Project Aura: Toward Distraction-Free Pervasive Computing
IEEE Pervasive Computing
IEEE Transactions on Knowledge and Data Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
LiveLab: measuring wireless networks and smartphone users in the field
ACM SIGMETRICS Performance Evaluation Review
FAST: quick application launch on solid-state drives
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
Mobile apps: it's time to move up to CondOS
HotOS'13 Proceedings of the 13th USENIX conference on Hot topics in operating systems
Profiling resource usage for mobile applications: a cross-layer approach
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
PocketWeb: instant web browsing for mobile devices
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Software abstractions for trusted sensors
Proceedings of the 10th international conference on Mobile systems, applications, and services
IODetector: a generic service for indoor outdoor detection
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
ACM Transactions on Embedded Computing Systems (TECS) - Special section on ESTIMedia'12, LCTES'11, rigorous embedded systems design, and multiprocessor system-on-chip for cyber-physical systems
A framework for context-aware privacy of sensor data on mobile systems
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Proceedings of the 2013 international conference on Intelligent user interfaces
Prefetching mobile ads: can advertising systems afford it?
Proceedings of the 8th ACM European Conference on Computer Systems
Personal cloudlets for privacy and resource efficiency in mobile in-app advertising
Proceedings of the first international workshop on Mobile cloud computing & networking
Practical prediction and prefetch for faster access to applications on mobile phones
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Prophet: what app you wish to use next
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Proceedings of the 2013 International Symposium on Wearable Computers
On mining mobile apps usage behavior for predicting apps usage in smartphones
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Why application errors drain battery easily?: a study of memory leaks in smartphone apps
Proceedings of the Workshop on Power-Aware Computing and Systems
User analytics with UbeOne: insights into web printing
Proceedings of the VLDB Endowment
PhoneLab: A Large Programmable Smartphone Testbed
Proceedings of First International Workshop on Sensing and Big Data Mining
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As mobile apps become more closely integrated into our everyday lives, mobile app interactions ought to be rapid and responsive. Unfortunately, even the basic primitive of launching a mobile app is sorrowfully sluggish: 20 seconds of delay is not uncommon even for very popular apps. We have designed and built FALCON to remedy slow app launch. FALCON uses contexts such as user location and temporal access patterns to predict app launches before they occur. FALCON then provides systems support for effective app-specific prelaunching, which can dramatically reduce perceived delay. FALCON uses novel features derived through extensive data analysis, and a novel cost-benefit learning algorithm that has strong predictive performance and low runtime overhead. Trace-based analysis shows that an average user saves around 6 seconds per app startup time with daily energy cost of no more than 2% battery life, and on average gets content that is only 3 minutes old at launch without needing to wait for content to update. FALCON is implemented as an OS modification to the Windows Phone OS.