Data Science: New Issues, Challenges and Applications
暫譯: 資料科學:新議題、挑戰與應用
Dzemyda, Gintautas, Bernatavičiene, Jolita, Kacprzyk, Janusz
- 出版商: Springer
- 出版日期: 2021-02-14
- 售價: $7,100
- 貴賓價: 9.5 折 $6,745
- 語言: 英文
- 頁數: 313
- 裝訂: Quality Paper - also called trade paper
- ISBN: 303039252X
- ISBN-13: 9783030392529
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相關分類:
Data Science
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相關主題
商品描述
This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science.
Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field.
In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.
Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field.
In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.
商品描述(中文翻譯)
本書包含16章,由來自不同數據科學領域的研究人員撰寫。他們專注於語言技術、優化、計算思維、智能決策支持系統、信號分解、模型驅動開發方法論、企業應用的互操作性、金融市場的異常檢測、3D虛擬現實、環境數據監測、卷積神經網絡、知識儲存、數據流分類以及社交網絡的安全性等理論與應用。各篇論文突顯了數據科學中的豐富議題及其應用。
現代技術使我們能夠快速存儲和傳輸大量數據。這些數據可以非常多樣化——包括圖像、數字、串流、與人類行為和生理參數相關等。無論數據是僅僅原始數字、粗糙圖像,還是能幫助解決當前問題並預測未來發展,取決於我們是否能有效地處理和分析這些數據。數據科學正在迅速發展,但它仍然是一個非常年輕的領域。
特別是,數據科學涉及可視化、統計、模式識別、神經計算、圖像分析、機器學習、人工智能、數據庫和數據處理、數據挖掘、大數據分析以及數據庫中的知識發現。它還與優化、區塊鏈、網絡社會和網絡物理系統、物聯網(IoT)、社會計算、高性能計算、內存鍵值存儲、雲計算、數據流、覆蓋網絡、認知計算、眾包分析、日誌分析、基於容器的虛擬化和終身價值建模等領域有許多接口。這些領域彼此高度相關。此外,數據科學現在正在擴展到新的應用領域:化學工程、生物技術、建築能源管理、材料顯微鏡、地理研究、學習分析、放射學、金屬設計、生態系統穩態研究等許多其他領域。