面向移动环境的高效情景数据挖掘及节能感知方法研究
|
摘要
移动环境中的情景感知是移动智能应用的基础。随着移动设备的飞速发展,内嵌在移动设备中的传感器越来越丰富多样,如GPS传感器,光学传感器等。因为用户一般随身携带移动设备,所以移动设备上内嵌的传感器搜集的信息能充分反映用户的情景。这些信息被称为情景数据。移动情景感知就是从移动用户的情景数据中挖掘出有用的情景以及情景相关的知识。通过情景感知可以学习出用户行为模式,更深入的了解用户。目前,一些移动应用已经使用情景感知来了为移动用户提供智能化、个性化的服务。如根据光强度改变手机屏幕亮度,达到保护用户视力并延长设备续航能力的目的。移动互联网是未来IT发展的主要趋势之一,移动环境下的情景感知和用户行为挖掘也越来越受到研究人员的关注。
移动环境下的情景感知有着新的特性。首先,移动设备的计算能力、存储能力有限,对情景感知及相关应用有着较强的实时性需求。其次,与传统数据相比移动情景数据含有更为丰富的情景信息,如移动用户所到之处的情景信息；同时这些数据是不平衡的,这些特征使传统的数据挖掘方法不能直接应用于移动情景感知。本文针对移动环境下的新特征,将数据挖掘与移动情景感知相结合,对移动环境的高效用户行为挖掘和节能情景感知方法进行深入研究,其中,用户行为模式挖掘可以应用于移动推荐和移动用户研究,节能情景感知作为情景信息平台为智能应用提供信息。本文的主要的研究内容和创新之处如下：
首先,针对移动情景数据的特点,提出一种高效的移动用户行为模式挖掘方法。传统的关联挖掘方法无法直接用于移动环境下的用户行为模式挖掘,这是由于情景信息与传统的事务项相比具有稀疏性；而现有的用户行为模式挖掘方法还不成熟,计算效率限制了这些方法的实际应用,无法向移动应用提供商及时反馈用户需求,还会造成情景感知应用启动周期长。本文通过研究现有的优化策略,提出了一种高效的用户行为模式挖掘算法。该方法能在移动设备计算能力、存储能力有限的情况下快速挖掘用户的行为模式。本文使用与诺基亚中国研究院合作采集的10个志愿者一个月的情景数据在真机上进行实验,结果表明算法在时间和空间开销上都有很大的提升。
其次,提出一种基于当前状态推断的节能情景感知模型。移动设备内嵌的传感器在提供丰富的用户数据的同时,也带来了高能耗的弊端。我们认为一台移动设备所有传感器在一个采样点上的值描述的都是相同的用户情景,只是不同传感器从不同方面描述。因此,根据传感器根据能耗和功能,将移动设备的传感器分为低能耗类型,基础类型和高能耗类型,提出了基于当前低能耗和基础类型传感器的输出信息推断高能耗传感器当前状态的模型。该模型根据低能耗类型和基础类型传感器的当前信息,推断高能耗传感器的当前状态,如果推断高能耗传感器处于不稳定状态,则打开这个传感器采集信息,否则使用该传感器最后一个有效值作为输出信息。该模型在真实数据集进行的实验上能降低最多70%的采样次数,同时保证90%以上的信息准确率。
最后,提出一种基于状态时间间隔推断的节能情景感知模型。在真实的环境中,用户处于某状态一般是有持续性的。这种时间上的持续性可以用来预测高能耗传感器在多长时间后状态才改变。那么,只需要在预测的时间到来时,打开高能耗传感器进行采样。其余时刻高能耗传感器处于稳定状态,不需要进行采样,仅使用它最后一个有效值作为输出。从而减少了高能耗传感器的采样次数,延长了设备的续航时间。我们还归纳基于稳定状态推断和基于状态时间间隔预测的模型为一般性的稳定状态推断模型框架,并指出该框架下节能情景感知会出现累积错误,给出了一种减少累积错误的方法。该方法能够明显提节能高情景感知的信息准确率,同时几乎不影响节能效果。实验表明了改进后的模型取得了很好的节能效果,同时信息准确率得到更好的保证。
To enable the intelligent mobile applications of the future, it is important to understand mobile users through the data collected from their mobile devices. In recent years, more and more commercial mobile devices such as smart phones and personal digital assistants are equipped with multiple context sensors including optical sensors,3D accelerometers, GPS sensors, etc, which makes it possible to bring to bear intelligent context-aware applications to ordinary mobile users. The data collected by sensors reflects user context because the mobile device is often carried by mobile user. Moreover, many interesting knowledge can be discovered from the collected context data (e.g., GPS trajectories and usage log) through data mining technologies. Some mobile applications have been used the data to support more intelligent, personalized service for users. For example, auto-adapting user interface according to light intensity can protects use eyes and extends the battery life. Since mobile internet is the one of the main trends in IT development, mobile context sensing and user behavior mining become more and more attentive in future.
There are some new characteristics for mobile context sensing. Firstly, com-puting resource, storage capacity is limited in mobile device, and intelligent ap-plications require high real-time condition. Secondly, compared with traditional context data, mobile context data contains richer context information. It records where user has arrived and some features are sparse(e.g. user interactions), so the traditional data mining technologies can not be directly used in mobile context sensing. In this thesis, we consider these characteristics, and leverage machine learning technologies for context sensing to in-depth study of energy efficient con-text sensing and efficient user behavior mining. To be specific, energy efficient context sensing can be a middleware which provides information to applications' continues context sensing, and user behavior mining can be used for research in mobile user recommendation or user interest. The main research innovations of the thesis are as follows:
Firstly, we propose an efficient algorithm, named BP-Growth, for efficient behavior pattern mining. The existing approaches for mining these behavior pat-terns are not practical in mobile environments due to limited computing resources on mobile devices. To fulfill this crucial void, we investigate optimizing strategies which can be used for improving the efficiency of behavior pattern mining in terms of computing and memory needs. Specifically, we examine typical optimizing s-trategies for association rule mining and study the feasibility of applying them to behavior pattern mining. We use real context data collected from10mobile user for experiments and the experimental results show that BP-Growth outperforms benchmark methods with a significant margin in terms of both computing and memory cost.
Secondly, we propose the Current Status Inference (CSI) model for energy efficient context sensing. The battery capacity of mobile devices becomes the bottleneck of context-aware applications because some context sensors are very energy consuming and cannot continuously work for the sake of user experience. We argue that the outputs of different context sensors of a mobile device may be more or less correlated since they essentially capture the same context at each time point, even though from different perspectives. Intuitively, we may be able to selectively avoid invoking high energy consuming sensors by inferring their statuses from the outputs of other sensors. Thus we group context sensors into the basic sensors, the light-duty sensors and the heavy-duty sensors based on their energy consumption and function, and then propose a CSI model. A CSI model will infer the status of a heavy-duty sensor according to output of basic/light-duty sensor. If the heavy-duty sensor is in stable status, the system will not invoke it again and instead use the latest invoked value. The experimental results on real data sets show that the energy efficiency of GPS sensing and audio level sensing are significantly improved by the proposed approach while the sensing accuracy is over90%.
