Topic
Data management
About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.
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TL;DR: A data management method for digital twin of product based on blockchain technology is proposed and the results show that the proposed method can solve the abovementioned data management problems simultaneously.
135 citations
TL;DR: This investigation of VGI for disaster management provides broader insight into key challenges and impacts of V GI on geospatial data practices and the wider field of geographical science.
Abstract: The immediacy of locational information requirements and importance of data currency for natural disaster events highlights the value of volunteered geographic information (VGI) in all stages of disaster management, including prevention, preparation, response, and recovery. The practice of private citizens generating online geospatial data presents new opportunities for the creation and dissemination of disaster-related geographic data from a dense network of intelligent observers. VGI technologies enable rapid sharing of diverse geographic information for disaster management at a fraction of the resource costs associated with traditional data collection and dissemination, but they also present new challenges. These include a lack of data quality assurance and issues surrounding data management, liability, security, and the digital divide. There is a growing need for researchers to explore and understand the implications of these data and data practices for disaster management. In this article, we review the current state of knowledge in this emerging field and present recommendations for future research. Significantly, we note further research is warranted in the pre-event phases of disaster management, where VGI may present an opportunity to connect and engage individuals in disaster preparation and strengthen community resilience to potential disaster events. Our investigation of VGI for disaster management provides broader insight into key challenges and impacts of VGI on geospatial data practices and the wider field of geographical science.
134 citations
TL;DR: This work presents an ontology-based approach for BI applications, specifically in statistical analysis and data mining, and implements this approach in financial knowledge management system (FKMS), which is able to do data extraction, transformation and loading, and data cubes creation and retrieval.
Abstract: Business intelligence (BI) applications within an enterprise range over enterprise reporting, cube and ad hoc query analysis, statistical analysis, data mining, and proactive report delivery and alerting. The most sophisticated applications of BI are statistical analysis and data mining, which involve mathematical and statistical treatment of data for correlation analysis, trend analysis, hypothesis testing, and predictive analysis. They are used by relatively small groups of users consisting of information analysts and power users, for whom data and analysis are their primary jobs. We present an ontology-based approach for BI applications, specifically in statistical analysis and data mining. We implemented our approach in financial knowledge management system (FKMS), which is able to do: (i) data extraction, transformation and loading, (ii) data cubes creation and retrieval, (iii) statistical analysis and data mining, (iv) experiment metadata management, (v) experiment retrieval for new problem solving. The resulting knowledge from each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports are stored, shared, disseminated, and thus helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification.
134 citations
01 Aug 2011
TL;DR: Some of the key challenges that cloud-based data markets face are outlined and the associated research problems that the community can help solve are discussed.
Abstract: Cloud-computing is transforming many aspects of data management. Most recently, the cloud is seeing the emergence of digital markets for data and associated services. We observe that our community has a lot to offer in building successful cloud-based data markets. We outline some of the key challenges that such markets face and discuss the associated research problems that our community can help solve.
134 citations
Book•
27 Jan 2003
TL;DR: The book blends theory and practice to provide practical knowledge and guidelines to enterprises wishing to understand the importance of managing documents to their operations along with presentation of document content to facilitate business planning and operations support.
Abstract: The book blends theory and practice to provide practical knowledge and guidelines to enterprises wishing to understand the importance of managing documents to their operations along with presentation of document content to facilitate business planning and operations support It gives extensive pointers to those who propose to embark upon the implementation of integrated document management systems
134 citations