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Journal articles on the topic 'Relational Online Analytical Processing'

Author: Grafiati

Published: 9 September 2021

Last updated: 29 July 2025

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1

Sucharitha, Shetty, Dinesh Rao B., and Prabhu Srikanth. "Growth of relational model: Interdependence and complementary to big data." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1780–95. https://doi.org/10.11591/ijece.v11i2.pp1780-1795.

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A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approac

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Jing, Changhong, Wenjie Liu, Jintao Gao, and Ouya Pei. "Research and implementation of HTAP for distributed database." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (2021): 430–38. http://dx.doi.org/10.1051/jnwpu/20213920430.

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Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterpri

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Alkharouf, Nadim W., D. Curtis Jamison, and Benjamin F. Matthews. "Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 181–88. http://dx.doi.org/10.1155/jbb.2005.181.

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Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest

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Hazboun, Fadi H., Majdi Owda, and Amani Yousef Owda. "A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube." AI 2, no. 4 (2021): 720–37. http://dx.doi.org/10.3390/ai2040043.

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Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse ob

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Redouane, Esbai, Hakkou Soufiane, and Achraf Habri Mohamed. "Modeling and automatic generation of data warehouse using model-driven transformation in business intelligence process." Modeling and automatic generation of data warehouse using model-driven transformation in business intelligence process 30, no. 3 (2023): 866–1874. https://doi.org/10.11591/ijeecs.v30.i3.pp1866-1874.

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The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation

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Lau, T. W., C. L. Hui, S. F. Ng, K. L. Moon, and G. Taylor. "An implication of e-commerce in textile and clothing industries: a relational Online Analytical Processing (OLAP) approach." International Journal of Information Technology and Management 7, no. 3 (2008): 231. http://dx.doi.org/10.1504/ijitm.2008.017603.

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Niemi, Timo, Turkka Näppilä, and Kalervo Järvelin. "A relational data harmonization approach to XML." Journal of Information Science 35, no. 5 (2009): 571–601. http://dx.doi.org/10.1177/0165551509104231.

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There are numerous approaches for integrating data from heterogeneous data sources. A common background assumption is that the data sources remain quite stable and are known in advance. Hence an integration system can be built to manipulate them. In practice there is, however, often a demand for supporting ad hoc information needs concerning unexpected autonomous data sources containing volatile data. A different approach is therefore needed. We propose that semantically similar data are harmonized when extracting data from XML-based data sources. We introduce a constructor algebra, which is a

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Esbai, Redouane, Soufiane Hakkou, and Mohamed Achraf Habri. "Modeling and automatic generation of data warehouse using model-driven transformation in business intelligence process." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1866. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1866-1874.

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The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation

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Martinez-Mosquera, Diana, Rosa Navarrete, Sergio Luján-Mora, Lorena Recalde, and Andres Andrade-Cabrera. "Integrating OLAP with NoSQL Databases in Big Data Environments: Systematic Mapping." Big Data and Cognitive Computing 8, no. 6 (2024): 64. http://dx.doi.org/10.3390/bdcc8060064.

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The growing importance of data analytics is leading to a shift in data management strategy at many companies, moving away from simple data storage towards adopting Online Analytical Processing (OLAP) query analysis. Concurrently, NoSQL databases are gaining ground as the preferred choice for storing and querying analytical data. This article presents a comprehensive, systematic mapping, aiming to consolidate research efforts related to the integration of OLAP with NoSQL databases in Big Data environments. After identifying 1646 initial research studies from scientific digital repositories, a t

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Martinez-Mosquera, Diana, Rosa Navarrete, Sergio Luján-Mora, Lorena Recalde, and Andres Andrade-Cabrera. "Integrating OLAP with NoSQL Databases in Big Data Environments: Systematic Mapping." Big Data and Cognitive Computing 8, no. 6 (2024): 1–29. https://doi.org/10.3390/bdcc8060064.