Finally, we propose the Status Interval Inference (SII) Model for energy effi-cient context sensing. In real world, a mobile user is generally in a state continuing for a period of time. A well trained model can be used to infer the time how long the heavy-duty sensor will stay in stable status. To be specific, we propose a SII model which can use the output of basic/light-duty sensor to infer the interval time how long the heavy-duty sensor will stay in stable status. If the heavy-duty sensor will switch to unstable status after some intervals, the system will not invoke it until these intervals are over and instead use the latest invoked value. Then we summary the CSI and SII models to a general Stable Status Inference (SSI) model and propose its framework and use maximum update interval for reducing the probable accumulated errors in their estimations. The experimental results also show that the energy efficiency of GPS sensing and audio level sensing are significantly improved by the proposed approach while the sensing accuracy is high and stable.
移动环境下的情景感知有着新的特性。首先,移动设备的计算能力、存储能力有限,对情景感知及相关应用有着较强的实时性需求。其次,与传统数据相比移动情景数据含有更为丰富的情景信息,如移动用户所到之处的情景信息；同时这些数据是不平衡的,这些特征使传统的数据挖掘方法不能直接应用于移动情景感知。本文针对移动环境下的新特征,将数据挖掘与移动情景感知相结合,对移动环境的高效用户行为挖掘和节能情景感知方法进行深入研究,其中,用户行为模式挖掘可以应用于移动推荐和移动用户研究,节能情景感知作为情景信息平台为智能应用提供信息。本文的主要的研究内容和创新之处如下：
首先,针对移动情景数据的特点,提出一种高效的移动用户行为模式挖掘方法。传统的关联挖掘方法无法直接用于移动环境下的用户行为模式挖掘,这是由于情景信息与传统的事务项相比具有稀疏性；而现有的用户行为模式挖掘方法还不成熟,计算效率限制了这些方法的实际应用,无法向移动应用提供商及时反馈用户需求,还会造成情景感知应用启动周期长。本文通过研究现有的优化策略,提出了一种高效的用户行为模式挖掘算法。该方法能在移动设备计算能力、存储能力有限的情况下快速挖掘用户的行为模式。本文使用与诺基亚中国研究院合作采集的10个志愿者一个月的情景数据在真机上进行实验,结果表明算法在时间和空间开销上都有很大的提升。
其次,提出一种基于当前状态推断的节能情景感知模型。移动设备内嵌的传感器在提供丰富的用户数据的同时,也带来了高能耗的弊端。我们认为一台移动设备所有传感器在一个采样点上的值描述的都是相同的用户情景,只是不同传感器从不同方面描述。因此,根据传感器根据能耗和功能,将移动设备的传感器分为低能耗类型,基础类型和高能耗类型,提出了基于当前低能耗和基础类型传感器的输出信息推断高能耗传感器当前状态的模型。该模型根据低能耗类型和基础类型传感器的当前信息,推断高能耗传感器的当前状态,如果推断高能耗传感器处于不稳定状态,则打开这个传感器采集信息,否则使用该传感器最后一个有效值作为输出信息。该模型在真实数据集进行的实验上能降低最多70%的采样次数,同时保证90%以上的信息准确率。
最后,提出一种基于状态时间间隔推断的节能情景感知模型。在真实的环境中,用户处于某状态一般是有持续性的。这种时间上的持续性可以用来预测高能耗传感器在多长时间后状态才改变。那么,只需要在预测的时间到来时,打开高能耗传感器进行采样。其余时刻高能耗传感器处于稳定状态,不需要进行采样,仅使用它最后一个有效值作为输出。从而减少了高能耗传感器的采样次数,延长了设备的续航时间。我们还归纳基于稳定状态推断和基于状态时间间隔预测的模型为一般性的稳定状态推断模型框架,并指出该框架下节能情景感知会出现累积错误,给出了一种减少累积错误的方法。该方法能够明显提节能高情景感知的信息准确率,同时几乎不影响节能效果。实验表明了改进后的模型取得了很好的节能效果,同时信息准确率得到更好的保证。
To enable the intelligent mobile applications of the future, it is important to understand mobile users through the data collected from their mobile devices. In recent years, more and more commercial mobile devices such as smart phones and personal digital assistants are equipped with multiple context sensors including optical sensors,3D accelerometers, GPS sensors, etc, which makes it possible to bring to bear intelligent context-aware applications to ordinary mobile users. The data collected by sensors reflects user context because the mobile device is often carried by mobile user. Moreover, many interesting knowledge can be discovered from the collected context data (e.g., GPS trajectories and usage log) through data mining technologies. Some mobile applications have been used the data to support more intelligent, personalized service for users. For example, auto-adapting user interface according to light intensity can protects use eyes and extends the battery life. Since mobile internet is the one of the main trends in IT development, mobile context sensing and user behavior mining become more and more attentive in future.
There are some new characteristics for mobile context sensing. Firstly, com-puting resource, storage capacity is limited in mobile device, and intelligent ap-plications require high real-time condition. Secondly, compared with traditional context data, mobile context data contains richer context information. It records where user has arrived and some features are sparse(e.g. user interactions), so the traditional data mining technologies can not be directly used in mobile context sensing. In this thesis, we consider these characteristics, and leverage machine learning technologies for context sensing to in-depth study of energy efficient con-text sensing and efficient user behavior mining. To be specific, energy efficient context sensing can be a middleware which provides information to applications' continues context sensing, and user behavior mining can be used for research in mobile user recommendation or user interest. The main research innovations of the thesis are as follows:
Firstly, we propose an efficient algorithm, named BP-Growth, for efficient behavior pattern mining. The existing approaches for mining these behavior pat-terns are not practical in mobile environments due to limited computing resources on mobile devices. To fulfill this crucial void, we investigate optimizing strategies which can be used for improving the efficiency of behavior pattern mining in terms of computing and memory needs. Specifically, we examine typical optimizing s-trategies for association rule mining and study the feasibility of applying them to behavior pattern mining. We use real context data collected from10mobile user for experiments and the experimental results show that BP-Growth outperforms benchmark methods with a significant margin in terms of both computing and memory cost.
Secondly, we propose the Current Status Inference (CSI) model for energy efficient context sensing. The battery capacity of mobile devices becomes the bottleneck of context-aware applications because some context sensors are very energy consuming and cannot continuously work for the sake of user experience. We argue that the outputs of different context sensors of a mobile device may be more or less correlated since they essentially capture the same context at each time point, even though from different perspectives. Intuitively, we may be able to selectively avoid invoking high energy consuming sensors by inferring their statuses from the outputs of other sensors. Thus we group context sensors into the basic sensors, the light-duty sensors and the heavy-duty sensors based on their energy consumption and function, and then propose a CSI model. A CSI model will infer the status of a heavy-duty sensor according to output of basic/light-duty sensor. If the heavy-duty sensor is in stable status, the system will not invoke it again and instead use the latest invoked value. The experimental results on real data sets show that the energy efficiency of GPS sensing and audio level sensing are significantly improved by the proposed approach while the sensing accuracy is over90%.
