OLAP Technology Research Articles

Expanding the intellectual capabilities of OLAP technology using neural networks

The article highlights the main characteristics, features and structure of Online Analytical Processing systems based on the same technology that perform online analytical processing of data. This technology allows analysts to explore and navigate a multidimensional indicator structure called an online analytical processing cube (data cube). Indicators (measures) of data cube play an important role in the decision-making process. To solve certain problems, these measures often need to be classified or grouped. Moreover, empty measures are common in data cube. This fact negatively affects strategic decision making. Unfortunately, online analytical processing itself is not well suited for classifying, clustering, and predicting empty measures of data cube in the presence of large data. In this regard, today there is a need to use new technologies to solve such problems. Such technologies include neural networks. The article discusses the problem of integrating online analytical processing and a neural network, showing the possibilities and advantages of such integration. It mentions that in the case of big data, the integration of OLAP and neural networks is very effective in solving problems of classification, clustering and empty measure prediction of data cube. An architectural and technological model for the integration of online analytical processing and neural networks is presented.

Modern technological tools for creating multidimensional matrix database machines

The article discusses a group of tasks that require online analytical data processing. The authors analyze the components of OLAP technology: multidimensional cubes, implementation methods and methods for accessing them. Based on the analysis, it is concluded that truly multidimensional databases do not exist. An algebra of multidimensional matrices is proposed, which corresponds to relational algebra. It is shown that on the basis of this algebra it is possible to develop a DBMS that will provide an effective parallel implementation of OLAP technology. A method for constructing a multidimensional matrix database engine based on available software and hardware is proposed.

Architecture of intelligent SIEM for detecting cyber incidents in databases of military information and communication systems.

The article looks at the current supply of cyber defense to the databases of information and communication systems. The effectiveness of continuous visits to databases is analyzed. The analysis indicates that existing systems are not immune to security, which is a critical aspect in the field of cybersecurity. A new approach to the architecture of the SIEM system is being introduced, which is aimed at different parts of the protection circuit of the information and communication system. The fragmented architecture makes it possible to effectively detect and respond to cyber attacks at all levels of protection, from the operating system to databases. A new approach is being introduced to ensure cyber security of databases with the benefit of the intelligent capabilities of the SIEM system. The main aspect of this architecture is rich database protection, which allows you to effectively detect and respond to cyber attacks. The registration approach includes the addition of data from different levels to the security circuit of the information and communication system, the module for analyzing data about categories in the database, which operates on the basis of The use of fuzzy multiplicity theory methods, fuzzy logic inference and rule correlation module to improve the detection of cyber incidents. And also the integration of OLAP technologies to provide a deep and analytical view of the database security system. The architecture for identifying cyber incidents has been designed to improve the efficiency of identifying cyber incidents related to the functioning of the database of information and communication systems and. The result of the investigation is a reduction in the capabilities of the SIEM system in identifying and responding to cyber identities in the database of the military information system. Further, we directly monitor the operational model of the cybersecurity system of the information and communication system database.

Approach to Generating Data for Analytical Reporting in the Enterprise Management System

Analytical reporting tools allow to turn disparate data accumulated in the information system into structured information described in terms of the relevant subject area. This information makes it possible to timely and adequately respond to changes, make informed management decisions for business leaders, analysts, economists, etc. The article describes the composition of business analysis systems, the components of which are built using OLAP technology. Varieties of OLAP solutions are considered. A method for constructing multidimensional cubes is proposed, developed and implemented by the authors in an automated system for managing financial and economic tasks. The scheme of storage, methodology and algorithm of data processing are described on the example of the "Purchases" subsystem. Examples of real analytical problems solved during the operation of the system at an industrial enterprise are given. A comparative analysis of data sampling time measurements based on structures and algorithms that provide the described approach to data generation, with a sample from unprepared relational tables for real problems from examples, was carried out. The texts of the corresponding SQL queries are given. The measurement results are summarized in tables and visualized in the form of a diagram. The evaluation of the results obtained is carried out, the problems of using unprepared data are described. The article also discusses the features of the analytical mechanisms of commercial accounting systems on the example of the 1C: Enterprise 8.3 platform (registers of accumulation, information, accounting, calculations). The effectiveness of the considered mechanisms for the formation of analytical reporting was assessed in comparison with the proposed approach to data structuring.