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The growing importance of data analytics is leading to a shift in data management strategy at many companies, moving away from simple data storage towards adopting Online Analytical Processing (OLAP) query analysis. Concurrently, NoSQL databases are gaining ground as the preferred choice for storing and querying analytical data. This article presents a comprehensive, systematic mapping, aiming to consolidate research efforts related to the integration of OLAP with NoSQL databases in Big Data environments. After identifying 1646 initial research studies from scientific digital repositories, a t

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Chakraborty, Sonali Ashish, and Jyotika Doshi. "An Approach for Retrieving Faster Query Results From Data Warehouse Using Synonymous Materialized Queries." International Journal of Data Warehousing and Mining 17, no. 2 (2021): 85–105. http://dx.doi.org/10.4018/ijdwm.2021040105.

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The enterprise data warehouse stores an enormous amount of data collected from multiple sources for analytical processing and strategic decision making. The analytical processing is done using online analytical processing (OLAP) queries where the performance in terms of result retrieval time is an important factor. The major existing approaches for retrieving results from a data warehouse are multidimensional data cubes and materialized views that incur more storage, processing, and maintenance costs. The present study strives to achieve a simpler and faster query result retrieval approach fro

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Fong, Joseph, Qing Li, and Shi-Ming Huang. "Universal Data Warehousing Based on a Meta-Data Modeling Approach." International Journal of Cooperative Information Systems 12, no. 03 (2003): 325–63. http://dx.doi.org/10.1142/s0218843003000772.

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Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements base

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Nuseir, Mohammed T. "Designing business intelligence (BI) for production, distribution and customer services: a case study of a UAE-based organization." Business Process Management Journal 27, no. 4 (2021): 1275–95. http://dx.doi.org/10.1108/bpmj-06-2020-0266.

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PurposeBusiness intelligence (BI) is a strategic approach that can use analytical tools to collect and integrate information, apply business rules and ensure the appropriate visible output of organizational information. This study aims to present the design and implementation of BI in areas of business process improvement for production, distribution and customer services.Design/methodology/approachThis study highlights the process of BI in the production, distribution and customer services based on the National Food Products Company (NFPC) in the United Arab Emirates (UAE). This study discuss

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Rahman, Md Arif, and Young-Koo Lee. "Forester: Approximate Processing of an Imperative Procedure for Query-Time Exploratory Data Analysis in a Relational Database." Electronics 13, no. 4 (2024): 759. http://dx.doi.org/10.3390/electronics13040759.

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Query-time Exploratory Data Analysis (qEDA) is an increasingly demanding aspect of the data analysis process that entails visually and quantitatively summarizing, comprehending, and interpreting the primary characteristics of a dataset. Nowadays, an imperative procedure is popular in relational databases for EDA because it enables us to write multiple dependent declarative queries with imperative logic. As online analytical processing (OLAP) systems contain extremely large datasets, data scientists often need quick visualizations of data, using approximate processing of imperative procedures,

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Mechri, Amel, Bilal Bouaita, Djamel Eddine Zegour, and Walid Khaled Hidouci. "Design and Implementation of a ROLAP Cube in Scalable Distributed Data Structure." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 20279–84. https://doi.org/10.48084/etasr.9648.

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The Scalable Distributed Data Structure (SDDS) is a data model specifically designed for distributed environments. An SDDS file comprises records that are dynamically distributed across servers using an SDDS algorithm. A notable feature of SDDS is the removal of a centralized addressing component, simplifying client-server communication and reducing both the message count and data access time in distributed systems. This work also explores a Data Warehouse (DW) within a decision support system, where multidimensional data are represented as a cube and managed through Relational Online Analytic

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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization." International Journal of Applied Evolutionary Computation 11, no. 3 (2020): 50–67. http://dx.doi.org/10.4018/ijaec.2020070104.

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A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of dat

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Davardoost, Farnaz, Amin Babazadeh Sangar, and Kambiz Majidzadeh. "An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier." Mathematical Problems in Engineering 2022 (April 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/2860735.

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Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classi

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Ahmed, Waqas, Esteban Zimányi, Alejandro Ariel Vaisman, and Robert Wrembel. "A Temporal Multidimensional Model and OLAP Operators." International Journal of Data Warehousing and Mining 16, no. 4 (2020): 112–43. http://dx.doi.org/10.4018/ijdwm.2020100107.

