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Reality mining : using big data to engineer a better world
Eagle N., Greene K., The MIT Press, Cambridge, MA, 2014. 208 pp. Type: Book (978-0-262027-68-7)
Date Reviewed: Jan 16 2015

Coauthored by a highly cited researcher and a well-known and experienced science journalist, this book is a comprehensive essay about the positive potential anticipated in the near future from the collection, management, and analysis of big data. The key term used here, starting from the title, is “reality analysis,” referring to data originating from the real world and the interactions among humans, objects, and the digital networked universe. The message of the book is clear: data are everywhere, especially when it comes to the use of today’s mobile devices. The scale of volume, velocity, and variation of such data is huge and despite the hard problems related to their manipulation, these data are sources of invaluable information that can be transformed into knowledge about real-world phenomena with appropriate computer science and statistical methods. In this regard, the authors provide a fascinating view of a future world, where data play a key role as a well of collective knowledge.

The book is written in a very readable manner without excessive use of technical details, and without downgrading the scientific quality. It can be read almost like a novel, since it narrates in an eloquent manner specific research stories of projects conducted by universities or companies. In order to present all of these studies, the authors use a scaling categorization of the material. Therefore, the narration starts from the individual scale and proceeds to small communities or neighborhoods, then to cities, to nations, and finally to the entire world. For this reason, the organization of the book involves five parts corresponding to the five aforementioned scales; each part is divided into two chapters, the first about various types of data and ways to access them and the second on reality mining applications. In each part of the book, besides discussing the positive potential, there are extensive discussions on the limitations and threats related to the difficulties of collecting or accessing the data and the security risks from their malevolent use.

The “individual” part deals with life logging, the tracking of individual daily actions. Mobile devices, equipped with sensors able to collect personal data, with limitless applications, play a central role. The evolving technology of mobile devices allows them to “learn” the individual’s life and help to coach and improve it with the support of powerful software. Other sources of data like specialized wearable sensors (glasses, monitors, biosensors) and social media are also discussed in connection with their potential uses, focusing on health issues.

The “neighborhood” part deals with relatively small communities. Examples are conference attendants, employees using knowledge management systems at a workplace, and citizens who use mobile devices to report various neighborhood problems like pollution and traffic. All of these data sources can be utilized for improving community life.

The “city” part deals mainly with two types of data: traffic metrics and crime statistics. Traffic data can be derived from GPS devices, traffic cameras, mobile phone location sensors, road sensors, police alerts, reports of incidents, and weather conditions. Their potential uses involve alerts to avoid jams or accidents, plans to improve highway networking performance, disaster management through evacuation planning and emergency response teams, and general optimal allocation of resources. On the other hand, crime data from incidence-arrest reports, street video cameras, and databases of criminal activities can be used for computerized crime systems, the optimal allocation of police forces in high-risk areas, and discovery of correlation between crimes.

The “nation” part first discusses data from the national census or surveys that are in general easily accessible. Such data can contribute to the improvement of national social and economic policies. The other data source discussed here is call detail records (CDRs), which are logs of communications and transactions owned by Internet companies. Although their access is especially difficult and there are many issues related to privacy, these data are useful for modeling human mobility and the dynamics of communities, especially the sensitive ones, and also for decision making and strategy planning during emergency situations or disasters. This part also contains discussions on the potential of major Internet data collectors (Google, Facebook, Twitter) and from banking transactions.

The final scale is the globe, so the last part is devoted to world data, with a main focus on the universal spread of infectious diseases. This part discusses transnational data from international organizations, passenger data routes from national and international airlines, routes of cargo ships, special terms from Google search queries, social network discussions, and CDRs. All of these sources can provide information that can be embedded in epidemiological models for improving the prediction of disease outbreaks.

In conclusion, the book can be read by anyone interested in the future trends of technology and science, without requiring special background. It is recommended to students, teachers, researchers, and generally to anyone seeking to discover new areas and opportunities in business and research.

More reviews about this item: Amazon, Goodreads

Reviewer:  Lefteris Angelis Review #: CR143093 (1505-0351)
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Data Mining (H.2.8 ... )
 
 
Life And Medical Sciences (J.3 )
 
 
Social And Behavioral Sciences (J.4 )
 
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