Benchmarking Criteria for A Cloud Data Warehouse

The Terasort benchmark and the YCSB benchmark are the two most used cloud computing benchmarks. Despite the fact that these benchmarks are quite helpful, data warehouse systems and associated OLAP technologies were not the focus of their creation. We initially introduce cloud computing and data warehouse systems in this essay. Then, we contend that the TPC-H benchmark, which is the most well- known benchmark for decision support systems, conflicts with the justifications for cloud computing (scalability, elasticity, pay-per-use, fault-tolerance characteristics), and customer relationship management (end- user satisfaction, Quality of Service features). Finally, we propose updated specifications for a benchmark for cloud data warehouse systems. The suggested specifications ought to make it possible to fairly compare the products offered by various cloud system providers.

Application of decision tree algorithm mining model in career planning goal simulation

Abstract To help college students choose their first career type, the author proposes an application method of a decision tree algorithm mining model in constructing a career planning goal model. The author uses OLAP technology to study and analyze the internal factors such as gender, personality, temperament, and interest of college students, as well as the career type that college students choose for the first time, excavate the potential law between the career type that college students choose for the first time and the internal factors of students, and apply it to the choice of college students’ first career type. The actual results show that the overall accuracy of the mining model is quite high when 50% of the data is used to obtain 50% of the target. The prediction accuracy of the decision tree model in 50% of the data is 47.14%. When the amount of data is 100%, the prediction accuracy of the model reaches 94.29%. The prediction accuracy of the actual decision tree model in 50% of the data is 47.14%. When the amount of data reaches 100%, the prediction accuracy of the model is 68.57%.

Energy Efficiency Evaluation Method for Township Power Users Based on OLAP Technology

With the development of the smart grid and energy Internet, the traditional demand side response is further expanded to the integrated energy management of users, which puts forward higher requirements for the energy management level of users. The energy consumption structure of users has changed greatly. How to measure the rationality of energy consumption, reduce the cost of energy consumption, save energy, and improve efficiency is very important for users. Aiming at the energy efficiency evaluation of township power users, this paper constructs an index system including users’ energy utilization. It also establishes an evaluation method based on the NSGA-III algorithm combined with OLAP technology to measure the closeness between each energy use scheme and the optimal energy use scheme. Finally, through the example analysis and verification, the results indicate that the proposed energy efficiency evaluation method based on OLAP technology is reasonable. Energy efficiency evaluation can meet the needs of information interaction between users and the smart grid, and improve the energy efficiency and energy-saving effect of township power users through energy efficiency evaluation and management.

Channel risks as a development factor of the risk management system in the customs sphere

Subject. The article addresses the customs risk management system. Objectives. The aim is to reveal the main characteristics of a new class of customs risk, i.e. a channel one. Methods. The research rests on the methodology of systems analysis and process-oriented approach, applies methods of modeling and comparative analysis. Results. We describe the current technology of decision-making to minimize risk, unveil the ideas of creating a decision support system with elements of artificial intelligence, present an experiment on model implementation for operational monitoring of risk objects at the stage of documentary customs control. We also define high-priority organizational solutions in the customs risk management system, give proposals on the structure of the customs risk service. The developed models can be used in research and analytical structural units of State authorities, including the Federal Customs Service, which exercise customs control using risk management systems. Conclusions. The study substantiates a new class of risks in the customs sphere, reveals limitations inherent in the risk-based approach methodology practiced in customs risk management, offers directions of modernization of the existing risk management system for a new class of customs risks, namely the introduction of end-to-end process of customs control; operational monitoring of the flow of commodity volumes; formation of profiles specific to channel risks; implementation of a multi-profile model of customs risk management; the use of OLAP technology for complex multidimensional data analysis under a specially developed methodology.

Marketing Decision Support System Based on Data Mining Technology

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.

Формирование данных информационной модели модульного здания в формате olap

The article provides an analysis of the use of OLAP technology in the design of modular objects based on information modeling technologies, presents the classification and systematization of information models of modular elements of maximum readiness. The purpose of this study is to develop and analyze a data model for the most effective data management of an information model of a capital construction object both at the design stage and at subsequent stages of the life cycle. In order to achieve this goal, it is necessary to take into account not only the features and attributes of information models, but also the key characteristics of modular design, including elements of the MEG within the existing tools for systematization of digital data. As a result, the impact of using OLAP cubes to represent a modular object at all stages of the life cycle is considered. New possibilities and advantages of using the OLAP data management model of the information model of the capital construction object are presented. The directions of further research are formulated.

