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Journal articles on the topic 'Data warehousing'

To see the other types of publications on this topic, follow the link: Data warehousing.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Jaroli, Priyanka, and Palak Masson. "Data Warehousing and OLAP Technology (Data warehousing)." International Journal of Engineering Trends and Technology 51, no. 1 (September 25, 2017): 45–50. http://dx.doi.org/10.14445/22315381/ijett-v51p208.

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2

Griffin, Robert K. "Data Warehousing." Cornell Hotel and Restaurant Administration Quarterly 39, no. 4 (August 1998): 28–35. http://dx.doi.org/10.1177/001088049803900406.

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3

Hoven, John van den. "Data Warehousing." Information Systems Management 14, no. 1 (January 1997): 70–72. http://dx.doi.org/10.1080/10580539708907035.

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4

Daniels, Shirley. "Data warehousing." Work Study 44, no. 7 (November 1995): 4–5. http://dx.doi.org/10.1108/00438029510096526.

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5

Cole, Karen, Michael Somers, and Jill Emery. "Data Warehousing." Serials Librarian 40, no. 3-4 (June 20, 2001): 349–53. http://dx.doi.org/10.1300/j123v40n03_22.

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6

Kumar Madria, Sanjay. "Data warehousing." Data & Knowledge Engineering 39, no. 3 (December 2001): 215–17. http://dx.doi.org/10.1016/s0169-023x(01)00040-4.

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7

Ferguson, Neil. "Data Warehousing." International Review of Law, Computers & Technology 11, no. 2 (October 1997): 243–50. http://dx.doi.org/10.1080/13600869755686.

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8

Tan, Xin, David C. Yen, and Xiang Fang. "Web warehousing: Web technology meets data warehousing." Technology in Society 25, no. 1 (January 2003): 131–48. http://dx.doi.org/10.1016/s0160-791x(02)00061-1.

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9

S., ARUNACHALAM, PAGE TOM, and THORSTEINSSON G. "Healthcare Data Warehousing." i-manager's Journal on Computer Science 4, no. 4 (2017): 1. http://dx.doi.org/10.26634/jcom.4.4.13414.

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10

Alshawi, Sarmad. "Oracle8i Data Warehousing." European Journal of Information Systems 10, no. 1 (March 2001): 67. http://dx.doi.org/10.1057/palgrave.ejis.3000356.

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11

CALVANESE, DIEGO, GIUSEPPE DE GIACOMO, MAURIZIO LENZERINI, DANIELE NARDI, and RICCARDO ROSATI. "DATA INTEGRATION IN DATA WAREHOUSING." International Journal of Cooperative Information Systems 10, no. 03 (September 2001): 237–71. http://dx.doi.org/10.1142/s0218843001000345.

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Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

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12

Le, D. Xuan, J. Wenny Rahayu, and David Taniar. "Web Data Warehousing Convergence." International Journal of Information Technology and Web Engineering 1, no. 4 (October 2006): 68–80. http://dx.doi.org/10.4018/jitwe.2006100105.

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13

Onions, T. "Review: Oracle8 Data Warehousing." Computer Bulletin 41, no. 2 (March 1, 1999): 30–31. http://dx.doi.org/10.1093/combul/41.2.30-c.

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14

Fabian, Benjamin, and Tom Göthling. "Privacy-preserving data warehousing." International Journal of Business Intelligence and Data Mining 10, no. 4 (2015): 297. http://dx.doi.org/10.1504/ijbidm.2015.072210.

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15

Sen, Arun, and Varghese S. Jacob. "Industrial-strength data warehousing." Communications of the ACM 41, no. 9 (September 1998): 28–31. http://dx.doi.org/10.1145/285070.285076.

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16

Lin, Xiangming, Kai Liu, and Yixuan Li. "BI warehousing system based on big data." E3S Web of Conferences 257 (2021): 02015. http://dx.doi.org/10.1051/e3sconf/202125702015.

