基于mda的NoSQL系统OCL约束检测方法

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2021-01-01 DOI:10.4018/IJDWM.2021010101
F. Abdelhédi, A. A. Brahim, G. Zurfluh
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引用次数: 1

摘要

近年来,大数据受到了广泛关注。不仅数据量与以前完全不同,而且作者的数据类型也不同,包括格式、结构和来源等因素。这无疑改变了处理大数据所需的工具,从而产生了NoSQL系统。虽然NoSQL系统已经证明了其处理大数据的效率,但如何在NoSQL系统中实现大数据的自动存储仍然是一个未解决的问题。本文提出了一种在NoSQL系统中实现UML概念模型的自动方法,包括将相关的OCL约束映射到检查它们所需的代码。为了证明工作的实际适用性,本文在一个支持四种基本OCL表达式的工具中实现了它:基于迭代的表达式、OCL预定义操作、If表达式和Let表达式。
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OCL Constraints Checking on NoSQL Systems Through an MDA-Based Approach
Big data have received a great deal of attention in recent years. Not only is the amount of data on a completely different level than before, but also the authors have different type of data including factors such as format, structure, and sources. This has definitely changed the tools one needs to handle big data, giving rise to NoSQL systems. While NoSQL systems have proven their efficiency to handle big data, it's still an unsolved problem how the automatic storage of big data in NoSQL systems could be done. This paper proposes an automatic approach for implementing UML conceptual models in NoSQL systems, including the mapping of the associated OCL constraints to the code required for checking them. In order to demonstrate the practical applicability of the work, this paper has realized it in a tool supporting four fundamental OCL expressions: iterate-based expressions, OCL predefined operations, If expression, and Let expression.
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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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