@InProceedings{ 5159,
	title = "Metroscope: Augmenting Urban Life Experience by Employing Collective City Intelligence",
	booktitle = "Mobisys 2009",
	author = "Youngki Lee and Seungwoo Kang and Sang Jeong Lee and Byoungjip Kim and Hyunju Kong",
	year = "2009",
	abstract = "We envision a highly interactive future city for the quality lives of city residents. In this paper, we propose Metroscope, a novel computational framework to effectively support residents to design and attain rich urban experiences. Metroscope first develops a novel model to describe urban experiences in a large-scale city. Also, we design and implement the Metroscope system to facilitate collective urban experience sharing reflecting city dynamics. More important, we develop highly efficient real-time activity pattern processing techniques to deal with massive scales of a city. We have field-tested our prototype system in a subpart of Daejeon city in Korea. We perform the focus-group interview and performance study. They show the effectiveness and efficiency of the Metroscope system.",
	localfile = "/home/stephan/Daten/Arbeit/Paper\_Tutorials/Paper/090101\_5159\_MetroscopeAugmentingUrbanLifeExperienceByEmployingCollectiveCityIntelligence.pdf",
	Bemerkung = "Habe ich fuer die Mobisys 2009 gereviewed. The authors propose a framework (Server and mobile client) that acquires, processes and utilises context information to provide recommendations for possible activities and interesting locations in a city environment. Recommendations are based on context- and profile information of users. The paper is well written and details a complete system to support and utilise pervasive context information. The authors present scenarios, provide a high-level view on the computational model and the system design but also specify their approach to activity detection and composition. The sections about the implementation and experiments provide sufficient information for other researchers to compare the proposed approach with their implementations. A weakness of the experiments is, however, the alternative, primitive activity detection method utilised for comparison. Since the authors re-implemented the method provided in [35], and did not provide any details on the implementation, a qualified comparison between both approaches is not possible. Furthermore, the experiments only detail the processing time but not the accuracy of context acquisition and proactive context deduction. Since contexts are classified by location and (very vague) duration only, I have some doubts on the context acquisition accuracy. It is also not detailed, how ambiguous context descriptions (e.g. same location and overlapping min-max-durations) are handled. I assume that the context acquisition accuracy can easily be increased when further context features - also from the same sensors, like time of day or number of people in the proximity, are considered. Minor remark: The third sentence in section 8 is started with an underscore '\_'. "
}

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J.","Kim, B.","Kong, H."],"year":2009,"bibtype":"inproceedings","biburl":"http://www.stephansigg.de/stephan/AmbientIntelligenceGroup/bibtex/LiteraturStephan","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Metroscope: Augmenting Urban Life Experience by Employing Collective City Intelligence","booktitle":"Mobisys 2009","author":[{"firstnames":["Youngki"],"propositions":[],"lastnames":["Lee"],"suffixes":[]},{"firstnames":["Seungwoo"],"propositions":[],"lastnames":["Kang"],"suffixes":[]},{"firstnames":["Sang","Jeong"],"propositions":[],"lastnames":["Lee"],"suffixes":[]},{"firstnames":["Byoungjip"],"propositions":[],"lastnames":["Kim"],"suffixes":[]},{"firstnames":["Hyunju"],"propositions":[],"lastnames":["Kong"],"suffixes":[]}],"year":"2009","abstract":"We envision a highly interactive future city for the quality lives of city residents. In this paper, we propose Metroscope, a novel computational framework to effectively support residents to design and attain rich urban experiences. Metroscope first develops a novel model to describe urban experiences in a large-scale city. Also, we design and implement the Metroscope system to facilitate collective urban experience sharing reflecting city dynamics. More important, we develop highly efficient real-time activity pattern processing techniques to deal with massive scales of a city. We have field-tested our prototype system in a subpart of Daejeon city in Korea. We perform the focus-group interview and performance study. They show the effectiveness and efficiency of the Metroscope system.","localfile":"/home/stephan/Daten/Arbeit/Paper_Tutorials/Paper/090101_5159_MetroscopeAugmentingUrbanLifeExperienceByEmployingCollectiveCityIntelligence.pdf","bemerkung":"Habe ich fuer die Mobisys 2009 gereviewed. The authors propose a framework (Server and mobile client) that acquires, processes and utilises context information to provide recommendations for possible activities and interesting locations in a city