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Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical onli

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Putra, I. Made Suwija, and Dewa Komang Tri Adhitya Putra. "Rancang Bangun Engine ETL Data Warehouse dengan Menggunakan Bahasa Python." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 2 (2019): 113–23. http://dx.doi.org/10.29207/resti.v3i2.872.

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Big companies that have many branches in different locations often have difficulty with analyzing transaction processes from each branch. The problem experienced by the company management is the rapid delivery of massive data provided by the branch to the head office so that the analysis process of the company's performance becomes slow and inaccurate. The results of this process used as a consideration in decision making which produce the right information if the data is complete and relevant. The right method of massive data collection is using the data warehouse approach. Data warehouse is

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Segall, Richard S. "Transdisciplinary Applications of Data Visualization and Data Mining Techniques as Represented for Human Diseases." Journal of Systemics, Cybernetics and Informatics 22, no. 7 (2024): 6–15. https://doi.org/10.54808/jsci.22.07.6.

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Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an Ameri

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Han, Yuxuan, Yining Chen, and Yunxuan Zhang. "Design and Implementation of Information Management System for Enterprise Decision Support." SHS Web of Conferences 213 (2025): 01002. https://doi.org/10.1051/shsconf/202521301002.

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In today’s rapidly changing business environment, enterprise decision support systems are important tools for improving organizational decision-making efficiency and effectiveness. This paper explores designing and implementing an information management system for enterprise decision support. The system relies on modern information technology and integrates multiple functions such as data processing, model analysis, and intelligent decision-making, providing strong data support and decision recommendations for managers. Firstly, the paper analyzes the common problems encountered in the decisio

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Ahmed, Waqas, Esteban Zimányi, Alejandro A. Vaisman, and Robert Wrembel. "Schema Evolution in Multiversion Data Warehouses." International Journal of Data Warehousing and Mining 17, no. 4 (2021): 1–28. http://dx.doi.org/10.4018/ijdwm.2021100101.

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Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiv

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Vági, Renátó. "How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases?" TalTech Journal of European Studies 13, no. 2 (2023): 138–51. http://dx.doi.org/10.2478/bjes-2023-0018.

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Abstract The spread of online databases and the increasingly sophisticated search solutions in the past 10–15 years have opened up many new opportunities for lawyers to find relevant documents. However, it is still a common problem that the various legal databases and legal search engines face an information crisis. Legal database providers use various information extraction solutions, especially named entity recognition (NER), to mitigate this problem. These solutions can improve the relevance of the lists of results. Their limitation, however, is that they can only extract and create searcha

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Kasprzyk, Jean-Paul, and Guénaël Devillet. "A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions." ISPRS International Journal of Geo-Information 10, no. 2 (2021): 87. http://dx.doi.org/10.3390/ijgi10020087.

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Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and ti

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Alshehadeh, Abdul Razzak, Ghaleb Awad Elrefae, Abdelhafid K. Belarbi, Amer Qasim, and Haneen A. Al-Khawaja. "The impact of business intelligence tools on sustaining financial report quality in Jordanian commercial banks." Uncertain Supply Chain Management 11, no. 4 (2023): 1667–76. http://dx.doi.org/10.5267/j.uscm.2023.7.002.

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The objective of this study was to showcase the influence of Business Intelligence (BI) tools, such as Online Analytical Processing (OLAP), Extract, Transform, Load (ETL) processes, Data Mining (DM), Relational Database Management Systems (RDBMS), and Document Management Systems (DMS), on maintaining the quality of financial reports in Jordanian commercial banks listed on the Amman Stock Exchange. Two approaches were employed to achieve the research objectives: a descriptive-analytical approach involving the development of a questionnaire to gather primary data on the independent variables ass

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Bakurova, A., and V. Bilyi. "DEVELOPMENT OF AN INFORMATION AND ANALYTICAL SYSTEM FOR RECORDING DESTRUCTION DUE TO RUSSIAN AGGRESSION." Municipal economy of cities 3, no. 191 (2025): 2–10. https://doi.org/10.33042/2522-1809-2025-3-191-2-10.

