The Results of Reality Mining at Where 2.0(Where 2.0上的现实挖掘)
Brady Forrest
2008/05/20
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Your cellphone and laptop leave an invisible trail. Collecting this data is known as Reality Mining. A formal definition of Reality Mining is "the collection of machine-sensed environmental data pertaining to human social behavior". We had two such collection projects this year at Where 2.0. They each took advantage of the signals that our inadvertent sensors (mobile phones and laptops primarily) broadcast. Several times through out the conference I told the attendees about the projects and showed them the same visualizations shared in this post. The goal in having them at Where 2.0 was two-fold. First, Where 2.0 is a conference on location, It's the goal of the conference to show the latest technology at the intersection of location and technology. Reality Mining applications definitely fit that description. Though our attendees' devices are undoubtedly tracked without their knowledge all the time we wanted to make the applications. Secondly, as an event organizer knowing even a little more about attendees group actions is very valuable. We received a little bit of useful data, but there definitely would need to be refinements before it could be considered one of our tools for judging how well a talk was received (the number of new http requests might be more accurate than the movement of attendees).
Path Intelligence is able to detect GSM signals from cellphones. We used it to learn more about the attendees of Where 2.0. The embedded slides show where our attendees are from and when they were in the session rooms vs. the hallway. One of the slides also shows the path of a single cellphone. All of the data that was collected is anonymous. The team behind Path Intelligence were on-hand to discuss the application and they were usually busy explaining See the TechCrunch post for more information on the company. (Disclosure: OATV has invested in the company)
Leonard Lin, co-founder of Upcoming, has put together a fun reality mining project for the conference. A couple of weeks ago Leonard created Fireball (also profiled on TechCrunch) to let attendees check in locations at the Web 2.0 Expo (like Dodgeball). We got to talking about how on could use BlueTooth to do the same thing at a more contained conference. He took on the challenge and turned three Mac Minis into sensors to be used at the conference. He wasn't able to detect signal strength via the native BlueTooth stack on the Minis so there is no proximity or geolocationbut he was able to collect some interesting info like the number of devices. He used Nodebox to create the visualizations. Future versions might be on the much cheaper and smaller Gumstix hardware.
Thanks Toby, Sharon and Leonard for your hard work. |
你的手机和笔记本电脑留下了一条看不见的轨迹。搜集这些数据信息叫做现实挖掘。正式的定义是“搜集与人类社会行为有关的机器可感知的环境数据”。在今年的Where 2.0上有两个这样的项目。是利用我们忽视的一些传感器(主要是手机和笔记本电脑)所广播的信号。在会上我几次跟与会人员讲到这些项目并且展示了下面这些可视化信息。 在Where 2.0上做这些项目有两个目的。首先，Where 2.0是一个关于位置的会议。其目的就是要展示关于位置技术的最新进展。现实挖掘恰恰符合这一点。尽管与会者的设备并不知道被跟踪了我还是想做这个应用。第二，作为活动的组织者任何关于参加者集体活动的信息都是有价值的。我们得到了一些有用的数据，但是想作为判断一个演讲是否很好地被大家接受（新Http请求数可能更精确）的工具，绝对还需要进一步改进。 Path Intelligence能够监测手机的GSM信号。我们通过它来了解Where 2.0与会人员的更多情况。嵌入的幻灯片显示了与会者来自哪里，什么时候他们在会议室里，什么时候他们会在走廊里。一张幻灯片还显示了单独一个手机的路径。所有这些被搜集的数据都是匿名的。Path Intelligence团队在现场讨论了这个应用，而且忙于解释情况。这个公司的更多信息可以参考TechCrunch帖子。（批露一下：OATV投资了该公司。） Upcoming创始人之一Leonard Lin也为这次会议准备了一个现实挖掘项目。此前Leonard为Web 2.0博览会准备了让参会者登记位置（就像Dodgeball）的Fireball(Techcrunch也有报道)。我们谈到如何在会议上使用蓝牙来做同样的事情。他接受了这个挑战，将三个Mac Mini改成传感器在会议上使用。结果通过Mini自己的蓝牙栈没能检测出信号强度。但是仍然搜集了一些像设备号这样的有趣信息。他用Nodebox做了可视化。未来可能会用更便宜更聪明的Gumstix硬件。 这些BlueBall数据一定程度上泄漏了人的身份。就如你在图里面看到的有时候蓝牙设备的名字被修改了从而泄漏了个人信息。其它情况下仅仅是个设备数字。随着这些传感器的普及（也许大家会认识到你经过一台计算机的时候它就监测到了你的蓝牙信号）我在想这样的设备名会改变什么？ 感谢Toby、Sharon和Leonard的辛苦工作。 |
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