Finally, we propose the Status Interval Inference (SII) Model for energy effi-cient context sensing. In real world, a mobile user is generally in a state continuing for a period of time. A well trained model can be used to infer the time how long the heavy-duty sensor will stay in stable status. To be specific, we propose a SII model which can use the output of basic/light-duty sensor to infer the interval time how long the heavy-duty sensor will stay in stable status. If the heavy-duty sensor will switch to unstable status after some intervals, the system will not invoke it until these intervals are over and instead use the latest invoked value. Then we summary the CSI and SII models to a general Stable Status Inference (SSI) model and propose its framework and use maximum update interval for reducing the probable accumulated errors in their estimations. The experimental results also show that the energy efficiency of GPS sensing and audio level sensing are significantly improved by the proposed approach while the sensing accuracy is high and stable.
引文
[1]国际电信联盟ITU. The World in 2011:ICT Facts and Figures. http://www.itu.int/ITU-D/ict/ statistics/.[2]中华人民共和国工业和信息化部.2011年7月全国通信业运行状况,August,2011. http: //www.miit.gov.cn/n11293472/n11293832/n11294132/n12858447/14104156.html.
[3]艾瑞网.2010-2011年中国移动互联网行业年度监测报告简版.2011
[4]Schilit N, Want R. Context-aware computing applications. Mobile Computing Systems and applications, WMCSA 1994, First Workshop on,1994.85-90.
[5]Stanley M. The Mobile Internet Report. December,2009.
[6]Schmidt A, Beigl M, Gellersen H W. There is more to context than location. Computers and Graphics,1999.23(6):893-902.
[7]Schmidt A, Aidoo K A. Takaluoma A, et al. Advanced interaction in context. In Proceedings of First International Symposium on Handheld and Ubiquitous Computing,1999.89-101.
[8]Anind K D, Gregory D A. Towards a Better Understanding of context and context-awareness. Technical Report GIT-GVU-99-22, Georgia Institute of Technology, College of Computing,1999..
[9]Want R, Hopper A, Veronica F. et al. The Active Badge location system. ACM Transactions on Information Systems,1992,10(1):91-102.
[10]Weiser M. Some computer science issues in ubiquitous computing. Communications of the ACM, 1993,36(7):75-84.
[11]Pascoe J. Adding generic contextual capabilities to wearable computers. In Proceedings of the Second International Symposium on Wearable Computers,1998..
[12]Tapia E M, Stephen S I, Larson K. Activity recognition in the home setting using simple and ubiquitous sensors. IEEE Proc of PERVASIVE 2004,2004.158-175.
[13]Philipose M, Kenneth P F, Perkowitz M, et al. Inferring activities from interactions with objects. IEEE Pervasive Computing 2004,2004,3(4):50-57.
[14]Van Someren B, Thijssen W, Speelman J, et al. A new actigraph for long-term registration of the duration and intensity of tremor and movement. IEEE Trans Biorned Eng,1998,45(3):386-395.
[15]Senanarong V, Harnphadungkit K, Prayoonwiwat N, et al. A new measurement of activities of daily living for Thai elderly with dementia. Int Psychogeriatr,2003,15(2):135-148.
[16]Inzitari D, Basile A M. Activities of daily living and global functioning. Int. Psychogcriatr,2003, 15:225-229.
[17]Bennett F, Richardson T, Harter A. Teleporting-making applications mobile. In Proceedings of IEEE Workshop on Mobile Computing Systems and Applications,1994.72-84.
[18]Anind K D, Futakawa M, Salber D, et al. The Conference Assistant:Combining Context-Awareness with Wearable Computing. In Proceedings of the 3rd International Symposium on Wearable Computers,1999.21-28.
[19]Yan H, Selker T. Context-aware office assistant. In Proceedings of the 2000 International Con-ference on Intelligent User Interfaces,2000.276-279,
[20]Nokia. Workshop on Large-Scale Sensor Networks and Applications. Kuusamo, Finland,2005. 3-6.
[21]Raento M, Oulasvirta A, Petit R, et al. ContextPhone:a prototyping platform for context-aware mobile applications. Pervasive Computing, IEEE,2005,4(2):51-59.
[22]Korkea-Aho M. Context-Aware Applications Survey. Techreport,2000, (Internetworking Seminar (Tik-110.551)).
[23]Setten M, Pokraev S, Koolwaaij J. Context-Aware Recommendations in the Mobile Tourist Ap-plication COMPASS. Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems. 2004:515-548.
[24]Fahy P, Clarke S. CASS-a middleware for mobile context-aware applications. Proceedings of Workshop on Context Awareness, MobiSys,2004.
[25]Priyantha N B, Miu A K, Balakrishnan H, et al. The cricket compass for context-aware mobile applications. Proceedings of Proceedings of the 7th annual international conference on Mobile computing and networking, New York, NY, USA:ACM,2001.1-14.
[26]Gu T, Pung H K, Zhang D Q. A middleware for building context-aware mobile services. Pro-ceedings of Vehicular Technology Conference,2004. VTC 2004-Spring.2004 IEEE 59th, volume 5, 2004.2656-2660 Vol.5.
[27]Miluzzo E, Lane N D, Fodor K, et al. Sensing meets mobile social networks:The design, imple-mentation and evaluation of the CcnceMc application.2008.337-350.
[28]College D. Mobile Sensing Group. http://sensorlab.cs.dartmouth.edu/.
[29]Mun M, Reddy S, Shilton K, ct al. PEIR:the personal environmental impact report, as a. platform for participatory sensing systems research. Proceedings of in Proc. ACM/USENIX Int. Conf. Mobile Systems, Applications, and Services (MobiSys) Krakow,2009.55-68.
[30]Choudhury T, Consolvo S, Harrison B, et al. The Mobile Sensing Platform:An Embedded System for Activity Recognition. IEEE Pervasive Comp.,2008,7(2):32-41.
[31]Honicky R, Erir A B, Paulos E, et, al. N-Smarts:Net worked Suite of Mobile Atmospheric Real-Time Sensors. Proc.2nd ACM SIGCOMM NSDR.,2008.25-30.
[32]Poh M Z, Kim K, Goessling A, et al. Heartphones:Sensor Earphones and Mobile Application for Non-Obtrusive Health Monitoring. IEEE Int, Symp. Wearable Comp.,2009.153-154.
[33]Burke J, Estrin D, Hansen M, et. al. Participatory Sensing. In:Workshop on World-Sensor-Web (WSW 06):Mobile Device Centric Sensor Networks and Applications,2006.117-134.
[34]Lu H, Pan W, Nicholas D L, et al. Sound-Sense:Scalable Sound Sensing for People-Centric Applications on Mobile Phones. Proc.7th ACM MobiSys,2009.165-178.
