A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making
Keywords:data models, multidimensional model, OLAP, decision making, query languages
AbstractOLAP 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.
Bojicic, I.; Marjanovic, Z.; Turajlic, N.; Petrovic, M.; Vuckovic, M.; Jovanovic (2016). A comparative analysis of data warehouse data models, In 2016 6th International Conference on Computers Communications and Control (ICCCC), Proceedings of, IEEE, 151-159, 2016. https://doi.org/10.1109/ICCCC.2016.7496754
Bojicic, I.; Marjanovic, Z.; Turajlic, N.; Petrovic, M.; Vuckovic, M.; Jovanovic, V.(2017). Domain/ Mapping Model: A Novel DataWarehouse Data Mode, International Journal of Computers Communications & Control, 12(2), 166-182, 2017. https://doi.org/10.15837/ijccc.2017.2.2876
Bouaziz, S.; Nabli, A.; Gargouri, F. (2019). Design a Data Warehouse Schema from Document Oriented database, Procedia Computer Science, 159, 221-230, 2019. https://doi.org/10.1016/j.procs.2019.09.177
Boukra, D.; BoussaÃ¯d, O.; Bentayeb, F. (2010). OLAP operators for complex object data cubes, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6295 LNCS, 103-116, 2010. https://doi.org/10.1007/978-3-642-15576-5_10
Codd, E. F. (1970). A relational model of data for large shared data banks, Communications of the ACM, 13(6), 377-387, 1970. https://doi.org/10.1145/362384.362685
Colomo-Palacios, R.; Fernandes, E.; Soto-Acosta, P.; Larrucea, X. (2018). A case analysis of enabling continuous software deployment through knowledge management, International Journal of Information Management, 36(1), 142-154, 2016.
Cuzzocrea, A.; Moussa, R. (2017). Multidimensional database modeling: Literature survey and research agenda in the big data era, 2017 International Symposium on Networks, Computers and Communications (ISNCC), 1-6, 2017. https://doi.org/10.1109/ISNCC.2017.8072024
Eroshkin, S. Y.; Kameneva, N.; Kovkov, D.; Sukhorukov, A(2017). Conceptual system in the modern information management, Procedia Computer Science, 103(C), 609-612, 2017. https://doi.org/10.1016/j.procs.2017.01.079
Golfarelli, M.; Rizzi, S. (1999). Designing the Data Warehouse: Key Steps and Crucial Issues, Journal of Computer Science and Information Management, 2(3), 1-14, 1999.
Hümmer, W.; Lehner, W.; Bauer, A.; Schlesinger, L. (2002). A Decathlon in Multidimensional Modeling: Open Issues and Some Solutions, Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery, 275-285, 2002. https://doi.org/10.1007/3-540-46145-0_27
Jaroli, P.; Masson, P. (2012). Data Warehousing and OLAP Technology (Data warehousing), International Journal of Engineering Trends and Technology, 2, 955-960, 2012.
Moole, B. R. (2003). A Probabilistic Multidimensional Data Model and Algebra for OLAP in Decision Support Systems, Conf. Proceedings - IEEE SOUTHEASTCON, 18-30, 2003. https://doi.org/10.1109/SECON.2003.1268426
Pablo, P. V. (2016). Business intelligence applied to monitoring and meta-monitoring scenarios, In 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), 1-6, 2016. https://doi.org/10.1109/CISTI.2016.7521536
Palominos, F. E.; Duran, C. A.; CÃ³rdova, F. M. (2018). Multidimensional data model for the analysis of information of productive, scientific or service processes, In 7th International Conference on Computers Communications and Control, IEEE, 17-22, 2018. https://doi.org/10.1109/ICCCC.2018.8390431
Palominos, F. E.; Duran, C. A.; CÃ³rdova, F. M. (2019). Improve efficiency in multidimensional database queries through the use of additives aggregation functions, Procedia Computer Science, 162, 754-761, 2019. https://doi.org/10.1016/j.procs.2019.12.047
Piasevoli, T.; Li, S. (2016). MDX with Microsoft SQL Server 2016 Analysis Services cookbook, 2016.
Rahimi, F.; MÃ¸ller, C.; Hvam, L (2016). Business process management and IT management: The missing integration, International Journal of Information Management, 40, 186-189, 2018.
Taleb, A.; Eavis, T.; Tabbara, H. (2011). The NOX OLAP query model: From algebra to execution, Lecture Notes in Computer Science, 6862 LNCS, 167-183, 2011. https://doi.org/10.1007/978-3-642-23544-3_13
Zhang, X. (2018). Design of Intelligent Management Decision Support System for Retailing Chains, In 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Proceedings of, 485-489, 2018. https://doi.org/10.1109/ICVRIS.2018.00125
Zykin, S. V.; Mosin S. V.; Poluyanov A. N. (2019). Technology of Multidimensional Data Formation Using Caching, 13th International IEEE Scientific and Technical Conference Dynamics of Systems, Mechanisms and Machines, Dynamics 2019 - Proceedings, 1-10, 2019. https://doi.org/10.1109/Dynamics47113.2019.8944579
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.