the effectiveness of the method showed that the proposed decision support system is more accurate: the average error does not exceed 55.68 cm for the entire flood period, the average error between the real and calculated values in the framework of forecasting for 5 days does not exceed 2.10 % compared to existing common methods/systems (8.36 %). This will give the necessary time to special services to carry out anti–flood measures to prepare for the protection of technical facilities of enterprises.

APPLICATION OF PROJECTION ALGORITHMS FOR GEOMETRIC MODELING AND OPTIMIZATION OF SOCIO-ECONOMIC PROCESSES

The article proposes an approach to systematization, modeling and optimization of multidimensional statistical data based on the use of projection algorithms for computer modeling. The proposed approach is presented on the example of computer modeling and optimization of socio-economic data, but it can also be effectively used to systematize and analyze other experimental statistical data. It consists the fact that the original multidimensional data are presented in the form of projections on the Radishchev’s complex drawing in the form of curved lines system. Then, on the indicator curve, the optimal value of the socio-economic indicator is selected (as a rule, this is one of the extrema of the function) and the value of the time at which it was reached is fixed. Here, the indicator curve is understood as the curve corresponding to the response function, and the factor curve is the curves corresponding to the factors influencing the response function. Further, a scientific hypothesis is put forward that the joint interaction of factors recorded at a given moment in time ensures the optimal value of the socio-economic indicator. Thus, we obtain the optimal values of the factors influencing the response function, which in this case is the socio-economic indicator. The interaction between the indicator curve and the factor curves is carried out through the line of interprojection connection. The proposed scientific hypothesis is fully justified, provided that all possible factors affecting the behavior of the socio-economic indicator are taken into account. The implementation of the proposed approach was carried out using the Radishchev’s complex drawing, which displays both the values of the factors and the socio-economic indicator. At the same time, on the Radishchev’s complex drawing, the most favorable conditions for the socio-economic indicator are selected by methods of mathematical analysis. Further, with the help of the line of inter-projection communication, by means of standardization, the desired weight coefficients are determined, corresponding to the most favorable conditions for the socio-economic indicator. This approach is completely independent of the subjective opinion of experts and based solely on the initial statistical information.