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The article presents the development of an information and analytical system designed to document and evaluate the destruction caused by the war in Ukraine. With dozens of cities, hundreds of enterprises, and thousands of residential buildings damaged or destroyed, there is an urgent need for a centralized digital platform to collect, store, and analyze data for subsequent damage assessment and compensation. The primary objective of the project is to assist the Ukrainian government—both at the regional level and within individual communities—in identifying the areas most severely affected by R

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Vaisman, Alejandro, and Esteban Zimányi. "Mobility Data Warehouses." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 170. http://dx.doi.org/10.3390/ijgi8040170.

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The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processi

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Haxhiu, Valdrin. "Decision making based on data analyses using data warehouses." International Journal of Business & Technology 6, no. 3 (2018): 1–6. http://dx.doi.org/10.33107/ijbte.2018.6.3.04.

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Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a t

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Spreadborough, Kristal, and Solange Glasser. "A literature review on the use of retrospective LMS data to investigate online Teaching and Learning practices." Pacific Journal of Technology Enhanced Learning 4, no. 1 (2022): 12–13. http://dx.doi.org/10.24135/pjtel.v4i1.131.

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Access to high quality education is a cornerstone of social, cultural, and economic recovery after any crisis. This is also true of the global COVID-19 pandemic which has disrupted the pedagogical practices of higher education institutions around the world (Butler-Henderson, Crawford, Rudolph, Lalani, & Sabu, 2020). Digital learning has become the new-norm, and tertiary education institutions have been propelled to innovate their teaching methods by integrating digital learning through the adoption of cost-effective (Al-Maroof & Al-Emran, 2018) and adaptable (Bhat et al., 2018) Learnin

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Sharma, Pitambar, and Piyush Girdhar. "Online Analytical Processing (OLAP)." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (2014): 01–04. http://dx.doi.org/10.53555/nncse.v1i3.520.

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This paper is basically accustomed define On-Line Analytical method (OLAP), WHO uses it and why, and to review the key choices required for OLAP code. On-Line Analytical method (OLAP) could also be a category of code technology that allows analysts, managers and executives to appreciate insight into info through fast, consistent, interactive access to an honest reasonably gettable views of {data of information} that has been transformed from data to mirror spatiality of the enterprise as understood by the user. whereas OLAP systems have the ability to answer "who?" and "what?" queries, it's th

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Lowe, Robert, Alexander Almér, and Christian Balkenius. "Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing." Open Information Science 3, no. 1 (2019): 235–60. http://dx.doi.org/10.1515/opis-2019-0017.

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Abstract Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanism

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Peng, Yuchen, Ke Chen, Lidan Shou, Dawei Jiang, and Gang Chen. "AQUA: Automatic Collaborative Query Processing in Analytical Database." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4006–9. http://dx.doi.org/10.14778/3611540.3611607.

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Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an op

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Chou, Chung-Hsien, Masahiro Hayakawa, Atsushi Kitazawa, and Phillip Sheu. "GOLAP: Graph-Based Online Analytical Processing." International Journal of Semantic Computing 12, no. 04 (2018): 595–608. http://dx.doi.org/10.1142/s1793351x18500071.

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Graph-based Online Analytical Processing (GOLAP) extends Online Analytical Processing (OLAP) to address graph-based problems that involve object attributes. Based on graph data, GOLAP can answer user queries related to combinatorial optimization, structural analytics, and influence analytics. Besides, since a GOLAP system is an online interactive system that requires fast response time, the execution time for graph-problem queries is essentially critical. Thus, how to speed up the execution time of specific graph problems becomes a challenge in GOLAP. In this paper, we show several methods to

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Chung, Gregory. "Toward the Relational Management of Educational Measurement Data." Teachers College Record: The Voice of Scholarship in Education 116, no. 11 (2014): 1–16. http://dx.doi.org/10.1177/016146811411601115.

