| 書目名稱 | The Four Generations of Entity Resolution | | 編輯 | George Papadakis,Ekaterini Ioannou,Themis Palpanas | | 視頻video | http://file.papertrans.cn/910/909936/909936.mp4 | | 叢書名稱 | Synthesis Lectures on Data Management | | 圖書封面 |  | | 描述 | Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of the research examines ways for improving its effectiveness and time efficiency. The initial ER methods primarily target Veracity in the context of structured (relational) data that are described by a schema of well-known quality and meaning. To achieve high effectiveness, they leverage schema, expert, and/or external knowledge. Part of these methods are extended to address Volume, processing large datasets through multi-core or massive parallelization approaches, such as the MapReduce paradigm. However, these early schema-based approaches are inapplicable to Web Data, which abound in voluminous, noisy, semi-structured, and highly heterogeneous information. To address the additional challenge of Variety, recent works on ER adopt a novel, loosely schema-aware functionality that emphasizes scalability and robustness to noise. Another line of present research focuses on the additional challenge ofVelocity, aiming to process data collections of a continuously increasing volume. The latest works, though, take advantage of the significant breakthroughs in Deep Learning and Crowdsourcing, incorpor | | 出版日期 | Book 2021 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01878-7 | | isbn_softcover | 978-3-031-00750-7 | | isbn_ebook | 978-3-031-01878-7Series ISSN 2153-5418 Series E-ISSN 2153-5426 | | issn_series | 2153-5418 | | copyright | Springer Nature Switzerland AG 2021 |
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