轨迹数据仓库建模的模型驱动体系结构

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI:10.4018/ijdwm.2020100102
Noura Azaiez, J. Akaichi
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引用次数: 1

摘要

商业智能包括支持决策制定的数据仓库概念。由于ETL流程呈现了仓储技术的核心,它负责从源系统中提取数据并将其放入数据仓库中。随着地理信息系统、普适系统和定位系统等技术的发展,传统的仓库特征已经无法处理集成在仓储链中的移动性方面的问题。因此,从移动物体运动中收集的轨迹或移动数据必须通过所谓的轨迹ELT来管理。为此,作者强调了模型驱动架构方法实现整个转换任务的能力,在这种情况下,将描述结果轨迹的轨迹数据源模型转换为轨迹数据集市模型。作者用癫痫患者状态的案例研究说明了所提出的方法。
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The Model-Driven Architecture for the Trajectory Data Warehouse Modeling
Business Intelligence includes the concept of data warehousing to support decision making. As the ETL process presents the core of the warehousing technology, it is responsible for pulling data out of the source systems and placing it into a data warehouse. Given the technology development in the field of geographical information systems, pervasive systems, and the positioning systems, the traditional warehouse features become unable to handle the mobility aspect integrated in the warehousing chain. Therefore, the trajectory or the mobility data gathered from the mobile object movements have to be managed through what is called the trajectory ELT. For this purpose, the authors emphasize the power of the model-driven architecture approach to achieve the whole transformation task, in this case transforming trajectory data source model that describes the resulting trajectories into trajectory data mart models. The authors illustrate the proposed approach with an epilepsy patient state case study.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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