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Background Historically, significant advances in scientific understanding have followed advances in measurement and observation. As the resolving power of an instrument increased, so have gains in the understanding of the phenomena being observed. Modern interactive systems are potentially the new “microscopes” when they are instrumented to record fine-grained observations of what students do in an online task. Advances in the conceptualization, design, and analyses of such interaction data enable the discovery of learning patterns and can power new applications. One application, personalizati

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Brisini, Kellie St Cyr, Denise Haunani Solomon, and Jon Nussbaum. "Transitions in marriage." Journal of Social and Personal Relationships 35, no. 6 (2017): 831–53. http://dx.doi.org/10.1177/0265407517699283.

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This study examines transitions in marriage by merging the frameworks of the relational turbulence model and the experiencing life transitions model. An online survey presented 208 married individuals with open-ended questions and closed-ended scales that gathered information about a particularly important transition in the respondent’s marriage, the quality of their relationship during the transition, and the strategies used to navigate the transition. Analyses, which focused on 157 participants who reported experiencing a transition in their marriage, revealed 10 categories of transitions, t

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Haugh, Michael, Wei-Lin Melody Chang, and Dániel Z. Kádár. "“Doing deference”." Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA) 25, no. 1 (2015): 73–98. http://dx.doi.org/10.1075/prag.25.1.04hau.

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In this paper we examine a key relational practice found in interactions in online discussion boards in Mainland China and Taiwan: ‘doing deference’. In drawing attention to a relational practice that has received attention in quite different research traditions, namely, linguistic pragmatics and conversation analysis (CA), we mean to highlight the possible advantages of an approach to analysis that draws from both in analysing relational work in CMC. We claim in the course of our analysis that the participants are orienting not only to relationships but also to identities through this practic

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Liu, J., W. K. Cham, and M. M. Y. Chang. "Online Chinese character recognition using attributed relational graph matching." IEE Proceedings - Vision, Image, and Signal Processing 143, no. 2 (1996): 125. http://dx.doi.org/10.1049/ip-vis:19960326.

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Nasseri, Mohammad Hossein, Mohammadreza Babaee, Hadi Moradi, and Reshad Hosseini. "Online relational tracking with camera motion suppression." Journal of Visual Communication and Image Representation 90 (February 2023): 103750. http://dx.doi.org/10.1016/j.jvcir.2022.103750.

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Wijaya, Andri, Mutia Maharani, and Meilinda. "IMPLEMENTASI PENDEKATAN AGILE UNTUK PENGEMBANGAN OLAP DATA PENJUALAN." ZONAsi: Jurnal Sistem Informasi 6, no. 1 (2024): 222–31. http://dx.doi.org/10.31849/zn.v6i1.17337.

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Sales data in large quantities are difficult to process and report using Microsoft Excel, which takes a long time. The proposed solution is to use Online Analytical Processing, a method that allows for faster decision-making through multidimensional data manipulation. In developing Online Analytical Processing for sales data, an agile approach is applied. With Online Analytical Processing, access to and display of transactional data becomes more efficient, improving analysis quality and supporting management decisions. Research results show that the prototype accelerates sales reports with a r

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Sainath, Muvva. "Comparative Analysis of Apache Sqoop and Apache Spark for Efficient Data Transfer Between Relational Databases and Hadoop Distributed File System (HDFS)." International Journal on Science and Technology 11, no. 3 (2020): 1–6. https://doi.org/10.5281/zenodo.14288579.

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With the growing adoption of big data technologies like Hadoop, many companies are overhauling their data infrastructure. A crucial aspect of this transition is the ability to transfer both transactional and analytical data from traditional relational database management systems (RDBMS) into the new ecosystem. This migration enables advanced data processing and facilitates deeper analytical insights. This paper focuses on exploring the various tools available for importing data from relational databases into the Hadoop Distributed File System (HDFS). It delves into the underlying mechanisms of

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Ranawade, Supriya Vitthal, Shivani Navale, Akshay Dhamal, Kuldeep Deshpande, and Chandrashekhar Ghuge. "Online Analytical Processing on Hadoop using Apache Kylin." International Journal of Applied Information Systems 12, no. 2 (2017): 1–5. http://dx.doi.org/10.5120/ijais2017451682.

