Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation (Hardcover)

Unstructured Mining Approaches to Solve Complex Scientific Problems

As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses.

The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches.

Encompassing systematic and practical perspectives, the book provides the necessary motivation and strategies as well as a heterogeneous set of comprehensive, illustrative examples. It reveals the importance of heterogeneous data analytics in aiding scientific discoveries and furthers data science as a discipline.

「解決複雜科學問題的非結構化挖掘方法」

隨著科學數據和文獻的數量呈指數級增長，科學家需要更強大的工具和方法來處理和綜合信息，並提出最可能既正確又重要的新假設。《加速發現：利用非結構化信息進行假設生成》描述了一種新的科學研究方法，該方法將非結構化數據分析作為生成新假設的工具。

作者開發了一種系統化的過程，利用異構結構化和非結構化數據源、數據挖掘和計算架構，使發現過程更快速、更有效。這個過程通過讓科學家和發明家更容易地分析和理解可能性空間、比較不同選擇，並發現全新的方法，加速了人類的創造力。

本書包含系統化和實用的觀點，提供了必要的動機和策略，以及一系列全面而生動的實例。它揭示了異構數據分析在幫助科學發現中的重要性，並推進了數據科學作為一門學科的發展。