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.

大数据是由许多小数据组成的：输入手机的数字、输入GPS设备的地址、访问网站、网上购物、ATM交易，以及其他任何留下数字痕迹的活动。尽管大数据的滥用--监视、间谍活动、黑客攻击--已经成为头条新闻，但它不应该掩盖大数据丰富的正面应用。在《现实挖掘》中，内森-伊格尔和凯特-格林穿透炒作和头条新闻，探索大数据的积极潜力，展示了大数据分析（"现实挖掘"）可用于改善人类系统的方式，如政治投票和疾病跟踪，同时考虑用户隐私。

伊格尔是该领域公认的专家，格林是经验丰富的技术记者，他从五个不同的层面描述了现实挖掘：个人、邻里和组织、城市、国家和世界。对于每个层次，他们首先对数据收集方法进行了非技术性的解释，然后描述了已经或可以建立的应用和系统。其中包括一个帮助吸烟者戒烟的移动应用；一个工作场所的 "知识系统"；利用GPS、Wi-Fi和手机数据来管理和预测交通流量；以及分析社交媒体来追踪疾病的传播。伊格尔和格林认为，大数据如果得到尊重和负责任的使用，可以帮助人们过上更好、更健康、更快乐的生活。