An adaptive and real-time based architecture for financial data integration

Fikri, Noussair; Rida, Mohamed; Abghour, Noreddine; Moussaid, Khalid; Omri, Amina El

doi:10.1186/s40537-019-0260-x

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…In the integration of UAV data, it mainly involves the preprocessing of UAV data, such as sag change processing, air belt deviation correction, and image stitching. e preprocessed UAV data, like other types of data, can be directly accessed by users through the same data management platform [16].…”

Section: Types Of Data Integrationmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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With the development of technology, more and more enterprises have begun to use data integration analysis to make decisions. The decision spatial information system is an information system that provides decision support after analyzing and processing the spatial information. Data integration refers to a method of reasonably centralizing different or multisource data for data sharing, which can assist in decision management. This paper aims to study a more efficient data integration method for decision space information system. In this paper, an emergency information decision-making system for digital water network is designed, and a specific data integration algorithm is proposed. And the performance test of the early warning capability and power consumption of the system is carried out. The results show that the early warning time of the system designed in this paper is within 1 to 4 seconds, while the early warning time of the traditional system is within 1.5 seconds to 8 seconds. The power consumption of the system designed in this paper is not more than 25 W, while the traditional system power consumption is up to 50 W. The power consumption of the system in this paper is about half of the traditional system. Compared with the traditional system early warning capability, the system designed in this paper is significantly faster, the system power consumption is lower, and the system is more stable.

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Section: Types Of Data Integrationmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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“…Also, the data may contain incorrect values, and the result of the analysis will be distorted, which also requires preliminary analysis and processing using Data Science tools. Fikri et al (2019) state that such work, as a rule, is taken over by pre-prepared ETL scripts, but their work also takes time.…”

Section: Problem Statementmentioning

confidence: 99%

OLAP Concept as a Basis for Business Analysis of Multidimensional Data Structures

Sokolov¹

2023

European Proceedings of Computers and Technology

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This paper reveals the concept of an OLAP technology stack for solving problems of Big Data multidimensional analysis as applied to business process reengineering of operational and tactical management of an organization based on incoming information about the current state of the external and internal environment of the organization. The description of the technology, its modifications, as well as the possibilities of the technology and application cases are given. OLAP technology allows you to quickly demonstrate the necessary aggregated indicators to the user, i.e., forms a certain data structure at the request of each user. The payment for the efficiency of providing aggregated data is the need for disk space, where additionally generated data tables will be stored. It should also be noted that the MDX language is considered as the query language for the hypercube data, not the SQL language, since it is the most suitable for working with multidimensional data structures. Application areas of OLAP technology are BI-systems, CRM-systems, and organization management systems.

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“…Parameter selection has been revealed as one of the most significant challenges especially in big data analysis tasks [25][26][27][28]. Manual fine-tuning of the parameters is inevitably time consuming and cannot achieve high accuracy in capricious industrial applications.…”

Section: Related Workmentioning

confidence: 99%

An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

Pang

Shang

2022

J Big Data

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A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices.

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An adaptive and real-time based architecture for financial data integration

Cited by 22 publications

References 23 publications

Data Integration Method Design of Decision Spatial Information System

Data Integration Method Design of Decision Spatial Information System

OLAP Concept as a Basis for Business Analysis of Multidimensional Data Structures

An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

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