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The papers are organized in topical sections on data models and query languages; data warehousing; query and data-flow optimization; information extraction and integration; spatial, temporal and streaming data; data mining and knowledge discovery; data organization and physical issues; and data and business processes.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52401631953169,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52401632837905,"sku":"NLS9783319109329","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319109329.jpg?v=1786442697"},{"product_id":"predicting-the-dynamics-of-research-impact-book-yannis-manolopoulos-9783030866709","title":"Predicting the Dynamics of Research Impact","description":"\u003cp\u003eThis book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction\u003ci\u003e \u003c\/i\u003efocuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks. \u003c\/p\u003e\n\n\u003cp\u003eThe single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art. \u003c\/p\u003e\n\nDue to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. 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