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In the wave of informatization, the data generated by enterprise operations has increased rapidly, prompting the intelligent development of enterprise warehousing systems. In the development of BI warehousing systems, the application of big data technology can promote the rapid development of business intelligence warehousing systems. The application of big data technology in the BI warehousing system can improve the service quality of the data intelligence of the warehousing system. Based on data, it provides support for corresponding decision-making, thereby improving the enterprise data management system. Therefore, this article mainly conducts research and analysis on the construction of BI warehousing system under the application of big data technology, and aims to provide a certain reference value for similar events in the future through a detailed explanation of the current situation of BI warehousing system construction and big data technology application.

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17

Miranda, Eka, and Natalya Elfreida. "Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk." ComTech: Computer, Mathematics and Engineering Applications 1, no. 2 (December 1, 2010): 344. http://dx.doi.org/10.21512/comtech.v1i2.2368.

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This paper is about designing and implementing data warehousing and data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The database the company used is not supporting data analysis and decision-making. Therefore, it made a data warehousing design that could be used to keep data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement data warehousing and data mining which consists of literature study, company problem analysis, and data warehousing design, and testing result. The writing results are a data warehousing design and data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation data. The data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.

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18

Rorimpandey, G. C., F. I. Sangkop, V. P. Rantung, J. P. Zwart, O. E. S. Liando, and A. Mewengkang. "Data Model Performance in Data Warehousing." IOP Conference Series: Materials Science and Engineering 306 (February 2018): 012044. http://dx.doi.org/10.1088/1757-899x/306/1/012044.

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19

Sasmal, Shubhodip. "Data Warehousing Revolution: AI-driven Solutions." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 12, no. 1 (2024): 01–06. http://dx.doi.org/10.55083/irjeas.2024.v12i01001.

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The contemporary landscape of data warehousing is undergoing a revolutionary transformation propelled by the integration of Artificial Intelligence (AI). This paper explores the intersection of AI and data warehousing, unraveling the dynamics that fuel this revolution and examining the profound implications for businesses and data management practices. The traditional paradigm of data warehousing has relied on structured data and predefined schemas, limiting its adaptability to the dynamic nature of modern datasets. The advent of AI injects a new dimension, enabling data warehouses to evolve into intelligent, adaptive entities capable of handling diverse data types, volumes, and velocities. This abstract encapsulates the essence of the research, delving into key themes that define the AI-driven revolution in data warehousing. The paper begins by surveying the historical trajectory of data warehousing, highlighting the challenges posed by the increasing complexity and heterogeneity of contemporary data sources. As businesses grapple with unstructured data, streaming data, and the need for real-time insights, the limitations of traditional data warehousing architectures become apparent. The introduction of AI-driven solutions revolutionizes data warehousing in several dimensions. Machine learning algorithms are harnessed for automating data integration, cleansing, and transformation processes, mitigating the manual labor associated with traditional ETL (Extract, Transform, Load) methods. Deep learning techniques, such as neural networks, unlock the potential to uncover complex patterns within massive datasets, enhancing predictive analytics and decision support capabilities. Moreover, the abstract explores the role of AI in enabling self-optimizing data warehouses. Adaptive query optimization, automated indexing, and real-time performance tuning emerge as pivotal components, ensuring that data warehouses evolve in response to changing workloads and user patterns. Ethical considerations and responsible AI practices within the context of data warehousing are also addressed. The abstract concludes by underlining the transformative impact of AI-driven solutions on the efficiency, agility, and strategic value of data warehousing, offering a glimpse into the future where intelligent data warehouses play a central role in shaping data-driven enterprises.

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20

Marketos, Gerasimos, Yannis Theodoridis, and Ioannis S. Kalogeras. "Seismological Data Warehousing and Mining." International Journal of Data Warehousing and Mining 4, no. 1 (January 2008): 1–16. http://dx.doi.org/10.4018/jdwm.2008010101.

