Reality Mining: Using Big Data to Engineer a Better World (Hardcover)

Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and any other activity that leaves a digital trail. Although the abuse of Big Data -- surveillance, spying, hacking -- has made headlines, it shouldn't overshadow the abundant positive applications of Big Data. In Reality Mining, Nathan Eagle and Kate Greene cut through the hype and the headlines to explore the positive potential of Big Data, showing the ways in which the analysis of Big Data ("Reality Mining") can be used to improve human systems as varied as political polling and disease tracking, while considering user privacy.

Eagle, a recognized expert in the field, and Greene, an experienced technology journalist, describe Reality Mining at five different levels: the individual, the neighborhood and organization, the city, the nation, and the world. For each level, they first offer a nontechnical explanation of data collection methods and then describe applications and systems that have been or could be built. These include a mobile app that helps smokers quit smoking; a workplace "knowledge system"; the use of GPS, Wi-Fi, and mobile phone data to manage and predict traffic flows; and the analysis of social media to track the spread of disease. Eagle and Greene argue that Big Data, used respectfully and responsibly, can help people live better, healthier, and happier lives.

大數據由許多小數據組成：輸入手機的數字、輸入GPS設備的地址、瀏覽網站、網上購物、ATM交易以及任何留下數字蹤跡的活動。儘管大數據的濫用（監視、間諜、駭客）經常成為新聞頭條，但這不應掩蓋大數據的豐富正面應用。在《現實挖掘》一書中，Nathan Eagle和Kate Greene剖析了大數據的正面潛力，展示了大數據分析（現實挖掘）在政治民意調查和疾病追蹤等各種人類系統改進方面的應用，同時考慮了用戶隱私。

Eagle是該領域的知名專家，Greene是經驗豐富的科技記者，他們將現實挖掘分為五個不同層次：個人、社區和組織、城市、國家和世界。對於每個層次，他們首先提供非技術性的數據收集方法解釋，然後描述已經或可能建立的應用和系統。這些包括一個幫助戒煙者戒煙的手機應用程序；一個工作場所的「知識系統」；使用GPS、Wi-Fi和手機數據來管理和預測交通流量；以及分析社交媒體來追蹤疾病的傳播。Eagle和Greene認為，尊重和負責任地使用大數據可以幫助人們過上更好、更健康、更幸福的生活。