مقاله انگلیسی رایگان در مورد استفاده از فن آوری های وب معنایی در توسعه انبار داده ها – وایلی ۲۰۱۸

مقاله انگلیسی رایگان در مورد استفاده از فن آوری های وب معنایی در توسعه انبار داده ها – وایلی ۲۰۱۸

 

مشخصات مقاله
ترجمه عنوان مقاله استفاده از فن آوری های وب معنایی در توسعه انبار داده ها: یک بازنمایی نظام‌مند
عنوان انگلیسی مقاله Using Semantic Web technologies in the development of data warehouses: A systematic mapping
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۲۵ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه وایلی
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) JCR – Master Journal List – Scopus
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
شناسه ISSN ۱۹۴۲-۴۷۹۵
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی فناوری اطلاعات، کامپیوتر
گرایش های مرتبط اینترنت و شبکه های گسترده، مهندسی نرم افزار، سیستم های چند رسانه ای، شبکه های کامپیوتری
نوع ارائه مقاله
ژورنال
مجله  بررسی های میان رشته ای وایلی. داده کاوی و کشف دانش – Wiley Interdisciplinary Reviews. Data Mining And Knowledge Discovery
دانشگاه Department of Computer Science and Informatics, Universidad de La Frontera, Temuco, Chile
کلمات کلیدی انبار داده ها، وب معنایی، بازنمایی نظام‌مند
کلمات کلیدی انگلیسی data warehouses، Semantic Web، systematic mapping
شناسه دیجیتال – doi
https://doi.org/10.1002/widm.1293
کد محصول E13016
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
۱- INTRODUCTION

۲- MOTIVATION AND CONTRIBUTION

۳- LITERATURE REVIEW METHOD

۴- ANALYSIS OF THE RESULTS AND DISCUSSION

۵- RESEARCH CHALLENGES

۶- LIMITATIONS OF THE STUDY

۷- RELATED WORK

۸- CONCLUSIONS AND FUTURE WORK

۹- BEYOND SM

REFERENCES

 

بخشی از متن مقاله:

INTRODUCTION

To include external web data in the traditional data warehouse (DW) systems, and the traditional online analytical processing (OLAP) processes, is a promising way for broadening traditional business intelligence (BI) analysis Abelló et al. (2013). In the context of BI, a DW is used to collect, organize and store subject-oriented, integrated, time-variant, and nonvolatile data (Inmon, 1992). Traditionally, a DW has been defined as a historical data repository containing data collected from a wide variety of heterogeneous sources by means of extraction–transformation–loading (ETL) processes (Kimball & Ross, 2002). On the other hand, multidimensional (MD) processing, also called OLAP, is an approach for responding to MD analytical queries. Research focusing on DWs and OLAP has led to the creation of important technologies for the design, administration, and use of information systems in decision-making support. Part of the interest in, and success of this field, can be attributed to the demonstrated need for software and tools that help improve data analysis and administration. This is mainly due to the large quantity of information that is being accumulated by corporations as well as scientific databases. Traditional BI tools, such as OLAP, have been successfully applied to large amounts of data coming from operational databases. However, there is a trend whereby DWs are becoming more and more dynamic, with updates occurring almost in real-time, and with the inclusion of more complex types of data (Henschen, 2015). This situation has forced traditional BI to open its gates to external data in order to encompass a more heterogeneous and open analysis scenario (Chen, Chiang, & Storey, 2012). Current research envisions that Semantic Web technologies are required for realizing the next generation of DWs (Abelló et al., 2015), as an increasing quantity of semantically annotated data is available over the Internet.1 To include Semantic Web information in a traditional OLAP analysis process is therefore a promising way to augment traditional BI analyses (Trujillo & Maté, ۲۰۱۲). The Semantic Web is an extension of the Web proposed by the World Wide Web Consortium.2 Its intended objective is to facilitate the creation of technologies that publish legible data for informatic applications, which are implemented by adding semantic metadata and ontologies to the web (Shadbolt, Berners-Lee, & Hall, 2006). In practical terms, the strength of the Semantic Web lies in its ability to aggregate the semantic annotations of web-published content so that the information can be effectively retrieved and processed, either by humans or by machines, for a wide variety of tasks (Hendler, 2001). The above goals is achieved through the use of a diverse variety of software technologies, such as ontologies (Coral, Francisco, & Mario, 2006) and markup languages (e.g., RDF and OWL) (Saha, 2007), which allow semantic annotations to be added to resources that can either be very simple, or very complex annotations, depending on the requirements.

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