[35]Thiagarajan A, Ravindranath L, Lacurts K, et al. VTrack:Accurate, Energy-aware R.oad Traffic Delay Estimation Using Mobile Phones. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems..
[36]Consolvo S, Mcdonald D W, Toscos T, et al. Activity Sensing in the Wild:A Field Trial of UbiFit Garden. Proceedings of Proc. of CHI' 08,2008.1797-1806.
[37]Campbell A T, Eisenman S B. Lane N D, et, al. People-centric urban sensing. Proceedings of In The Second Annual International Wireless Internet Conference (WICON. IEEE Computer Society Press,2006.2-5.
[38]CENS/UCLA. Participatory Sensing/Urban Sensing Projects. http://research.cens.ucla.edu/.
[39]Berkeley U, Nokia, NAVTEQ. Mobile Millennium. http://traffic.berkeley.edu/.
[40]R.ana R. K, Chou C T, Kanhere S S, et al. Ear-phone:an end-to-end participatory urban noise mapping system. Proceedings of Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, New York, NY, USA:ACM,2010.105-116.
[41]Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules.1994.487-499.
[42]Zaki M J, Hsiao C. CHARM:An efficient algorithm for closed itemset mining.2002.457-473.
[43]Nayak J R, Cook D J, Cook D J. Approximate Association Rule Mining. Proceedings of in Proceedings of the Florida Arti Intelligence Research Symposium,2001.259-263.
[44]Liu B, Hsu W, Ma Y. Integrating Classification and Association Rule Mining.1998.80-86.
[45]International C H, Hidber C. Online Association Rule Mining.1999.145-156.
[46]Saygin Y, Verykios V,, et al. Privacy Preserving Association Rule Mining. Proceedings of Proc. of 12th Intl. Workshop on Research Issues in Data Engineering (RIDE,2002.
[47]Tseng V S, Lin K W. Efficient mining and prediction of user behavior patterns in mobile web systems. Information and Software Technology,2006,48(6):357-369.
[48]Mobasher B, Dai H, Luo T, et al. Effective personalization based on association rule discovery from web usage data. Proceedings of Proceedings of the 3rd international workshop on Web information and data management, New York, NY, USA:ACM,2001.9-15.
[49]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining. Commun. ACM,2000,43:142-151.
[50]Perkowitz M, Etzioni O. Towards adaptive Web sites:Conceptual framework and case study. Artificial Intelligence,2000,118(1-2):245-275.
[51]Koufaris M, Kambil A, LaBarbera P A. Consumer Behavior in Web-Based Commerce:An Em-pirical Study. Int.J. Electron. Commerce,2001,6:115-138.
[52]Yang Q, Li T, Wang K. Building Association-Rule Based Sequential Classifiers for Web-Document Prediction. Data Mining and Knowledge Discovery,2004,8:253-273. 10.1023/B:DAMI.0000023675.04946.fl.
[53]Borges J, Levene M. Data Mining of User Navigation Patterns. Proceedings of Web Usage Analysis and User Profiling. Springer Berlin/Heidelberg,2000:92-112.
[54]Chen M, Member S. Park J S, et al. Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering,1998,10:209-221.
[55]Chen Z, Fowler R, Fu A C. Linear time algorithms for finding maximal forward references. Pro-ceedings of Information Technology:Coding and Computing [Computers and Communications], 2003. Proceedings. ITCC 2003. International Conference on,2003.160-164.
[56]Pei J, Han J, Mortazavi-asl B, et al. Mining Access Patterns Efficiently from Web Logs. Pro-ceedings of Proc.2000 Paci c-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'OO, 2000.396-407.
[57]Tan P N, Kumar V. Mining Indirect Associations in Web Data. Proceedings of WEBKDD 2001 —Mining Web Log Data Across All Customers Touch Points. Springer Berlin/Heidelberg,2002: 221-234.
[58]Kyriakakos M, Hadjiefthymiades S, Frangiadakis N, et al. Multi-user Driven Path Prediction Algorithm for Mobile Computing. Database and Expert Systems Applications, International Workshop on,2003,0:191.
[59]Lee A J, Wang Y T. Efficient data mining for calling path patterns in GSM networks. Information Systems,2003,28(8):929-948.
[60]Tseng S, Chan W C. Mining Complete User Moving Paths in a Mobile Environment, in Proceed-ings of 2002 International Workshop on Databases and Software Engineering,2002..
[61]Wang Y, Lim E P, Hwang S Y. Efficient Group Pattern Mining Using Data Summarization. Proceedings of Database Systems for Advanced Applications. Springer Berlin/Heidelberg,2004: 159-175.
[62]Akyildiz I F, McNair J, Ho J S, et al. Mobility Management in Next-Generation Wireless Systems. Proceedings of PROCEEDINGS OF THE IEEE,1999.1347-1384.
[63]Li G, Lam K, Kuo T. Location Update Generation in Cellular Mobile Computing Systems,2001.
[64]Chen J L. Resource allocation for cellular data services using multiagent schemes. Systems, Man, and Cybernetics, Part B:Cybernetics, IEEE Transactions on,2001,31(6):864-869.
[65]Huang J L, Chen M S, Peng W C. Exploring group mobility for replica data allocation in a mobile environment. Proceedings of Proceedings of the twelfth international conference on Information and knowledge management, New York, NY, USA:ACM,2003.161-168.
[66]Saygin Y, Ulusoy O. Exploiting Data Mining Techniques for Broadcasting Data in Mobile Comput-ing Environments. IEEE Transactions on Knowledge and Data Engineering,2002,14:1387-1399.
[67]Tseng S M, Tsui C F. An efficient method for mining associated service patterns in mobile web environments. Proceedings of Proceedings of the 2003 ACM symposium on Applied computing, New York, NY, USA:ACM,2003.455-459.
[68]Cao, H, Bao, T, and Yang, Q et al. An effective approach for mining mobile user habits. Pro-ceedings of Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM'10),2010.1677-1680.
[69]Palen L. Salzman M, Youngs E. Going wireless:behavior & practice of new mobile phone users. Proceedings of Proceedings of the 2000 ACM conference on Computer supported cooperative work, New York, NY, USA:ACM,2000.201-210.
[70]Peebles D, Lu H, Lane N D, et al. Community-Guided Learning:Exploiting Mobile Sensor Users to Model Human Behavior. Proceedings of AAAI'10,2010.-1-1.
[71]Intel, Berkeley U. Urban Atmospheres. http://www.urban-atmospheres.net/.
[72]Kapadia A, Kotz D, Triandopoulos N. Opportunistic sensing:Security challenges for the new paradigm. Proceedings of Communication Systems and Networks and Workshops,2009. COM-SNETS 2009. First International,2009.1-10.
[73]Ganti R K, Pham N, Tsai Y E, et al. PoolView; stream privacy fox grassroots participatory sensing. Proceedings of Proceedings of the 6th ACM conference on Embedded network sensor systems, New York, NY, USA:ACM,2008.281-294.