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21

Bhardwaj, Vinayak, and Rincy Jacob. "Data Warehousing and OLAP Technology." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (March 31, 2014): 05–11. http://dx.doi.org/10.53555/nncse.v1i3.521.

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Data warehousing and Online Analytical Processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Data warehouse provides an effective way for the analysis and tatic to the mass data and helps to do the decision making. Many commercial products and services are now available and all of the principal database management system vendors now have offering in these areas. The paper introduces the data warehouse and online analysis process with an accent on their new requirements.

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22

Abimbola, Bola. "Trends in Data Warehousing Technology." International Journal of Data Science and Big Data Analytics 1, no. 3 (November 5, 2021): 15. http://dx.doi.org/10.51483/ijdsbda.1.3.2021.15-21.

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23

Rahman, Nayem, Dale Rutz, and Shameem Akhter. "Agile Development in Data Warehousing." International Journal of Business Intelligence Research 2, no. 3 (July 2011): 64–77. http://dx.doi.org/10.4018/jbir.2011070105.

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Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.

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24

Jukic, Nenad, and Boris Jukic. "Modeling-Centered Data Warehousing Learning." International Journal of Business Intelligence Research 3, no. 4 (October 2012): 74–95. http://dx.doi.org/10.4018/jbir.2012100104.

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Though data warehousing is widely recognized in the industry as the principal decision support system architecture and an integral part of the corporate information system, the majority of academic institutions in the US and world-wide have been slow in developing curriculums that reflect this. The authors examine the issues that have contributed to the lag in the coverage of data warehousing topics at universities and introduce methods, concepts and resources that can enable business educators to deal with these issues and conduct comprehensive, detailed, and meaningful coverage of data warehouse related topics.

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25

Kryukov, A., and A. Demichev. "Decentralized data warehousing: building technologies." Программирование, no. 5 (October 2018): 12–30. http://dx.doi.org/10.31857/s013234740001212-8.

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26

Attaf, Sarah, Nadjia Benblidia, and Omar Boussaid. "Warehousing and Analyzing Textual Data." International Journal of Mathematical Modelling and Numerical Optimisation 8, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijmmno.2017.10006816.

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27

Attaf, Sarah, Nadjia Benblidia, and Omar Boussaid. "Warehousing and analysing textual data." International Journal of Mathematical Modelling and Numerical Optimisation 8, no. 3 (2018): 216. http://dx.doi.org/10.1504/ijmmno.2018.088972.

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28

Gallegos, Frederick. "Data Warehousing: A Strategic Approach." Information Strategy: The Executive's Journal 16, no. 1 (September 1999): 41–47. http://dx.doi.org/10.1080/07438613.1999.10744617.

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29

Gray, Paul. "Mining for Data Warehousing Gems." Information Systems Management 14, no. 1 (January 1997): 82–86. http://dx.doi.org/10.1080/10580539708907038.

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30

Merritt, Kimberly. "Data Warehousing and the Internet." Journal of Internet Commerce 1, no. 2 (March 2002): 49–61. http://dx.doi.org/10.1300/j179v01n02_04.

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31

Watson, Hugh J., David A. Annino, Barbara H. Wixom, K. Liddell Avery, and Mathew Rutherford. "Current Practices in Data Warehousing." Information Systems Management 18, no. 1 (January 2001): 47–55. http://dx.doi.org/10.1201/1078/43194.18.1.20010101/31264.6.

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32

Watson, Hugh, Thilini Ariyachandra, and Robert J. Matyska. "Data Warehousing Stages of Growth." Information Systems Management 18, no. 3 (June 2001): 42–50. http://dx.doi.org/10.1201/1078/43196.18.3.20010601/31289.6.

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33

Schonbach, C., P. Kowalski-Saunders, and V. Brusic. "Data warehousing in molecular biology." Briefings in Bioinformatics 1, no. 2 (January 1, 2000): 190–98. http://dx.doi.org/10.1093/bib/1.2.190.

