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عنوان فارسی مقاله: یک رویکرد انبار داده برای هوش تجاری
عنوان انگلیسی مقاله: A Data Warehouse Approach for Business Intelligence
مجله/کنفرانس: بیست و هشتمین کنفرانس بین المللی فن آوری های توانمندساز: زیرسازی برای شرکتهای مشترک - ۲۸th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises
رشته های تحصیلی مرتبط: کامپیوتر، مهندسی فناوری اطلاعات، مدیریت
گرایش های تحصیلی مرتبط: رایانش ابری، هوش مصنوعی، مدیریت سیستم های اطلاعات، مدیریت کسب و کار، مدیریت فناوری اطلاعات
کلمات کلیدی فارسی: هوش تجاری، یکپارچه سازی داده ها، داده های فضایی-زمانی، انبار داده ها، انبار داده های مبتنی بر ابر
کلمات کلیدی انگلیسی: Business Intelligence، Data Integration، Spatiotemporal Data، Data Warehouse، Cloud Based Data Warehouse
شناسه دیجیتال (DOI): https://doi.org/10.1109/WETICE.2019.00022
دانشگاه: University of Thessaly
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: کنفرانس
نوع مقاله: ISI
سال انتشار مقاله: 2019
شناسه ISSN: 1524-4547
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 6
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13347
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I- Introduction


II- Related Work


III- Methodology for Data Warehouse Design


IV- Telecommunication DW Case Study


V- Implementation


VI- Discussion


References

نمونه متن انگلیسی مقاله

Abstract


In a cloud based data warehouse (DW), business users can access and query data from multiple sources and geographically distributed places. Business analysts and decision makers are counting on DWs especially for data analysis and reporting. Temporal and spatial data are two factors that affect seriously decision-making and marketing strategies and many applications require modelling and special treatment of these kinds of data since they cannot be treated efficiently within a conventional multidimensional database. One main application domain of spatiotemporal data warehousing is telecommunication industry, which is rapidly dominated by massive volume of data. In this paper, a DW schema modelling approach is proposed which integrate in a unified manner temporal and spatial data in a general data warehousing framework. Temporal and spatial data integration becomes more important as the volume and sharing of data grows. The aim of this research work is to facilitate the understanding, querying and management of spatiotemporal data for on-line analytical processing (OLAP). The proposed new spatiotemporal DW schema extends OLAP queries for supporting spatial and temporal queries. A case study is developed and implemented for the telecommunication industry.


INTRODUCTION


An increasing number of Cloud Computing (CC) platforms provide facilities for big Data Warehouse (DW) storage and manipulation. Having all the DW functionalities over the Internet simplifies the access on it and storage is no longer an issue since clouds offer almost limitless storage capacity. The Apache Hive Data Warehouse [1] manages large distributed data sets using SQL, while Microsoft with Azure SQL Data Warehouse [2] can fully manage a cloud DW providing a single holistic DW solution. Amazon offers also cloud DW capabilities over Amazon Redshift Cluster using standard SQL [3]. Google isn’t out of this with Google BiqQuerry to antagonize the other big vendors [4]. Nowadays, almost all big and smaller cloud providers like IMB [5], Oracle [6], Teradata [7], CoolaData [8] etc. have already include DW services in their cloud environments. Business intelligence (BI) is a technology-driven process for the collection, integration, analysis, and presentation of business information. It includes a wide variety of tools, applications and methodologies that permit organizations to collect data from internal and external sources for analysis and decision-making. A component of BI is online analytical processing (OLAP). OLAP creates a multidimensional view of data for the user to do the analysis. The approach for OLAP is classified into three categories, MOLAP, ROLAP and HOLAP. In MOLAP (multi-dimensional online analytical processing) the data used for analysis is stored in specialized multidimensional databases. ROLAP works directly with relational databases. HOLAP approach is a hybrid OLAP approach which combines MOLAP with ROLAP by allowing the designer to decide which portion of the data will be stored in MOLAP and which portion in ROLAP. BI data is typically stored in a DW. The purpose of data warehousing is to construct a huge repository of integrated data, which is optimized for analysis purposes. Nowadays, the big data challenge moves the tradition DWs to cloud based DWs with limitless storage resources and internet based secure access from anywhere to everywhere.

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