標(biāo)題: Titlebook: Data Quality Management with Semantic Technologies; Christian Fürber Book 2016 Springer Fachmedien Wiesbaden 2016 Data Quality Problems.Da [打印本頁] 作者: foresight 時間: 2025-3-21 19:06
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書目名稱Data Quality Management with Semantic Technologies網(wǎng)絡(luò)公開度
書目名稱Data Quality Management with Semantic Technologies網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Data Quality Management with Semantic Technologies被引頻次學(xué)科排名
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書目名稱Data Quality Management with Semantic Technologies年度引用學(xué)科排名
書目名稱Data Quality Management with Semantic Technologies讀者反饋
書目名稱Data Quality Management with Semantic Technologies讀者反饋學(xué)科排名
作者: 你敢命令 時間: 2025-3-22 00:07 作者: 吞沒 時間: 2025-3-22 04:05 作者: ascetic 時間: 2025-3-22 08:24
https://doi.org/10.1057/9780230113442rk. Ontology-based data quality management frameworks in here are artifacts that make use of ontologies to support data quality management activities. In the following, we provide a high-level classification of the field, which is then used to organize the presentation of related work in this chapter.作者: escalate 時間: 2025-3-22 11:07
https://doi.org/10.1007/978-3-030-64290-7In this chapter, we will provide a brief introduction into the thesis topic, clarify our understanding of the term “data” and its dependency to business processes and decisions, and discuss the economic relevance of the systematic management of data quality. Moreover, we give a short overview of the thesis structure.作者: xanthelasma 時間: 2025-3-22 12:58 作者: xanthelasma 時間: 2025-3-22 20:41 作者: GILD 時間: 2025-3-22 22:29
Application Procedure of SDQM,In this chapter, we explain how to use the SDQM architecture from the perspective of business users who want to create data requirements, identify data requirement violations, and evaluate the quality state based on their data requirements.作者: 良心 時間: 2025-3-23 02:19 作者: 罵人有污點(diǎn) 時間: 2025-3-23 09:04
Comparative and International Educationg & Strong, 1996, p. 22f.) that can be defined from several different perspectives (cf. Ge & Helfert, 2007, p. 1; Kahn et al., 2002, p. 185). For example, data consumers, data producers, data providers, and data custodians may all have different perspectives on the definition of data quality (cf. Ka作者: 期滿 時間: 2025-3-23 10:19
Comparative and International Educationof meaning by machines”. Ontologies are one of the core elements of semantic solutions. In the following, we review the definition of ontologies and briefly describe their general characteristics. Moreover, we discuss important concepts for ontology and knowledge representation within the Semantic W作者: Fibrinogen 時間: 2025-3-23 16:45 作者: covert 時間: 2025-3-23 18:07 作者: Etching 時間: 2025-3-24 02:10 作者: inspiration 時間: 2025-3-24 03:13
https://doi.org/10.1007/978-1-4615-8936-5 with the evaluation of precision and recall of SDQM’s data quality monitoring and assessment algorithms. The second part evaluates the practical applicability of SDQM by applying the framework to three different use cases, namely one business use case on material master data of a large organization作者: cathartic 時間: 2025-3-24 09:06 作者: ACME 時間: 2025-3-24 11:26 作者: 無孔 時間: 2025-3-24 15:25
https://doi.org/10.1007/978-3-658-12225-6Data Quality Problems; Data Quality Processes; Semantic Web; Semantic Technology; Information Systems作者: 肥料 時間: 2025-3-24 22:57
978-3-658-12224-9Springer Fachmedien Wiesbaden 2016作者: Platelet 時間: 2025-3-25 01:08 作者: Lipohypertrophy 時間: 2025-3-25 05:23 作者: CRANK 時間: 2025-3-25 10:48
Data Quality Management with Semantic Technologies作者: 共同確定為確 時間: 2025-3-25 12:40
Data Quality Management with Semantic Technologies978-3-658-12225-6作者: CHECK 時間: 2025-3-25 16:30 作者: 比喻好 時間: 2025-3-25 20:52
Semantic Technologies,eb. After that, we explain ways to process knowledge representations, such as reasoning, inferencing, and querying. Due to the focus of this thesis, we finally describe how relational databases and ontologies are related.作者: 使人煩燥 時間: 2025-3-26 01:07