[74]Cuervo E, Balasubramanian A, Clio D k, et al. MAUI:making smartphones last longer with code offload. Proceedings of Proceedings of the 8th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2010.49-62.
[75]Abowd G, Atkeson C, Hong J, et al. Cyberguide:A mobile context-aware tour guide. Wireless Networks,1997,3:421-433.
[76]Lemlouma. T, Layaida N. Coutext-aware adaptation for mobile devices. Proceedings of Mobile Data Management,2004. Proceedings.2004 IEEE International Conference on,2004.106-111.
[77]Chen Y, Yang Q, Yin J, et al. Power-Efficient Access-Point Selection for Indoor Location Estima-tion. IEEE Transactions on Knowledge and Data Engineering,2006,18:877-888.
[78]Ben Abdesslem F, Phillips A, Henderson T. Less is more:energy-efficient mobile sensing with senseless. Proceedings of Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds, New York, NY, USA:ACM,2009.61-62.
[79]Constandache I, Gaonkar S, Sayler M, et al. EnLoc:Energy-Efficient Localization for Mobile Phones. Proceedings of INFOCOM 2009, IEEE,2009.2716-2720.
[80]Kjaergaard M B, Langdal J, Godsk T, et al. EnTrackcd:energy-efficient robust position tracking for mobile devices. Proceedings of Proceedings of the 7th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2009.221-234.
[81]Paek J, Kim J, Govindan R. Energy-efficient rate-adaptive GPS-based positioning for smart-phones. Proceedings of Proceedings of the 8th international conference on Mobile systems, appli-cations, and services, New York, NY, USA:ACM,2010.299-314.
[82]Zhuang Z, Kim K H, Singh J P. Improving energy efficiency of location sensing on smartphones. Proceedings of Proceedings of the 8th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2010.315-330.
[83]Paleologo G A, Benini L, Bogliolo A, et al. Policy optimization for dynamic power management. Proceedings of Proceedings of the 35th annual Design Automation Conference, New York, NY, USA:ACM,1998.182-187.
[84]Simunic T, Benini L, Glynn P, et al. Dynamic power management for portable systems. Pro-ceedings of Proceedings of the 6th annual international conference on Mobile computing and networking, New York, NY, USA:ACM,2000.11-19.
[85]Garg R, Son S W, Kandemir M, et al. Markov Model Based Disk Power Management for Data Intensive Workloads. Proceedings of Cluster Computing and the Grid,2009. CCGRID'09.9th IEEE/ACM International Symposium on,2009.76-83.
[86]Heinzelman W, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. Proceedings of System Sciences,2000. Proceedings of the 33rd Annual Hawaii International Conference on,2000.10 pp. vol.2.
[87]Younis O, Fahmy S. HEED:A Hybrid, Energy-Efficient. Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing,2004,3:366-379.
[88]Choi W, Shah P, Das S. A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks. Proceedings of Mobile and Ubiquitous Systems:Networking and Services,2004. MOBIQUITOUS 2004. The First Annual International Conference on,2004.203-212.
[89]Campobello G, Leonardi A, Palazzo S. On the Use of Chinese Remainder Theorem for Ener-gy Saving in Wireless Sensor Networks. Proceedings of Communications,2008. ICC '08. IEEE International Conference on,2008.2723-2727.
[90]Chan H, Perrig A. ACE:An Emergent Algorithm lor Highly Uniform Cluster Formation. Pro-ceedings of Wireless Sensor Networks. Springer Berlin/Heidelberg,2004:154-171.
[91]Chong S K, Krishnaswamy S, Loke S W. A Context-Aware Approach to Conserving Energy in Wireless Sensor Networks. Pervasive Computing and Communications Workshops, IEEE Inter-national Conference on,2005,0:401-405.
[92]Chong S K, Gaber M M, Loke S W, et al. ARTS:Adaptive Rule Triggers on Sensors for Energy-Conservation in Applications using Coarse-Granularity Data. Embedded Software and Systems, Second International Conference on,2008,0:314-321.
[93]Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databas-es. SIGMOD Rec.,1993,22:207-216.
[94]Fukuda T, Morimoto Y, Morishita S, et al. Mining optimized association rules for numeric at-tributes. Proceedings of Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART sympo-sium on Principles of database systems, New York, NY, USA:ACM,1996.182-191.
[95]IIan J, Pei J, Yin Y. Mining freqnent patterns without candidate generation. SIGMOD Rec, 2000,29:1-12.
[96]Han, J, Pei, J and Yin, Y et al. Mining frequent patterns without candidate generation:a frequent-pattern tree approach. Data Min. Knowl. Discov.,2004,8(1):53-87.
[97]Wang J, Han J, Pei J. CLOSET+:searching for the best strategies for mining frequent closed itemsets. Proceedings of Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA:ACM,2003.236-245.
[98]Pei J, Han J, Mortazavi-asI B, et al. PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth.2001.215-224.
[99]Chen, E, and Cao, H, and Li, Q et al. Efficient strategies for tough aggregate constraint-based sequential pattern mining. Inf. Sci.,2008,178(6):1498-1518.
[100]Farrahi K, Gatica-Perez D. Discovering routines from large-scale human locations using proba-bilistic topic models. ACM Trans. Intel]. Syst. Technol.,2011,2:3:1-3:27.
[101]Zheng Y, Xie X. Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol.,2011,2:2:1-2:29.
[102]Abdesslcrn F B, Phillips A, Henderson T. Less is more:energy-efficient mobile sensing with senseless. Proceedings of MobiHeld'09:Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds,2009.61-62.
[103]Cortes C, Vapnik V. Support-vector networks. Machine Learning,1995,20(2):273-297.
[104]Suykens J, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters,1999,9:293-300.10.1023/A.1018628609742.
[105]An introduction to support vector machines:and other kernel-based learning methods.1 ed., Cambridge University Press, March,2000.
[106]Quinlan J R. Induction of decision trees. Machine Learning,1986,1:81-106.10.1007/BF00116251.
[107]Quinlan J R. C4.5:Programs for machine learning. Morgan Kaufmann,1993.
[108]Larose D T. Decision Trees. John Wiley & Sons, Inc.,2005:107-127.
[109]Breiman L. Classification and regression trees. Belmont, Calif:Wadsworth International Group, 1984:358.
[110]Vapnik, N V. The nature of statistical learning theory. New York, NY, USA:Springer-Verlag New York, Inc.,1995.
[111]Fan R E, Chen P H, Lin C J. Working set selection using second order information for training SVM. Journal of Machine Learning Research,2005,6:1889-1918.
[112]Breiman L. Bias, variance, and arcing classifiers. Bias Variance and Arcing Classifiers Technical Report 460 Statistics Department University of California,1996, (460).
[113]Magidson J. The use of the new ordinal algorithm in CHAID to target profitable segments. The Journal of Database Marketing,1993.29-48.