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34

Watson, Hugh J., and Ronald S. Swift. "Data Warehousing Around the World." Journal of Global Information Technology Management 5, no. 2 (April 2002): 1–6. http://dx.doi.org/10.1080/1097198x.2002.10856322.

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35

Subramanian, Ashok, L. Douglas Smith, Anthony C. Nelson, James F. Campbell, and David A. Bird. "Strategic planning for data warehousing." Information & Management 33, no. 2 (December 1997): 99–113. http://dx.doi.org/10.1016/s0378-7206(97)00040-2.

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36

Jensen, Christian S., Torben Bach Pedersen, and Christian Thomsen. "Multidimensional Databases and Data Warehousing." Synthesis Lectures on Data Management 2, no. 1 (January 2010): 1–111. http://dx.doi.org/10.2200/s00299ed1v01y201009dtm009.

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37

Tschandl, Martin, and Wolfgang Hergolitsch. "Die Einführung von Data Warehousing." Controlling 14, no. 2 (2002): 99–110. http://dx.doi.org/10.15358/0935-0381-2002-2-99.

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38

Arif, Muhammad, and Faheem Zaffar. "Challenges in Efficient Data Warehousing." International Journal of Grid and Distributed Computing 8, no. 2 (April 30, 2015): 37–48. http://dx.doi.org/10.14257/ijgdc.2015.8.2.05.

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39

Vetterli, Thomas, Anca Vaduva, and Martin Staudt. "Metadata standards for data warehousing." ACM SIGMOD Record 29, no. 3 (September 2000): 68–75. http://dx.doi.org/10.1145/362084.362138.

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40

Georgiev, Angel, and Vladimir Valkanov. "Custom data quality mechanism in Data Warehouse facilitated by data integrity checks." Mathematics and Education in Mathematics 53 (March 16, 2024): 67–75. http://dx.doi.org/10.55630/mem.2024.53.067-075.

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In the era of data-driven decision-making, Data Warehousing (DWH) is crucial for organizations seeking to leverage extensive datasets. However, the success of DWH initiatives depends on the quality of the enclosed data. Insuﬃcient quality data in Data Warehousing can impact the accuracy of analytical results, leading to misguided decisions and reduced business performance. This paper examines the signiﬁcance of Data Quality Mechanisms in addressing challenges related to data quality. Data Quality Mechanisms play a crucial role in identifying, rectifying, and preventing data quality issues throughout the data lifecycle. This paper explores fundamental concepts, challenges, and impacts of data quality on business operations. It emphasizes the critical role of robust Data Quality Mechanisms in ensuring the accuracy, completeness, and reliability of data within the Data Warehousing ecosystem. As organizations increasingly recognize data as a strategic asset, it is imperative to implement eﬀective data quality mechanisms to unlock the true potential of data warehouses and derive actionable insights.

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41

Ahmadi, Sina. "Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (December 12, 2023): 282–301. http://dx.doi.org/10.60087/jklst.vol2.n3.p301.

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This research focuses on the development of elastic data warehousing while adapting to changing workloads with the help of cloud-based technologies. The traditional methods of data warehousing need innovative and creative strategies in order to improve their efficiency. Thus, this research focuses on analyzing innovative methods which can improve the future of data warehousing, such as machine learning, encryption, artificial intelligence, etc. Moreover, the study also focuses on specific industries that require customized solutions to data warehousing. These include the manufacturing, finance, and healthcare industries. The study uses qualitative data gained through a comprehensive review of literature. The findings reveal a great level of significance of modern data warehousing techniques that assist in improving the overall efficiency of traditional methods

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42

Bhoite, Dr Sudhakar. "Decision Making Using Data Warehousing Technology: A Strategic Tool." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 84–86. http://dx.doi.org/10.15373/22778179/aug2013/26.

