Device Analyzer collects usage statistics in the background while you use your phone.
This data is stripped of personally identifying information as best as possible while preserving useful information. Periodically the recorded data is uploaded to our server at the University of Cambridge, where we will aggregate it with other people's data and draw inferences from the patterns that emerge.
What's in for me?
- Personal analytics - extracting data from the things we do every day, e.g. phone calls or our movement patterns - is becoming increasingly popular. We provide you with new ways to look at what is happening inside your mobile phone!
- The website allows you to download all the raw data we collect from your phone
- There are some basic analytics at the moment but development is ongoing and more will follow soon:
- For example, we can recommend you the best phone plan based on your historical usage
- or recommend apps that you may be interested in based on which apps you use the most.
- New features will use historic data and so it's worth starting to collect now!
Why do we collect your data?
Perhaps surprisingly, it is not well understood what people do with their smart phones. Which of the many features do you use? And how often? Do you often miss calls? How many text messages do you send? Mobile phone carriers know your calling patterns but often don't make them available to manufacturers. They in turn conduct interviews and surveys to see what people like about their phones and what they use them for, but these are always limited to a relatively small group of people.
Device Analyzer performs rigorous, automatic collection. It doesn't get tired after a week of recording what you have done and can provide a large and detailed data set covering a broad audience. We will use this data set to make recommendations for the improvement of future smart phones, extract patterns and trends and will let you benefit from our insights with cool new statistics and recommendations based on what other people do.
Here are some publications so far using Device Analyzer data:
- Daniel Wagner, Andrew Rice and Alastair Beresford, Device Analyzer: Understanding smartphone usage, 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Tokyo, Japan, December 2013 [pdf]
- Daniel Wagner, Andrew Rice and Alastair Beresford, Device Analyzer: Large-scale mobile data collection, Big Data Analytics workshop (in conjunction with ACM Sigmetrics 2013), Pittsburgh, PA, USA, June 2013 [pdf]
- Ning Ding, Daniel Wagner, Xiaomeng Chen, Abhinav Pathak, Y. Charlie Hu, Andrew Rice, Characterizing and Modeling the Impact of Signal Strength on Smartphone Energy Consumption, Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems, Pittsburgh, PA, USA, June 2013 [pdf]
- Daniel T. Wagner, Andrew Rice and Alastair R. Beresford. Device Analyser. In HOTMOBILE 2011 12th Workshop on Mobile Computing Systems and Applications, Mar 2011. [pdf],[poster]
With your permission, we would like to share your data set with other researchers worldwide so that they can get even more from it! If you don't want that you can just let us know with a single click and your data will only be used within the University of Cambridge.
What is collected?
Here is an overview of the types of data we collect:
- when you turn on your phone or charge it
- when you make phone calls and send texts
- which apps you use
- wifi and bluetooth devices near your phone
- your coarse (network-based location)
A detailed document containing all data collected is available here.
Obtaining a copy of the dataset
We now have over 100 billion records of Android smartphone usage from over 17,000 devices across the globe. Many of our contributors have agreed to share much of their data with other researchers, and in this competition we invite researchers from around the world to put this data to good use. The dataset is available for free for both academic and industrial researchers. The only restriction we place is to prohibit efforts to directly identify any individual within the dataset.
Phase 1 - access sample dataset: please email us with your your name and a brief description of your idea. We will email back a download link for the data and also any advice we have to offer on how to use the data to help solve the problem or idea you have.
Phase 2 - full dataset: Access to the full dataset requires you to provide a summary of your idea and sign the terms and conditions. Please email us with your name and a description of your idea in less than 250 words. We will email back any advice we have to offer on how to use the data to help solve the problem or idea you have along with the terms and conditions for you to sign. Once we have received your signed terms and conditions, we will enable access to the full archive of data.
So far we have shared data with researchers from: Xerox, Hong Kong University of Science and Technology, IBM, Fudan University (China), University of Glasgow (UK), Intel, Parc, University of Luxembourg, Hasselt University (Belgium), Institute for Research in IT and Random Systems (France), Universitat Rovira i Virgili (Catalonia), Telecom Sudparis, Indiana University (USA), Columbia University (USA), Indian Institute of Technology Roorkee, Microsoft, Nagoya University (Japan), Universität Osnabrück (Germany), Slovenská technická univerzita v Bratislave (Slovakia), Bedarra Research Labs, Universidade Federal de Minas Gerais (Brasil), University of South Australia, Stevens Institute of Technology (USA), Tata Consultancy Services, Uppsala University (Sweden), University of Maryland (USA), Indraprastha Institute of Information Technology Delhi (India), University of Colorado Denver (USA), Korea Advanced Institute of Science and Technology, Technische Universität Berlin, (Germany), National Chengchi University (Taiwan), University of California Davis (USA), The University of Milan (Italy), Luleå University of Technology (Sweden), Royal Melbourne Institute of Technology (Australia), Imperial College (UK), University of Texas Arlington (USA), Cardiff University (UK), Universidad de Sevilla (Spain), Central South University of China, Alcatel-Lucent, AT&T, Nanjing University (China), Poznań University of Economics (Poland), Samsung Research (USA), CICESE (Mexico), Università degli Studi di Parma (Italy), University of Luxembourg, Technicolor, Aalto University (Finland), Birkbeck University of London (UK), NIC Chile Research Labs, KTH Royal Institute of Technology (Sweden), North Carolina State University (USA), Nanyang Technological University (Singapore), Telefonica Research and Development (Spain), UC Irvine (USA), NUS Singapore University, Ohio State University (USA), Università degli Studi di Pisa (Italy), Colorado School of Mines (USA), Carnegie Mellon University (USA), ERNET (India), Università Ca' Foscari Venezia (Italy), NEC Labs (Europe), Blekinge Tekniska Högskola (Sweden), Karlsruhe Institute of Technology (Germany), University of Groningen (Netherlands), RWTH Aachen University (Germany), MIT (USA), Arizona State University (USA), Hanyang University (Korea), University at Buffalo (USA), Zhejiang University (China), Distributed Artificial Intelligence Laboratory (Germany), Old Dominion University (USA), Die Johannes Kepler Universität in Linz (Austria), Pitney Bowes, University of Massachusetts Lowell (USA), KDDI R&D Laboratories (Japan), The University of Tokyo (Japan), Hochschule RheinMain (Germany), Singapore Management University, Cornell University (USA), UC Berkeley (USA), University of Michigan (USA), University of Lancaster (UK), Dartmouth College (USA), NICTA (Australia), State University of New York (Korea), Liverpool John Moores University (UK), University of Oulu (Finland), Intersec Group, Spotify, TÉLUQ (Canada), Technische Universität Darmstadt (Germany), The University of Melbourne (Australia), University of South Carolina (USA), University of Helsinki (Finland), Cisco, Ben-Gurion University of the Negev (Israel), CETYS Universidad Baja California (Mexico)
Other research projects
If you are interested in Device Analyzer the following research projects may interest you:
- Portolan collects signal coverage and measures connectivity to create a map of the internet. Developed at the University of Pisa.
- Carat analyses mobile usage and gives recommendations on how to lower battery consumption. Developed at UC Berkeley.
- Open Signal creates mobile signal coverage maps separate for each carrier.
- pressureNET tracks atmospheric pressure changes over time using the barometer in participants' Android devices for meteorological research.
- Memorit is a contextual reminder app that collects social data, both proximity and online data to understand pervasive social context, in the Proximates project at Lund University.
You can reach the authors of Device Analyzer for comments or suggestions at dtg-android at cl.cam.ac.uk