پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 709 |
| تعداد مقالات | 10,213 |
| تعداد مشاهده مقاله | 71,680,611 |
| تعداد دریافت فایل اصل مقاله | 63,498,576 |
مدل بهبودیافته دولایه ای در طراحی سطح منطقی پایگاه داده تحلیلی | ||
| مدل سازی در مهندسی | ||
| مقاله 2، دوره 16، شماره 54، مهر 1397، صفحه 21-37 اصل مقاله (849.94 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2017.5631. | ||
| نویسندگان | ||
| محیا ارومیه1؛ نگین دانشپور* 2 | ||
| 1دانشگاه آزاد اسلامی- واحد قزوین | ||
| 2استادیار دانشگاه تربیت دبیر شهید رجایی | ||
| تاریخ دریافت: 28 مرداد 1394، تاریخ بازنگری: 11 خرداد 1396، تاریخ پذیرش: 25 مهر 1396 | ||
| چکیده | ||
| پایگاه داده تحلیلی، مخزن داده ای متمرکز، جمع آوری شده از منابع اطلاعاتی مختلف و ناهمگن در یک محدوده وسیع زمانی و برای پشتیبانی از سیستم های تصمیم یار می باشد. پایگاه داده تحلیلی منبع داده ای است که در فرایند تصمیم گیری از طریق پردازش تحلیلی بر خط استفاده می شود. فرایند توسعه یک پایگاه داده تحلیلی با تحلیل پایگاه داده عملیاتی، شناسایی نیازهای تحلیلی و نهایتا طراحی در سه سطح مفهومی، منطقی و فیزیکی انجام می شود. در این مقاله ابتدا مدل های طراحی موجود در سطوح مختلف پایگاه داده تحلیلی بررسی می شود. سپس مدل های سطح منطقی با توجه به خصوصیات مطرح در آن ها مقایسه و تحلیل شده، و نهایتا مدلی بهبودیافته در این سطح، برای فرایند طراحی پایگاه داده تحلیلی، ارائه می شود که ترکیبی از دو مدل ستاره ای و دانه برفی، به صورت دولایه ای است. به منظور مقایسه مدل پیشنهادی با مدل های موجود، از معیار زمان پاسخگویی به پرس وجوها استفاده شده است. آزمایشات نشان می دهد که مدل پیشنهادی منجر به بهبود زمان پاسخ به پرس وجوها می شود. در واقع زمان پاسخ به پرس وجوها در مدل پیشنهادی( دولایه ای) نسبت به مدل ستاره ای بطور متوسط 53/58 درصد و نسبت به مدل دانه برفی بطور متوسط 61/96 درصد بهبود می یابد. | ||
| کلیدواژهها | ||
| پایگاه داده تحلیلی در سطح مفهومی؛ پایگاه داده تحلیلی در سطح منطقی؛ مدل دانه برفی؛ مدل ستاره ای؛ طراحی پایگاه داده تحلیلی | ||
| عنوان مقاله [English] | ||
| An Improved two-layer Model in the Logical Level Data Warehouse Designing | ||
| نویسندگان [English] | ||
| Mahya Oroumiyeh1؛ Negin Daneshpour2 | ||
| 2Assistant Professor Shahid Rajaee Teacher Training University | ||
| چکیده [English] | ||
| Data warehouses are centralized repositories collected from various heterogeneous sources in a wide range of time (time-variant) for decision support systems. Data warehouses are data sources used in decision making processes by online analytical processing. The process of developing a data warehouse is done through operational database analyzing, analytical requirement identification and finally designing in conceptual, logical and physical levels. In this paper, the design models at different levels of data warehouses are researched and the logical level models are compared and analyzed with respect to their properties. Finally, an improved model is proposed in logical level that combines two models (star and snowflake) in the form of a two-layered model. Queries response time measure is used to compare the proposed model with existing models. Experiments show that the proposed model improves the response time of queries. In fact, the response time of queries on the proposed model (two-layer) is improved 58.53 percent in average versus star model, and 96.61 percent in average versus snowflake model. | ||
| کلیدواژهها [English] | ||
