Design and application on metadata management for information supply chain

Dong, Runsha; Su, Fei; Yang, Siqing; Xu, Lexi; Cheng, Xinzhou; Chen, Weiwei

doi:10.1109/iscit.2016.7751658

Cited by 25 publications

(2 citation statements)

References 6 publications

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“…Additionally, the choice of a NoSQL database type should align with the specific requirements of the application and the nature of the data being managed. As organizations embrace the diversity of data sources and formats in the age of Big Data, NoSQL databases provide the flexibility needed to adapt and thrive in this evolving data landscape [5,6].…”

Section: Characteristics Of Nosql Databasesmentioning

confidence: 99%

The Synergy between Business Process and Big Data

Duggineni

2022

J Arti Inte & Cloud Comp

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The synergy of Big Data and cloud computing has ushered in a new era of data-driven innovation, transforming the way organizations store, process, and derive insights from vast and complex datasets. This comprehensive overview delves into the intersection of Big Data and cloud computing, exploring key technologies, scalability, elasticity, data warehousing, NoSQL databases, distributed data processing, and the benefits they offer. By harnessing the power of cloud-based resources, organizations can efficiently manage their data resources, optimize performance, control costs, and gain a competitive edge in the data-centric landscape.

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Section: Characteristics Of Nosql Databasesmentioning

confidence: 99%

The Synergy between Business Process and Big Data

Duggineni

2022

J Arti Inte & Cloud Comp

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“…Metadata researchers draw on these functions to create typologies identifying different metadata types (Méndez & van Hooland, 2013). Business and data warehousing typologies generally include business and technical metadata, and, at times, also include process and operational metadata (Dong et al, 2016;Shankaranarayanan & Even, 2006;Vaduva & Dittrich, 2001). The digital library, archive, and repository communities have categories, such as descriptive, technical, preservation, provenance, and usage metadata (Zeng & Qin, 2016).…”

Section: Metadata: a Value-added Languagementioning

confidence: 99%

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Greenberg

2017

Journal of Data and Information Science

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Purpose: The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.Design/methodology/approach: This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings:The "utilitarian nature" and "historical and traditional views" of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata lingua franca to help frame research in the data science research space. Research limitations:There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications:The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.Originality/value: Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

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