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Titlebook: Measuring the Data Universe; Data Integration Usi Reinhold Stahl,Patricia Staab Book 2018 Springer International Publishing AG, part of Spr

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发表于 2025-3-21 16:34:16 | 显示全部楼层 |阅读模式
书目名称Measuring the Data Universe
副标题Data Integration Usi
编辑Reinhold Stahl,Patricia Staab
视频video
概述Provides an introduction to organizing and integrating data.Introduces the international statistics standard Statistical Data and Metadata Exchange (SDMX) and explains how it could be used in data int
图书封面Titlebook: Measuring the Data Universe; Data Integration Usi Reinhold Stahl,Patricia Staab Book 2018 Springer International Publishing AG, part of Spr
描述.This richly illustrated book provides an easy-to-read introduction to the challenges of organizing and integrating modern data worlds, explaining the contribution of public statistics and the ISO standard SDMX (Statistical Data and Metadata Exchange). As such, it is a must for data experts as well those aspiring to become one..Today, exponentially growing data worlds are increasingly determining our professional and private lives. The rapid increase in the amount of globally available data, fueled by search engines and social networks but also by new technical possibilities such as Big Data, offers great opportunities. But whatever the undertaking – driving the block chain revolution or making smart phones even smarter – success will be determined by how well it is possible to integrate, i.e. to collect, link and evaluate, the required data. One crucial factor in this is the introduction of a cross-domain order system in combination with a standardization of the data structure.. .Using everyday examples, the authors show how the concepts of statistics provide the basis for the universal and standardized presentation of any kind of information. They also introduce the international
出版日期Book 2018
关键词data integration; SDMX; standardization; big data; statistics; data analysis; data modeling; business intel
版次1
doihttps://doi.org/10.1007/978-3-319-76989-9
isbn_softcover978-3-030-08342-7
isbn_ebook978-3-319-76989-9
copyrightSpringer International Publishing AG, part of Springer Nature 2018
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978-3-030-08342-7Springer International Publishing AG, part of Springer Nature 2018
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Where We Stand, Where We Want to Be, and How to Get Theree collection of more and more granular data..Companies are increasingly aware that they are sitting on an underestimated treasure of data. But most of it is stored in separate .. Therefore, many organisations are making major efforts to . data, to link the treasures hidden in the silos and to create
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What Does Reality Look Like? gaps still exist..When data sets do not fit together, the potential within them cannot be exploited. Nevertheless, the information industry has neither a system of order nor any comprehensive standard for data. This deficiency explains why firms launch countless data warehousing, business intellige
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What Can We Expect From Big Data? parallelisation and networking, immensely greater computing power is possible. This drives the idea of simply throwing all the data into a . and magically recovering new insights from it..However, this brute approach soon reaches its limits—set not only by ethics but also by feasibility. For all it
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Why Is Data Integration So Hard?, is established to enable automated handling of the data. A common understanding will then be achieved through semantic harmonisation..Data integration allows linking and subsequent processing of data from different sources. Essential for this is this three-step standardisation, which unfortunately
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Basic Thoughts About Standardisatione initial investments have been made. Often they are not even the “best solution” to the single individual problem. However, the strength of a standard does not come from its genius but from the fact that it is taken up by all. Once a standard has been established or even endorsed by official author
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Standardisation and Researchtandardisation but in a narrow subject area for which they need well-prepared datasets..Technical tools can provide a high degree of automation both for data integration and for reporting. However, there is a gap between what researchers want from a dataset and what even well-ordered data structures
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