| Data Warehouse In conceptual level, Data Warehouse In logical level, Snowflake Schema, Star Schema, Data warehouse Designing | ||
| مراجع | ||
|
[1] Jindal, R., Taneja, SH. )2012(. “Comparative study of Data Warehouse Design Approches: A Survey”. International Journal of Database Management Systems (IJDMS), Vol.4, No.1. [2] Arfaoui, N., Akaichi, J. (2012). “Data warehouse: conceptual and logical schema – survey”. International Journal of Enterprise Computing and Business Systems, Vol.2, pp. 1-31. [3] Tryfona, N., Busborg, F., Christiansen, J.G. B. (1999). “starER: A Conceptual Model for Data Warehouse Design”. Department of Computer Science, Aalborg University, Fredrik Bajersvej 7E, DK-9220, Aalborg Øst, Denmark, pp. 1-6. [4] Kamble, A.S. (2008). “A conceptual model for multidimensional data”. Proceedings of the fifth on Asia-Pacific conference on conceptual modeling, Wollongong, NSW, Australia. [5] Başaran, B. P. (2005). “A comparison of data warehouse design models”. [6] Moody, D. L., Kortink, M. A. R. (2000). “From Enterprise Models to Dimensional Models: A Methodology for Data Warehouse and Data Mart Design”. In Proceedings of International Workshop on Design and Management of Data Warehouses (DMDW'2000), Sweden,pp. 5-1, 5-12. [7] Seyed-Abbassi, B. (2001). “Teaching Effective Methodologies to Design a Data Warehouse”. [8] Moody, D. L., Kortink, M. A. R. (2008). “From ER Models to Dimensional Models Part II: Advanced Design Issues”. Journal of Business Intelligence, PP. 1-12. [9] Mishra, D., Yazici, A., Başaran, B. P. (2008). “A Case study of Data Models in Data Warehousing Design”. pp. 314-319 [10] Nugawela, S. (2013). “Data warehousing model for integrating fragmented electronic health records from disparate and heterogeneous clinical data stores”. School of Electrical Engineering and Computer Science Faculty of Science and Engineering Queensland University of Technology. [11] Kimball, R. (1996). “Data Warehouse Toolkit”. John Wiley & Sons, Inc, New York.
[12] Gosain, A. (2015). “Literature Review of Data model Quality metrics of Data Warehouse”. International Conference on Intelligent Computing, Communication & Convergence (ICCC), Vol. 48, pp. 236 – 243. [13] Completo, J., Cruz, R.S., Coheur, L., Delgado, M. (2012). “Design and Implementation of a Data Warehouse for Benchmarking in Clinical Rehabilitation”. International Conference on Health and Social Care Information Systems and Technologies, Vol. 5, pp. 885 – 894. [14] Arora, M., Gosain, A. (2011). “Schema Evolution for Data Warehouse: A Survey”. International Journal of Computer Applications, Vol. 22, No.6. [15] Jovanovica, P., Romeroa, O., Simitsisb, A., Abellóa, A., Mayorova, D. (2014). “A requirement-driven approach to the design and evolution of data warehouses”. Information Systems, Vol.44, pp. 94–119. [16] George, J., Vijay Kumar, B., Santhosh Kumar, V. (2015). “Data Warehouse Design Considerations for a Healthcare Business Intelligence System”. Proceedings of the World Congress on Engineering (WCE), Vol. 1. ]17[زاهدین لباف، ز.، دانشپور، ن. (1394). "یافتن خودکار حقایق و ابعاد با روش اکتشافی در پایگاه داده تحلیلی". بیست و یکمین کنفرانس ملی سالانه انجمن کامپیوتر ایران، تهران، 18 تا 20 اسفند. [18] Tiwari, V., Singh Thakur, R. (2015). “ Contextual snowflake modelling for pattern warehouse logical design”.