environment. Recommendations are based on context- and profile information of users. The paper is well written and details a complete system to support and utilise pervasive context information. The authors present scenarios, provide a high-level view on the computational model and the system design but also specify their approach to activity detection and composition. The sections about the implementation and experiments provide sufficient information for other researchers to compare the proposed approach with their implementations. A weakness of the experiments is, however, the alternative, primitive activity detection method utilised for comparison. Since the authors re-implemented the method provided in [35], and did not provide any details on the implementation, a qualified comparison between both approaches is not possible. Furthermore, the experiments only detail the processing time but not the accuracy of context acquisition and proactive context deduction. Since contexts are classified by location and (very vague) duration only, I have some doubts on the context acquisition accuracy. It is also not detailed, how ambiguous context descriptions (e.g. same location and overlapping min-max-durations) are handled. I assume that the context acquisition accuracy can easily be increased when further context features - also from the same sensors, like time of day or number of people in the proximity, are considered. Minor remark: The third sentence in section 8 is started with an underscore '_'. ","bibtex":"@InProceedings{ 5159,\n\ttitle = \"Metroscope: Augmenting Urban Life Experience by Employing Collective City Intelligence\",\n\tbooktitle = \"Mobisys 2009\",\n\tauthor = \"Youngki Lee and Seungwoo Kang and Sang Jeong Lee and Byoungjip Kim and Hyunju Kong\",\n\tyear = \"2009\",\n\tabstract = \"We envision a highly interactive future city for the quality lives of city residents. In this paper, we propose Metroscope, a novel computational framework to effectively support residents to design and attain rich urban experiences. Metroscope first develops a novel model to describe urban experiences in a large-scale city. Also, we design and implement the Metroscope system to facilitate collective urban experience sharing reflecting city dynamics. More important, we develop highly efficient real-time activity pattern processing techniques to deal with massive scales of a city. We have field-tested our prototype system in a subpart of Daejeon city in Korea. We perform the focus-group interview and performance study. They show the effectiveness and efficiency of the Metroscope system.\",\n\tlocalfile = \"/home/stephan/Daten/Arbeit/Paper\\_Tutorials/Paper/090101\\_5159\\_MetroscopeAugmentingUrbanLifeExperienceByEmployingCollectiveCityIntelligence.pdf\",\n\tBemerkung = \"Habe ich fuer die Mobisys 2009 gereviewed. The authors propose a framework (Server and mobile client) that acquires, processes and utilises context information to provide recommendations for possible activities and interesting locations in a city environment. Recommendations are based on context- and profile information of users. The paper is well written and details a complete system to support and utilise pervasive context information. The authors present scenarios, provide a high-level view on the computational model and the system design but also specify their approach to activity detection and composition. The sections about the implementation and experiments provide sufficient information for other researchers to compare the proposed approach with their implementations. A weakness of the experiments is, however, the alternative, primitive activity detection method utilised for comparison. Since the authors re-implemented the method provided in [35], and did not provide any details on the implementation, a qualified comparison between both approaches is not possible. Furthermore, the experiments only detail the processing time but not the accuracy of context acquisition and proactive context deduction. Since contexts are classified by location and (very vague) duration only, I have some doubts on the context acquisition accuracy. It is also not detailed, how ambiguous context descriptions (e.g. same location and overlapping min-max-durations) are handled. I assume that the context acquisition accuracy can easily be increased when further context features - also from the same sensors, like time of day or number of people in the proximity, are considered. Minor remark: The third sentence in section 8 is started with an underscore '\\_'. \"\n}\n\n","author_short":["Lee, Y.","Kang, S.","Lee, S. J.","Kim, B.","Kong, H."],"key":"5159","id":"5159","bibbaseid":"lee-kang-lee-kim-kong-metroscopeaugmentingurbanlifeexperiencebyemployingcollectivecityintelligence-2009","role":"author","urls":{},"downloads":0,"html":""},"search_terms":["metroscope","augmenting","urban","life","experience","employing","collective","city","intelligence","lee","kang","lee","kim","kong"],"keywords":[],"authorIDs":[],"dataSources":["LHPT6ZANm8yTecwp8"]}