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43

Agustian, Indra, Eka Muktiono, Akhmat Nuryadin, and Bambang Suharjo. "WAREHOUSHING AND INVENTORY INFORMATION SYSTEM MODEL USING RADIO FREQUENCY IDENTIFICATION (RFID) IN EASTERN NAVAL WAREHOUSING." JOURNAL ASRO 10, no. 2 (July 24, 2019): 31. http://dx.doi.org/10.37875/asro.v10i2.127.

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Eastern Naval Warehousing is the technical implementation elements in charge of receiving, storing, maintaining and distributing supplies to support existing user in the Navy in particular user located in the eastern region. To support the operational activities of the Navy, Eastern Naval Warehousing as service organizations should be supported with the provision of an information system capable of supporting the activities become more optimal provisioning. Speed and accuracy of support provision to user is an indication for the success of a service. Information technology nowadays has grown very rapidly to develop into the current warehousing system. RFID (Radio Frequency Identification) is the process of identifying an object automatically by Radio Frequency. There are two important components of an RFID system is the card (tag) and the reader. Application warehousing information system can be integrated with RFID technology, so that the system can run more optimal for warehousing system. This research aims to design warehousing information system based on RFID in Eastern Naval Warehousing used in processing existing warehousing system to gain optimal in provisioning operations running in Eastern Naval Warehousing to support provisioning activities to meet the requirement of existing user in Navy. Field studies conducted in Eastern Naval Warehousing, aimed to determine the current state of warehousing system that runs to identify existing problems. Furthermore, to process the data, created the design of an information system based on RFID warehousing in software system. This paper get to report out of the goods, report the amount of inventory in real time, the location of goods and EOQ value of goods to determine the optimal inventory level of each item.

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44

Golfarelli, Matteo, and Stefano Rizzi. "A Survey on Temporal Data Warehousing." International Journal of Data Warehousing and Mining 5, no. 1 (January 2009): 1–17. http://dx.doi.org/10.4018/jdwm.2009010101.

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45

S, Vishesh, Manu Srinath, Akshatha C. Kumar, and Nandan A.S. "Data Warehousing Architecture and Pre-Processing." IJARCCE 6, no. 5 (May 30, 2017): 13–18. http://dx.doi.org/10.17148/ijarcce.2017.6503.

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46

Jukic, Nenad, and Miguel Velasco. "Data Warehousing Requirements Collection and Definition." International Journal of Business Intelligence Research 1, no. 3 (July 2010): 66–76. http://dx.doi.org/10.4018/jbir.2010070105.

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Defining data warehouse requirements is widely recognized as one of the most important steps in the larger data warehouse system development process. This paper examines the potential risks and pitfalls within the data warehouse requirement collection and definition process. A real scenario of a large-scale data warehouse implementation is given, and details of this project, which ultimately failed due to inadequate requirement collection and definition process, are described. The presented case underscores and illustrates the impact of the requirement collection and definition process on the data warehouse implementation, while the case is analyzed within the context of the existing approaches, methodologies, and best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.

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47

Little, R. G., and M. L. Gibson. "Perceived influences on implementing data warehousing." IEEE Transactions on Software Engineering 29, no. 4 (April 2003): 290–96. http://dx.doi.org/10.1109/tse.2003.1191794.

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48

McCusker, James P., Joshua A. Phillips, Alejandra Beltrán, Anthony Finkelstein, and Michael Krauthammer. "Semantic web data warehousing for caGrid." BMC Bioinformatics 10, Suppl 10 (2009): S2. http://dx.doi.org/10.1186/1471-2105-10-s10-s2.

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49

Ramamurthy, K., A. Sen, and A. P. Sinha. "Data Warehousing Infusion and Organizational Effectiveness." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 38, no. 4 (July 2008): 976–94. http://dx.doi.org/10.1109/tsmca.2008.923032.

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50

Ma, Catherine, David C. Chou, and David C. Yen. "Data warehousing, technology assessment and management." Industrial Management & Data Systems 100, no. 3 (April 2000): 125–35. http://dx.doi.org/10.1108/02635570010323193.

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