[24] Sapia, C., Blaschka, M. H., Fling, G., Dinter, B. (1998). “Extending the E/R Model for the Multidimensional Paradigm”. In Proceeding of the Intrenational Workshop on Data Warehousing and Data Mining, Singapore, pp. 105-116. [25]Malinowski, E., Zimányi, E. (2004). “Representing spatiality in a conceptual multidimensional model”. In Proceedings of ACM international work shop on Geographic information systems, ACM Press, New York, pp. 12-22. [26] Malinowski, E., Zimányi, E. (2005). “Spatial Hierarchies and Topological Relationships in the Spatial MultiDimERmodel”. In Proceedings of the22nd British National Conferenceon Databases, BNCOD22, number3567 in Lecture Notes in Computer Science, Springer-Verlag, Sunderland, UK, pp. 17-28. [27] Malinowski, E., Zimányi, E. (2006). “Hierarchies in a Multidimensional Model: From Conceptual Modeling to Logical Representation”. Data &Knowledge Engineering, Vol.59, No.2, pp. 348-377. [28] Boehnlein, M., Ende, A.U. (1999). “Deriving Initial Data Warehouse Structuresfrom the Conceptual Data Models of the Underlying OperationalInformation Systems”. In Proceedings of the ACM Second InternationalWorkshop on Data Warehousing and OLAP (DOLAP'1999, Kansas City, 6. November), pp. 15-21. [29] Abellò, A., Samos, J., Saltor, F. (2002). “YAM2 (Yet another multidimensional model): An extension of UML”. International Database Engineering &Applications Symposium (IDEAS 2002), Edmonton, Canada, IEEE Computer Society, pp. 172-18. [30] Abellòٍ, A., Samos, J., Saltor, F. (2006). “YAM2: a multidimensional conceptual model extending UML”. Information Systems (IS), Elsevier, pp. 541–567. [31] Mora, S.L., Trujillo, J. (2003). “A comprehensive method for data warehouse design”. In Proceedings of the5th International Workshop on Design and Management of Data Warehouses, Germany, Vol. 1, pp. 1-14. [32] Mora, S.L., Trujillo, J., Song, I.Y. (2006). “A UML profile for multidimensional modeling in data warehouses”. Data & Knowledge Engineering, pp. 725-769. [33] Trujillo, J., Palomar, M. (1998). “An Object Oriented Approach to Multidimensional Database Conceptual Modeling (OOMD)”. Conference on Information and Knowledge Management, ACM Press, New York, pp. 16-21. [34] Trujillo, J. (1999). “The GOLD Model: An Object Oriented Multidimensional Data Model For Multidimensional Databases”. In Proceedings of the 9thECOOP International Workshop For PhD Students In Objects OrientedSystems, Lisboa, Portugal. [35] Sarkar, A., CHoudhury, S. (2009). “Conceptual Level Design of Object Oriented Data Warehouse: Graph Semantic Based Model”. [36] Xuan, D. N., Bellatreche, L., Pierra, G. (2006). “A Versioning Management Model for Ontology-Based Data Warehouses”. In Proceedings of the 8th International Conference on Data Warehousing and Knowledg Discovery DaWaK 2006, Krakow, Poland, pp. 195-206. [37] Skoutas, D., Simitsis, A. (2007). “Ontology-Based Conceptual Design of ETL Processes for Both Structured and Semi-Structured Data”. International Journal of Semantic Web and Information Systems, Vol. 3, No. 4, pp. 1-24. [38] Tsois, N., Karayannidis, T. S. (2001). “MAC: Conceptual data modeling forOLAP”. In Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW-2001), pp. 5–1, 5–13. [39] Golfarelli, M., Maio, D., Rizzi, S. (1998). “The Dimensional Fact Model: a Conceptual Model for Data Warehouses”. International Journal of Cooperative Information Systems, pp. 215-247. [40] Golfarelli, M. (2010). “From User Requirements to Conceptual Design in Data Warehouse Design – a Survey”. [41]Gutiérrez, A., Marotta, A. ( 2000). “An Overview of Data Warehous Design Approaches and Techniques”. Instituto de Computación, Facultad de Ingenieria, Universidad de la República, Montevideo, Uruguay. [42] Peralta, V., Ruggia, R. (2003). “Using Design Guidelines to Improve DataWarehouse Logical Design”. Proc of the International Workshop onDesign and Management of Data Warehouses, Berlin. [43] Peralta, V. (2003). “Data Warehouse Logical Design from Multidimensional Conceptual Schemas”. Instituto de Computación, Universidad de la República, Julio Herreray Reisig 565, Montevideo, CP 11300, Uruguay. [44] Peyravi, M. H. (2012). “A Schema Selection Framework for Data Warehouse Design”. International Journal of Machine Learning and Computing, Vol. 2, No. 3. [45] Shekofteh, M. (2012). “ Building Quick and Accurate Structure for Data Warehouse” .International Journal of Advanced Computer Science, Vol. 2, No. 7, pp. 274-276, This work was supported by Department Of computer science, Sarvestan Branch, Islamic Azad University, sarvestan, Iran. [46] Shrivastava, R.K., Mahrishi, M., Parnami,P. (2012). “An Optimized algorithm to select the appropriate Schema in Data Warehouses”. International Journal of Engineering Sciences & Research Technology, pp. 198-204. ISSN: 2277-9655.
| ||
|
آمار تعداد مشاهده مقاله: 1,257 تعداد دریافت فایل اصل مقاله: 434 |
||