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Data speculation support for a chip multiprocessor
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A Survey of Association-Rule Mining
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Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Speculative execution in a distributed file system
ACM Transactions on Computer Systems (TOCS)
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TRAcME: Temporal Activity Recognition Using Mobile Phone Data
EUC '08 Proceedings of the 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing - Volume 01
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SoundSense: scalable sound sensing for people-centric applications on mobile phones
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Decentralized Detection of Group Formations from Wearable Acceleration Sensors
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Finding optimal satisficing strategies for and-or trees
Artificial Intelligence
MAUI: making smartphones last longer with code offload
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Energy-accuracy trade-off for continuous mobile device location
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IEEE Communications Magazine
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Activity recognition using cell phone accelerometers
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Indoor localization without infrastructure using the acoustic background spectrum
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
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The pipelined set cover problem
ICDT'05 Proceedings of the 10th international conference on Database Theory
Virtual compass: relative positioning to sense mobile social interactions
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
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Towards real-time, fine-grained energy analytics in buildings through mobile phones
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
NuActiv: recognizing unseen new activities using semantic attribute-based learning
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Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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iMac: strategy-proof incentive mechanism for mobile crowdsourcing
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
Maximum likelihood analysis of conflicting observations in social sensing
ACM Transactions on Sensor Networks (TOSN)
PhoneLab: A Large Programmable Smartphone Testbed
Proceedings of First International Workshop on Sensing and Big Data Mining
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We propose ACE (Acquisitional Context Engine), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user's current context to applications running on it. In addition, it dynamically learns relationships among various context attributes (e.g., whenever the user is Driving, he is not AtHome). ACE exploits these automatically learned relationships for two powerful optimizations. The first is inference caching that allows ACE to opportunistically infer one context attribute (AtHome) from another already-known attribute (Driving), without acquiring any sensor data. The second optimization is speculative sensing that enables ACE to occasionally infer the value of an expensive attribute (e.g., AtHome) by sensing cheaper attributes (e.g., Driving). Our experiments with two real context traces of 105 people and a Windows Phone prototype show that ACE can reduce sensing costs of three context-aware applications by about 4.2x, compared to a raw sensor data cache shared across applications, with a very small memory and processing overhead.