Особливості застосування OLAP-моделювання в освіті

The analysis of the possibility of using OLAP-modeling that support the activities of financial institutions and support the functioning of business processes, the adoption of effective solutions at all levels of organization management (production, marketing, and personnel decisions, decisions affecting prices, discounts), which in the final the result will lead that the success of the entire organization as a whole, can be implemented through the use of OLAP technologies and OLAP systems. The results of the study can be tested through the implementation of relevant projects driven by challenges and trends in the financial industry, and market and regulatory changes.

Opportunities for the Application of a Model of Cost Management and Reduction of Risks in Financial and Economic Activity Based on the OLAP Technology: The Case of the Agro-Industrial Sector of Russia

The development of cloud technologies enables companies to actively implement technologies for cost management and risk reduction in their financial and economic activities. The use of cloud-based models of risk management in the financial and economic activities of the enterprise will help small and medium-sized companies in the agro-industrial sector in Russia to make structural and strategic changes, as well as discover new opportunities for business expansion. The purpose of the study is to develop models for cost management and reduction of risks in the financial and economic activities of companies based on the OLAP technology for application in Russian agro-industrial enterprises. The study employs a qualitative approach based on the case study methodology. The paper discloses and substantiates the authors’ conceptual model of a cost management system that allows executives to make decisions proceeding from four types of cost prices. The distinguishing feature of the management system is the use of a digital twin, which makes it possible to manage risks at the early stages of decision-making. The application of OLAP systems improves the quality of analysis and visualization methods as part of the cost management system. In addition, the study provides practical insight into how the applied model will help small and medium-sized agro-industrial enterprises to develop different business vision strategies based on cost reduction, manage the level of risk at the early stages of decision-making, and analyze information from a geographically dispersed logistics chain of divisions (production facilities, warehouses, stores).

Application of OLAP Technology in the Environment of Electronic Demography

The article proposes an approach to the development of an electronic demographic decision support system using technologies of Data Warehouse (DW) and Interactive Analytical Processing OLAP. This makes it possible to conduct high-level demographic research and provide support to decision-makers in demographic sphere. The article notes that demography is an interdisciplinary field of research and is defined as a complex science. Each industry of demography has many indicators. A sample list of these indicators is presented. The main characteristics of the DW, which should be taken into account when developing its architecture, are stated. Among these characteristics, one can find the main defining characteristics of Big Data — volume, velocity, variety, veracity, variability, visualization, value etc. For a more rational and efficient use of a large amount of information, taking into account its constant increase, to ensure the speed of execution of requests for a given system, it is proposed to use a Bus of Interconnected Data Marts (DM) as an architecture of DW. One of the advantages of using DM is that their use assumes distributed parallel data processing. This architecture allows for much faster results generation. It is based on the MapReduce distributed computing model and the Hadoop project. In addition, to effectively use large amounts of data, it is also proposed to use OLAP operations such as roll-up and drill-down, as well as fuzzy set theory, based on the technique of computing with words. The article also shows the practical application of interconnected DM. An OLAP cube is built on the basis of these DM. OLAP operations provide the ability to view cubes in different slices and provide aggregate data.

Various Olap Technologies and Their Impact on Decision Making

This paper provides an outline of knowledge repositing, OLAP, OLTP technologies, exploring the options, applications and therefore the design of knowledge repositing. the information warehouse supports on-line analytical process (OLAP), the practical and performance needs of that area unit quite totally different from those of the on-line dealing process (OLTP) applications historically supported by the operational databases. knowledge warehouses give on-line analytical process (OLAP) tools for the interactive analysis of two-dimensional knowledge of assorted granularities, that facilitates effective data processing. knowledge repositing and on-line analytical process (OLAP) area unit essential parts of call support, that has more and more become a spotlight of the info business. OLTP is customer-oriented and is employed for dealing and question process by clerks, purchasers and knowledge technology professionals. Associate in Nursing OLAP system is market-oriented and is employed for knowledge analysis by data staff, together with managers, executives and analysts. knowledge repositing and OLAP have emerged as leading technologies that facilitate knowledge storage, organization so, vital retrieval. call support places some rather totally different needs on info technology compared to ancient on-line dealing process applications.

Modern Systems for Processing and Analyzing GEO-data Based on OLAP Technology

Modern Systems for Processing and Analyzing GEO-data Based on OLAP Technology

Construction and performance analysis of data warehouse based on ship propulsion system

A large number of operation data will be generated in the process of ship propulsion system operation. Aiming at the processing, analysis and application of these operation data, a data warehouse for propulsion system is constructed based on the operation data of a ship, and the multidimensional analysis and result display of data are realized by combining OLAP technology with visual analysis software tableau. On the basis of the data warehouse, the soft sensing of parameters is applied to the data warehouse. Based on the soft sensing data, the hydrodynamic characteristics of the propeller are studied, and the hydrodynamic characteristic curves of the propeller are obtained. The research of ship propulsion system based on data warehouse provides support for the comprehensive information of propulsion system data.

