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322 A g r e e m e n t s u p p l e m e n t s mApping ConversAtions mAp loCAtion F, G, 6 threAD loCAtion Page 107 sCApe Assessment Mapping Conversations needs a method mAssive sCAle mAp loCAtion G, H, 2 threAD loCAtion Page 142 sCApe Cataloging Relationships Massive Scale influenced by Author R. David Lankes Figure 175 Figure 176 A g r e e m e n t s u p p l e m e n t s 323 Agreement DesCription In the process of a Transportation Research Board study on information management in the transportation industry, several panel members observed that soon every mile of road will generate a gigabyte of data a day.1 These data will come from road sensors embedded into asphalt to detect temperature for winter salting, real-time traffic data from roadway cameras, weather information, toll data from RadioFrequency Identification (RFID) expressway systems, car black boxes, and a myriad of other data sources. It is assumed that this will become a gigabyte an hour as more and more technology finds its way into our vehicles and management systems (GPS data, real-time environment monitoring, etc.). Because there are 3.5 million miles of highways in the United States, that would be 3.3 petabytes of data per hour or 28 exabytes per year. Some readers may not be familiar with an exabyte. It is the name for a large volume of storage like megabytes, gigabytes (1024 megabytes ), and terabytes (1024 gigabytes), technically 2^60 bytes. Table 32 will give the reader some sense of the scale involved. bYte 1 bYte: a single character; kilobYte 2 kilobYtes: a tYPewritten Page; megabYte 2 megabYtes: a high-resolution PhotograPh ; gigabYte 2 gigabYtes: 20 meters of shelveD books terabYte 2 terabYtes: an acaDemic research librarY PetabYte 2 PetabYtes: all u.s. acaDemic research libraries; exabYte 5 exabYtes: all worDs ever sPoken bY humans. ZettabYte YottabYte What the reader needs to realize is that each succeeding row in the table, from megabyte to gigabyte to terabyte and so forth, is an exponential increase. By and large, people do not think in exponential terms. Gladwell uses the analogy of folding paper to demonstrate just how big the shifts involved in exponential change are. Imagine you have a huge piece of paper.3 Although the paper is large in terms of its width and height, it is only 0.01 inches thick. You fold it in half. You then fold it in half again 50 times. How tall would it be? Many people might say as thick as a phone book or get really brave and predict as high as a refrigerator. The actual answer is approximately the distance between the earth and the sun. How can this be? Certainly if I stack 50 pieces of paper on top of each other the stack would not be that large. However, stacking separate sheets is a linear progression, and that is not what you accomplished by folding the paper. With every fold, you doubled the thickness of the paper. With one fold, the paper is twice as thick as when you started. With the second fold, the paper is four times as thick—the next fold is eight times as thick, and so on. In the first few folds, you do not see a major increase, but at about fold 40 you are doubling a mile. We are not used to thinking in terms of exponential growth because most things we deal with grow linearly. However, technology is not. Predictable Change In 1965, computer pioneer Gordon E. Moore predicted that the number of transistors that could be fit on a chip (roughly equivalent to the speed at which the chip could process information) would double every eighteen months.4 The prediction has become so reliable it is referred to as Moore’s Law. The law is an exponential change just like the paper folding. Computers have not just gotten faster over the past decade, they have gotten exponentially faster. What is more, currently , makers of storage technologies—hard drives, solid-state flash memory, and the like—are exceeding Moore’s Law. The emergence of massive-scale computing in our every day lives is a predictable change unlike the web. 1. Transportation Research Board, Committee for a Future Strategy for Transportation Information Management (2006). Transportation knowledge networks: A management strategy for the 21st century. TRB Special Report 284, Washington D.C.


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