Data Quality in the Semantic Web,e et al., 2001). For example, if the prices of all consumer products were published in a machine-readable format and structure throughout the whole Web, then more complete price comparisons at global scale would be possible with minimal manual effort.作者: 獨(dú)裁政府 時間: 2025-3-26 04:37
Evaluation of the Semantic Data Quality Management Framework (SDQM),, one Semantic Web use case with data from DBpedia, and one use case that examines the capability of SDQM to automatically identify inconsistent data requirements. In the third part of the evaluation, SDQM is compared to a conventional data quality tool.作者: BIAS 時間: 2025-3-26 09:20
Book 2016 this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.?.作者: precede 時間: 2025-3-26 16:19
Specification of Initial Requirements,hodology (DSRM, cf. Peffers et al., 2008) process as explained in section 2.4. We start with describing the required artifacts with a motivating scenario that illustrates the needs related to data quality management. Based on the motivating scenario, we derive initial requirements for the framework.作者: expansive 時間: 2025-3-26 17:05
Architecture of the Semantic Data Quality Management Framework (SDQM),management vocabulary (DQM Vocabulary), (4) the data requirements editor, and (5) the reporting layer. The design of the architecture is based on the requirements identified in the previous chapter. The following sections are organized according to these major components of the SDQM.作者: 我不重要 時間: 2025-3-26 21:30 作者: Maximize 時間: 2025-3-27 04:27
Comparative and International Educationhn et al., 2002, p. 184). From the consumer viewpoint, data quality can be defined as “data that are fit for use by data consumers” (Wang & Strong, 1996, p. 6) in analogy to the popular quality definition related to products and services by Juran (Juran, 1988, p. 2.2).作者: 罐里有戒指 時間: 2025-3-27 08:48 作者: 啤酒 時間: 2025-3-27 10:17 作者: 預(yù)示 時間: 2025-3-27 17:04
https://doi.org/10.1007/978-1-4615-8936-5, one Semantic Web use case with data from DBpedia, and one use case that examines the capability of SDQM to automatically identify inconsistent data requirements. In the third part of the evaluation, SDQM is compared to a conventional data quality tool.作者: animated 時間: 2025-3-27 19:54
ity software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.?.978-3-658-12224-9978-3-658-12225-6作者: Ligament 時間: 2025-3-27 23:06 作者: buoyant 時間: 2025-3-28 02:15 作者: cutlery 時間: 2025-3-28 10:02 作者: 干旱 時間: 2025-3-28 13:50 作者: 絆住 時間: 2025-3-28 14:45
https://doi.org/10.1057/9780230113442at. Moreover, we configured a wiki, called data requirements wiki, which contains standard forms to capture data requirements and to store them based on the elements of our ontology, the DQM vocabulary.作者: Archipelago 時間: 2025-3-28 18:54 作者: infatuation 時間: 2025-3-29 00:40 作者: Suggestions 時間: 2025-3-29 03:29 作者: Catheter 時間: 2025-3-29 10:54 作者: Brain-Waves 時間: 2025-3-29 11:24
Specification of Initial Requirements,QM), which shall be developed to support data quality management activities by the use of ontologies. We thereby apply the Design Science Research Methodology (DSRM, cf. Peffers et al., 2008) process as explained in section 2.4. We start with describing the required artifacts with a motivating scena作者: 多山 時間: 2025-3-29 15:57
Architecture of the Semantic Data Quality Management Framework (SDQM),component of SDQM’s architecture as illustrated in figure 26, namely (1) the data acquisition layer, (2) the data storage layer, (3) the data quality management vocabulary (DQM Vocabulary), (4) the data requirements editor, and (5) the reporting layer. The design of the architecture is based on the 作者: 同音 時間: 2025-3-29 23:46
Evaluation of the Semantic Data Quality Management Framework (SDQM), with the evaluation of precision and recall of SDQM’s data quality monitoring and assessment algorithms. The second part evaluates the practical applicability of SDQM by applying the framework to three different use cases, namely one business use case on material master data of a large organization