Business Intelligence para apoio à gestão na construção civil: uma revisão sistemática da literatura

Introdução: A gestão é essencial para que se cumpram os requisitos de um projeto, e as ferramentas computacionais de Business Intelligence (BI) têm grande potencial de contribuição, fornecendo informações gerenciais sobre o negócio. Ferramentas desse tipo são utilizadas em diversos setores da indústria, porém na construção civil, foco deste trabalho, o cenário é diferente, com muito a avançar. Diante disso, apresenta-se um levantamento das ferramentas de BI aplicáveis ao setor da construção e suas utilizações. Método: Conduz uma revisão sistemática da literatura, que analisou 595 artigos de seis bases de dados (ACM, Engineering Village, IEEE, Material Science Engineering, Science Direct, Scopus e Web of Science). Resultados: Identifica 12 diferentes aplicações, principalmente na área de gestão de custos, orçamento da obra e segurança do trabalho. Nas aplicações, foram evidenciadas utilizações das tecnologias de Data Warehouse e OLAP. Verifica que a maioria das ferramentas de BI foram desenvolvidas para cada empresa em detrimento dos softwares comerciais. Conclusão: Existem diversas ferramentas de BI para a construção civil, com diferentes aplicações. A maioria dos softwares foram desenvolvidos para cada caso estudado devido às características únicas do setor da construção. A adoção em larga escala das ferramentas passe pela cooperação entre empresas, entidades de classe e universidades. Verifica limitações na pesquisa quanto à caracterização das empresas, devido à ausência desses dados nos artigos analisados. Sugere que os desafios de implementação das tecnologias e as limitações verificadas sejam abordados em estudos futuros.

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

OLAP and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. Although there are a wide variety of tools related to the OLAP approach, many implementations are performed in relational database systems (R-OLAP). So, all interrogation actions are performed through queries that must be reinterpreted in the SQL language. This translation has several consequences because SQL language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. This paper presents a multidimensional query language that allows expressing multidimensional queries directly over ROLAP databases. The implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. Currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database.

Development of cognitive approach to the organization of business processes in virtual machine-building enterprises within Industry 4.0

The paper sets out an approach to improving the efficiency of business processes at virtual machine-building enterprises (VMBE), which consists in improving the existing methodological and software tools to support the life cycle of products of such enterprises within Industry 4.0. In the approach, the concept of creating, deploying and implementing business processes at VMBE and principles of this concept, as well as methods for implementing the basic principles, are considered. The essence of the approach is to organize a full production cycle at VMBE, with almost complete absence of fixed assets at such an enterprise. This involves the fixed assets of subcontractors distributed within the country and, in some cases, abroad. The peculiarity of the approach lies in taking into account the specifics of the Industry 4.0 concept, the main feature of which is the individualization of production and consumption. Practical application of the discussed approach will ensure: the formation of VMBE marketing policy by creating a multi-agent web-basedenvironment; decision support for the adaptation of VMBE production to the current market conditions; alternative choice of subcontractors in the organization of virtual production using multi-agent technologies; information support for decisions on the feasibility of manufacturing VMBE products based on OLAP technology; adjustment of the life cycle of VMBE products by applying special information support methods and models for organizational management of VMBE. The effectiveness of the described approach is shown by analyzing the activities of a typical VMBE. The results of the study can be used to organize, deploy and support the effective functioning of virtual production enterprises in various industries

A Review on OLAP Technologies Applied to Information Networks

Many real systems produce network data or highly interconnected data, which can be called information networks. These information networks form a critical component in modern information infrastructure, constituting a large graph data volume. The analysis of information network data covers several technological areas, among them OLAP technologies. OLAP is a technology that enables multi-dimensional and multi-level analysis on a large volume of data, providing aggregated data visualizations with different perspectives. This article presents a literature review on the main applications of OLAP technology in the analysis of information network data. To achieve such goal, it shows a systematic review to list the works that apply OLAP technologies in graph data. It defines seven comparison criteria (Materialization, Network, Selection, Aggregation, Model, OLAP Operations, Analytics) to qualify the works found based on their functionalities. The works are analyzed according to each criterion and discussed to identify trends and challenges in the application of OLAP in the information network.