[114]Chang C, Lin C. LIBSVM:a library for support vector machines.,2001. Software available at. http://www.csie.ntu.edu.tw/～cjlin/libsvm.
[115]Steinberg D, Colla P. CART-Classification and Regression Trees. Proceedings of Salford Systems, 1997.
[116]http://www.pervasive.jku.at/Rcscarch/Context_Databasc/..
[117]Williamus H G. Conometric Analysis 5th. Prentice Hall,2002:66-67.
[118]Jolliffe T. Principal component analysis (2nd ed.). Hardcover,2002:167-168.
[119]Derksen S, Kcselman H J. Backward, forward and stopwise antomated subset selection algorithm-s:frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology,1992,45:265-282.
[3]艾瑞网.2010-2011年中国移动互联网行业年度监测报告简版.2011
[4]Schilit N, Want R. Context-aware computing applications. Mobile Computing Systems and applications, WMCSA 1994, First Workshop on,1994.85-90.
[5]Stanley M. The Mobile Internet Report. December,2009.
[6]Schmidt A, Beigl M, Gellersen H W. There is more to context than location. Computers and Graphics,1999.23(6):893-902.
[7]Schmidt A, Aidoo K A. Takaluoma A, et al. Advanced interaction in context. In Proceedings of First International Symposium on Handheld and Ubiquitous Computing,1999.89-101.
[8]Anind K D, Gregory D A. Towards a Better Understanding of context and context-awareness. Technical Report GIT-GVU-99-22, Georgia Institute of Technology, College of Computing,1999..
[9]Want R, Hopper A, Veronica F. et al. The Active Badge location system. ACM Transactions on Information Systems,1992,10(1):91-102.
[10]Weiser M. Some computer science issues in ubiquitous computing. Communications of the ACM, 1993,36(7):75-84.
[11]Pascoe J. Adding generic contextual capabilities to wearable computers. In Proceedings of the Second International Symposium on Wearable Computers,1998..
[12]Tapia E M, Stephen S I, Larson K. Activity recognition in the home setting using simple and ubiquitous sensors. IEEE Proc of PERVASIVE 2004,2004.158-175.
[13]Philipose M, Kenneth P F, Perkowitz M, et al. Inferring activities from interactions with objects. IEEE Pervasive Computing 2004,2004,3(4):50-57.
[14]Van Someren B, Thijssen W, Speelman J, et al. A new actigraph for long-term registration of the duration and intensity of tremor and movement. IEEE Trans Biorned Eng,1998,45(3):386-395.
[15]Senanarong V, Harnphadungkit K, Prayoonwiwat N, et al. A new measurement of activities of daily living for Thai elderly with dementia. Int Psychogeriatr,2003,15(2):135-148.
[16]Inzitari D, Basile A M. Activities of daily living and global functioning. Int. Psychogcriatr,2003, 15:225-229.
[17]Bennett F, Richardson T, Harter A. Teleporting-making applications mobile. In Proceedings of IEEE Workshop on Mobile Computing Systems and Applications,1994.72-84.
[18]Anind K D, Futakawa M, Salber D, et al. The Conference Assistant:Combining Context-Awareness with Wearable Computing. In Proceedings of the 3rd International Symposium on Wearable Computers,1999.21-28.
[19]Yan H, Selker T. Context-aware office assistant. In Proceedings of the 2000 International Con-ference on Intelligent User Interfaces,2000.276-279,
[20]Nokia. Workshop on Large-Scale Sensor Networks and Applications. Kuusamo, Finland,2005. 3-6.
[21]Raento M, Oulasvirta A, Petit R, et al. ContextPhone:a prototyping platform for context-aware mobile applications. Pervasive Computing, IEEE,2005,4(2):51-59.
[22]Korkea-Aho M. Context-Aware Applications Survey. Techreport,2000, (Internetworking Seminar (Tik-110.551)).
[23]Setten M, Pokraev S, Koolwaaij J. Context-Aware Recommendations in the Mobile Tourist Ap-plication COMPASS. Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems. 2004:515-548.
[24]Fahy P, Clarke S. CASS-a middleware for mobile context-aware applications. Proceedings of Workshop on Context Awareness, MobiSys,2004.
[25]Priyantha N B, Miu A K, Balakrishnan H, et al. The cricket compass for context-aware mobile applications. Proceedings of Proceedings of the 7th annual international conference on Mobile computing and networking, New York, NY, USA:ACM,2001.1-14.
[26]Gu T, Pung H K, Zhang D Q. A middleware for building context-aware mobile services. Pro-ceedings of Vehicular Technology Conference,2004. VTC 2004-Spring.2004 IEEE 59th, volume 5, 2004.2656-2660 Vol.5.
[27]Miluzzo E, Lane N D, Fodor K, et al. Sensing meets mobile social networks:The design, imple-mentation and evaluation of the CcnceMc application.2008.337-350.
[28]College D. Mobile Sensing Group. http://sensorlab.cs.dartmouth.edu/.
[29]Mun M, Reddy S, Shilton K, ct al. PEIR:the personal environmental impact report, as a. platform for participatory sensing systems research. Proceedings of in Proc. ACM/USENIX Int. Conf. Mobile Systems, Applications, and Services (MobiSys) Krakow,2009.55-68.
[30]Choudhury T, Consolvo S, Harrison B, et al. The Mobile Sensing Platform:An Embedded System for Activity Recognition. IEEE Pervasive Comp.,2008,7(2):32-41.
[31]Honicky R, Erir A B, Paulos E, et, al. N-Smarts:Net worked Suite of Mobile Atmospheric Real-Time Sensors. Proc.2nd ACM SIGCOMM NSDR.,2008.25-30.
[32]Poh M Z, Kim K, Goessling A, et al. Heartphones:Sensor Earphones and Mobile Application for Non-Obtrusive Health Monitoring. IEEE Int, Symp. Wearable Comp.,2009.153-154.
[33]Burke J, Estrin D, Hansen M, et. al. Participatory Sensing. In:Workshop on World-Sensor-Web (WSW 06):Mobile Device Centric Sensor Networks and Applications,2006.117-134.
[34]Lu H, Pan W, Nicholas D L, et al. Sound-Sense:Scalable Sound Sensing for People-Centric Applications on Mobile Phones. Proc.7th ACM MobiSys,2009.165-178.
[35]Thiagarajan A, Ravindranath L, Lacurts K, et al. VTrack:Accurate, Energy-aware R.oad Traffic Delay Estimation Using Mobile Phones. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems..
[36]Consolvo S, Mcdonald D W, Toscos T, et al. Activity Sensing in the Wild:A Field Trial of UbiFit Garden. Proceedings of Proc. of CHI' 08,2008.1797-1806.
[37]Campbell A T, Eisenman S B. Lane N D, et, al. People-centric urban sensing. Proceedings of In The Second Annual International Wireless Internet Conference (WICON. IEEE Computer Society Press,2006.2-5.
[38]CENS/UCLA. Participatory Sensing/Urban Sensing Projects. http://research.cens.ucla.edu/.
