یک بازنمایی نظاممند در مورد تکنولوژی وب معنایی در توسعه انبار داده
1- INTRODUCTION
2- MOTIVATION AND CONTRIBUTION
3- LITERATURE REVIEW METHOD
4- ANALYSIS OF THE RESULTS AND DISCUSSION
5- RESEARCH CHALLENGES
6- LIMITATIONS OF THE STUDY
7- RELATED WORK
8- CONCLUSIONS AND FUTURE WORK
9- BEYOND SM
REFERENCES
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, 2012). 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.