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HE, Zhao-qing, Pan ZHOU, and Ke-hua YANG. "Community-partition-based online analytical processing query optimization." Journal of Computer Applications 30, no. 12 (2011): 3371–73. http://dx.doi.org/10.3724/sp.j.1087.2010.03371.

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Giannopoulos, Konstantinos, and Basilis Boutsinas. "Tourism Satellite Account Support Using Online Analytical Processing." Journal of Travel Research 55, no. 1 (2014): 95–112. http://dx.doi.org/10.1177/0047287514538836.

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Budianto, Galih. "Data Warehouse Modeling Using Online Analytical Processing Approach." Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) 1, no. 1 (2022): 7–13. http://dx.doi.org/10.58602/jima-ilkom.v1i1.2.

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Increasing business needs affect business competition in many companies that utilize information technology. The competition aims to gain advantages that utilize information technology in facilitating business processes. One of the conveniences offered is the use of information technology to support decision making in carrying out existing business processes in middle-to-upper scale companies that have large amounts of data. Online Analysis Processing (OLAP) is an approach method to present answers to the demand for a dimensional analysis process quickly, namely the design of applications and

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Nabibayova, Gulnara. "Expanding the intellectual capabilities of OLAP technology using neural networks." Problems of Information Society 15, no. 2 (2024): 43–48. http://dx.doi.org/10.25045/jpis.v15.i2.05.

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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 strat

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Sugiono, Ahmad, Agus Rahayu, Lili Adi Wibowo, and Ratih Hurriyati. "Analysis of Incoterms and Relational Resources to Improve Competitive Advantage: A Study of Freight Forwarders Company in Indonesia." Asian Journal of Logistics Management 2, no. 2 (2023): 64–91. http://dx.doi.org/10.14710/ajlm.2023.20288.

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This study aims to analyze the impact of Incoterm and relational resources on the competitive advantage of Indonesian Freight forwarders. The measurement methodology uses structural equation model (SEM) analysis with SmartPLS software to analyze the impact of Incoterms and relational resources on competitive advantage. Through social media, online questionnaires were sent to gather research data. The Likert scale of 5 was used to create the questionnaire. Companies that provide freight forwarding services were the respondents in this study, chosen by simple random sampling. Online questionnair

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Rama Krishna Jujjavarapu. "Employing AWS cloud technologies for enhanced scalability in data modeling: A comparative analysis of relational versus dimensional strategies." World Journal of Advanced Research and Reviews 19, no. 1 (2023): 1569–79. https://doi.org/10.30574/wjarr.2023.19.1.1410.

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This paper provides a detailed comparative analysis of relational and dimensional data modeling strategies, utilizing AWS Cloud technologies to enhance scalability and performance. Relational data models are traditionally used for transactional systems, while dimensional models are more suited for analytical processing and business intelligence tasks. In this study, we used Amazon Redshift and AWS Glue to test the scalability of both strategies on large datasets. We evaluated the performance of each model by comparing query execution times, storage efficiency, and data accessibility. Results s

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Tankoano, Joachim. "Multi-model Database Design and Query Processing in NewSQL DBMSs." American Journal of Computer Science and Technology 8, no. 2 (2025): 52–71. https://doi.org/10.11648/j.ajcst.20250802.12.

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NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise&apos;s operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrit

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Gamezardashvili, Zaza, Giorgi Ghlonti, and Hakan Ergun. "About One NoSQL Mechanism For Accessing Panel Data." Journal of Technical Science and Technologies 7, no. 2 (2018): 7–13. http://dx.doi.org/10.31578/jtst.v7i2.140.

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In the presented paper, the problems connected with the support of the life cycle of information resource represented by panel data are considered. An example of a logical markup of a statistical document emerged at the stage of information resource planning is given, as well as a relational formalism describing the structure of the statistical table included in the document. Examples of NoSQL statements for data manipulation and processing, based on this relational formalism are presented. The problem of building a cyber infrastructure intended for the accumulation and distribution of an anal

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Taufik, M., F. Renaldi, and F. R. Umbara. "Implementing Online Analytical Processing in Hotel Customer Relationship Management." IOP Conference Series: Materials Science and Engineering 1115, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1757-899x/1115/1/012040.

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