[39]Berkeley U, Nokia, NAVTEQ. Mobile Millennium. http://traffic.berkeley.edu/.
[40]R.ana R. K, Chou C T, Kanhere S S, et al. Ear-phone:an end-to-end participatory urban noise mapping system. Proceedings of Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, New York, NY, USA:ACM,2010.105-116.
[41]Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules.1994.487-499.
[42]Zaki M J, Hsiao C. CHARM:An efficient algorithm for closed itemset mining.2002.457-473.
[43]Nayak J R, Cook D J, Cook D J. Approximate Association Rule Mining. Proceedings of in Proceedings of the Florida Arti Intelligence Research Symposium,2001.259-263.
[44]Liu B, Hsu W, Ma Y. Integrating Classification and Association Rule Mining.1998.80-86.
[45]International C H, Hidber C. Online Association Rule Mining.1999.145-156.
[46]Saygin Y, Verykios V,, et al. Privacy Preserving Association Rule Mining. Proceedings of Proc. of 12th Intl. Workshop on Research Issues in Data Engineering (RIDE,2002.
[47]Tseng V S, Lin K W. Efficient mining and prediction of user behavior patterns in mobile web systems. Information and Software Technology,2006,48(6):357-369.
[48]Mobasher B, Dai H, Luo T, et al. Effective personalization based on association rule discovery from web usage data. Proceedings of Proceedings of the 3rd international workshop on Web information and data management, New York, NY, USA:ACM,2001.9-15.
[49]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining. Commun. ACM,2000,43:142-151.
[50]Perkowitz M, Etzioni O. Towards adaptive Web sites:Conceptual framework and case study. Artificial Intelligence,2000,118(1-2):245-275.
[51]Koufaris M, Kambil A, LaBarbera P A. Consumer Behavior in Web-Based Commerce:An Em-pirical Study. Int.J. Electron. Commerce,2001,6:115-138.
[52]Yang Q, Li T, Wang K. Building Association-Rule Based Sequential Classifiers for Web-Document Prediction. Data Mining and Knowledge Discovery,2004,8:253-273. 10.1023/B:DAMI.0000023675.04946.fl.
[53]Borges J, Levene M. Data Mining of User Navigation Patterns. Proceedings of Web Usage Analysis and User Profiling. Springer Berlin/Heidelberg,2000:92-112.
[54]Chen M, Member S. Park J S, et al. Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering,1998,10:209-221.
[55]Chen Z, Fowler R, Fu A C. Linear time algorithms for finding maximal forward references. Pro-ceedings of Information Technology:Coding and Computing [Computers and Communications], 2003. Proceedings. ITCC 2003. International Conference on,2003.160-164.
[56]Pei J, Han J, Mortazavi-asl B, et al. Mining Access Patterns Efficiently from Web Logs. Pro-ceedings of Proc.2000 Paci c-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'OO, 2000.396-407.
[57]Tan P N, Kumar V. Mining Indirect Associations in Web Data. Proceedings of WEBKDD 2001 —Mining Web Log Data Across All Customers Touch Points. Springer Berlin/Heidelberg,2002: 221-234.
[58]Kyriakakos M, Hadjiefthymiades S, Frangiadakis N, et al. Multi-user Driven Path Prediction Algorithm for Mobile Computing. Database and Expert Systems Applications, International Workshop on,2003,0:191.
[59]Lee A J, Wang Y T. Efficient data mining for calling path patterns in GSM networks. Information Systems,2003,28(8):929-948.
[60]Tseng S, Chan W C. Mining Complete User Moving Paths in a Mobile Environment, in Proceed-ings of 2002 International Workshop on Databases and Software Engineering,2002..
[61]Wang Y, Lim E P, Hwang S Y. Efficient Group Pattern Mining Using Data Summarization. Proceedings of Database Systems for Advanced Applications. Springer Berlin/Heidelberg,2004: 159-175.
[62]Akyildiz I F, McNair J, Ho J S, et al. Mobility Management in Next-Generation Wireless Systems. Proceedings of PROCEEDINGS OF THE IEEE,1999.1347-1384.
[63]Li G, Lam K, Kuo T. Location Update Generation in Cellular Mobile Computing Systems,2001.
[64]Chen J L. Resource allocation for cellular data services using multiagent schemes. Systems, Man, and Cybernetics, Part B:Cybernetics, IEEE Transactions on,2001,31(6):864-869.
[65]Huang J L, Chen M S, Peng W C. Exploring group mobility for replica data allocation in a mobile environment. Proceedings of Proceedings of the twelfth international conference on Information and knowledge management, New York, NY, USA:ACM,2003.161-168.
[66]Saygin Y, Ulusoy O. Exploiting Data Mining Techniques for Broadcasting Data in Mobile Comput-ing Environments. IEEE Transactions on Knowledge and Data Engineering,2002,14:1387-1399.
[67]Tseng S M, Tsui C F. An efficient method for mining associated service patterns in mobile web environments. Proceedings of Proceedings of the 2003 ACM symposium on Applied computing, New York, NY, USA:ACM,2003.455-459.
[68]Cao, H, Bao, T, and Yang, Q et al. An effective approach for mining mobile user habits. Pro-ceedings of Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM'10),2010.1677-1680.
[69]Palen L. Salzman M, Youngs E. Going wireless:behavior & practice of new mobile phone users. Proceedings of Proceedings of the 2000 ACM conference on Computer supported cooperative work, New York, NY, USA:ACM,2000.201-210.
[70]Peebles D, Lu H, Lane N D, et al. Community-Guided Learning:Exploiting Mobile Sensor Users to Model Human Behavior. Proceedings of AAAI'10,2010.-1-1.
[71]Intel, Berkeley U. Urban Atmospheres. http://www.urban-atmospheres.net/.
[72]Kapadia A, Kotz D, Triandopoulos N. Opportunistic sensing:Security challenges for the new paradigm. Proceedings of Communication Systems and Networks and Workshops,2009. COM-SNETS 2009. First International,2009.1-10.
[73]Ganti R K, Pham N, Tsai Y E, et al. PoolView; stream privacy fox grassroots participatory sensing. Proceedings of Proceedings of the 6th ACM conference on Embedded network sensor systems, New York, NY, USA:ACM,2008.281-294.
[74]Cuervo E, Balasubramanian A, Clio D k, et al. MAUI:making smartphones last longer with code offload. Proceedings of Proceedings of the 8th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2010.49-62.
[75]Abowd G, Atkeson C, Hong J, et al. Cyberguide:A mobile context-aware tour guide. Wireless Networks,1997,3:421-433.
[76]Lemlouma. T, Layaida N. Coutext-aware adaptation for mobile devices. Proceedings of Mobile Data Management,2004. Proceedings.2004 IEEE International Conference on,2004.106-111.
[77]Chen Y, Yang Q, Yin J, et al. Power-Efficient Access-Point Selection for Indoor Location Estima-tion. IEEE Transactions on Knowledge and Data Engineering,2006,18:877-888.
[78]Ben Abdesslem F, Phillips A, Henderson T. Less is more:energy-efficient mobile sensing with senseless. Proceedings of Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds, New York, NY, USA:ACM,2009.61-62.
[79]Constandache I, Gaonkar S, Sayler M, et al. EnLoc:Energy-Efficient Localization for Mobile Phones. Proceedings of INFOCOM 2009, IEEE,2009.2716-2720.
[80]Kjaergaard M B, Langdal J, Godsk T, et al. EnTrackcd:energy-efficient robust position tracking for mobile devices. Proceedings of Proceedings of the 7th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2009.221-234.
[81]Paek J, Kim J, Govindan R. Energy-efficient rate-adaptive GPS-based positioning for smart-phones. Proceedings of Proceedings of the 8th international conference on Mobile systems, appli-cations, and services, New York, NY, USA:ACM,2010.299-314.
[82]Zhuang Z, Kim K H, Singh J P. Improving energy efficiency of location sensing on smartphones. Proceedings of Proceedings of the 8th international conference on Mobile systems, applications, and services, New York, NY, USA:ACM,2010.315-330.
[83]Paleologo G A, Benini L, Bogliolo A, et al. Policy optimization for dynamic power management. Proceedings of Proceedings of the 35th annual Design Automation Conference, New York, NY, USA:ACM,1998.182-187.
[84]Simunic T, Benini L, Glynn P, et al. Dynamic power management for portable systems. Pro-ceedings of Proceedings of the 6th annual international conference on Mobile computing and networking, New York, NY, USA:ACM,2000.11-19.
[85]Garg R, Son S W, Kandemir M, et al. Markov Model Based Disk Power Management for Data Intensive Workloads. Proceedings of Cluster Computing and the Grid,2009. CCGRID'09.9th IEEE/ACM International Symposium on,2009.76-83.
[86]Heinzelman W, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. Proceedings of System Sciences,2000. Proceedings of the 33rd Annual Hawaii International Conference on,2000.10 pp. vol.2.
[87]Younis O, Fahmy S. HEED:A Hybrid, Energy-Efficient. Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing,2004,3:366-379.
[88]Choi W, Shah P, Das S. A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks. Proceedings of Mobile and Ubiquitous Systems:Networking and Services,2004. MOBIQUITOUS 2004. The First Annual International Conference on,2004.203-212.
[89]Campobello G, Leonardi A, Palazzo S. On the Use of Chinese Remainder Theorem for Ener-gy Saving in Wireless Sensor Networks. Proceedings of Communications,2008. ICC '08. IEEE International Conference on,2008.2723-2727.
[90]Chan H, Perrig A. ACE:An Emergent Algorithm lor Highly Uniform Cluster Formation. Pro-ceedings of Wireless Sensor Networks. Springer Berlin/Heidelberg,2004:154-171.
[91]Chong S K, Krishnaswamy S, Loke S W. A Context-Aware Approach to Conserving Energy in Wireless Sensor Networks. Pervasive Computing and Communications Workshops, IEEE Inter-national Conference on,2005,0:401-405.
[92]Chong S K, Gaber M M, Loke S W, et al. ARTS:Adaptive Rule Triggers on Sensors for Energy-Conservation in Applications using Coarse-Granularity Data. Embedded Software and Systems, Second International Conference on,2008,0:314-321.
[93]Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databas-es. SIGMOD Rec.,1993,22:207-216.
[94]Fukuda T, Morimoto Y, Morishita S, et al. Mining optimized association rules for numeric at-tributes. Proceedings of Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART sympo-sium on Principles of database systems, New York, NY, USA:ACM,1996.182-191.
[95]IIan J, Pei J, Yin Y. Mining freqnent patterns without candidate generation. SIGMOD Rec, 2000,29:1-12.
[96]Han, J, Pei, J and Yin, Y et al. Mining frequent patterns without candidate generation:a frequent-pattern tree approach. Data Min. Knowl. Discov.,2004,8(1):53-87.
[97]Wang J, Han J, Pei J. CLOSET+:searching for the best strategies for mining frequent closed itemsets. Proceedings of Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA:ACM,2003.236-245.
[98]Pei J, Han J, Mortazavi-asI B, et al. PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth.2001.215-224.
[99]Chen, E, and Cao, H, and Li, Q et al. Efficient strategies for tough aggregate constraint-based sequential pattern mining. Inf. Sci.,2008,178(6):1498-1518.
[100]Farrahi K, Gatica-Perez D. Discovering routines from large-scale human locations using proba-bilistic topic models. ACM Trans. Intel]. Syst. Technol.,2011,2:3:1-3:27.
[101]Zheng Y, Xie X. Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol.,2011,2:2:1-2:29.
[102]Abdesslcrn F B, Phillips A, Henderson T. Less is more:energy-efficient mobile sensing with senseless. Proceedings of MobiHeld'09:Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds,2009.61-62.
[103]Cortes C, Vapnik V. Support-vector networks. Machine Learning,1995,20(2):273-297.
[104]Suykens J, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters,1999,9:293-300.10.1023/A.1018628609742.
[105]An introduction to support vector machines:and other kernel-based learning methods.1 ed., Cambridge University Press, March,2000.
[106]Quinlan J R. Induction of decision trees. Machine Learning,1986,1:81-106.10.1007/BF00116251.
[107]Quinlan J R. C4.5:Programs for machine learning. Morgan Kaufmann,1993.
[108]Larose D T. Decision Trees. John Wiley & Sons, Inc.,2005:107-127.
[109]Breiman L. Classification and regression trees. Belmont, Calif:Wadsworth International Group, 1984:358.
[110]Vapnik, N V. The nature of statistical learning theory. New York, NY, USA:Springer-Verlag New York, Inc.,1995.
[111]Fan R E, Chen P H, Lin C J. Working set selection using second order information for training SVM. Journal of Machine Learning Research,2005,6:1889-1918.
[112]Breiman L. Bias, variance, and arcing classifiers. Bias Variance and Arcing Classifiers Technical Report 460 Statistics Department University of California,1996, (460).
[113]Magidson J. The use of the new ordinal algorithm in CHAID to target profitable segments. The Journal of Database Marketing,1993.29-48.
[114]Chang C, Lin C. LIBSVM:a library for support vector machines.,2001. Software available at. http://www.csie.ntu.edu.tw/～cjlin/libsvm.
[115]Steinberg D, Colla P. CART-Classification and Regression Trees. Proceedings of Salford Systems, 1997.
[116]http://www.pervasive.jku.at/Rcscarch/Context_Databasc/..
[117]Williamus H G. Conometric Analysis 5th. Prentice Hall,2002:66-67.
[118]Jolliffe T. Principal component analysis (2nd ed.). Hardcover,2002:167-168.
[119]Derksen S, Kcselman H J. Backward, forward and stopwise antomated subset selection algorithm-s:frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology,1992,45:265-282.