Proceedings of the 2015 Workshop on Mobile Big Data最新文献_第2页

An Optimal Dynamic Frame Slot-Segment Algorithm 一种最优动态帧槽段算法

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757395

Litian Duan, Z. Wang, Fu Duan

引用次数: 1

Crime Risk Evaluation in Individual's Local Community 个人所在社区的犯罪风险评估

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757387

Yunkai Liu, Anirudh Marthur, Christopher Magno

{"title":"Crime Risk Evaluation in Individual's Local Community","authors":"Yunkai Liu, Anirudh Marthur, Christopher Magno","doi":"10.1145/2757384.2757387","DOIUrl":"https://doi.org/10.1145/2757384.2757387","url":null,"abstract":"Our everyday lives involve navigating in the public space. If the public space is not safe it will reduce our freedom of movement and ability to participate in school, work and public life. Awareness on the level of security and danger in our public space can help us prepare and secure the area around us. But the lack of boundaries of public space makes it difficult to assess the level of safety and designing of security. The theory suggest (Creating Defensible Space by Oscar Newman, 1976) that the more personal and individualize the space we have, the more control and influence we have of defending it. Based on this theory, we are proposing a new sociological term called \"Individual's Local Community (ILC)\", which addresses on the unique environment of each individual navigating the public space. ILC is designed as the smallest measurable unit crossover local communities. ILC will personalize each individual's navigation to the public space. To measure the level of danger and safety in ILC we developed an android-based mobile application that estimate the risk of being victimize of crime in specified space and time-period. To extend the concept, one resident's risk to become victim or perpetrators of crime incidents is evaluated by estimated values based on \"stable\" ILC and historical crime records. The evaluation system is developed as a mobile app, named as \"Are You Safe\". The application contains local crime maps and a GPS tracking component. Mathematical formulas are developed to evaluate the potential risk. Furthermore, a re-routing function is provided for \"safer\" ILC. The prototype of \"Are You Safe\" app is implemented and tested in the city of northwestern Pennsylvania, United States. We believe the ILC concept will enrich the study of Personal Big Data and enable us to study social science in a different level. Also the app will help residents to have a better understanding on the safety and security of their environment.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123604588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data Preservation in Data-Intensive Sensor Networks With Spatial Correlation 具有空间相关性的数据密集型传感器网络中的数据保存

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757389

Nathaniel Crary, Bin Tang, Setu Taase

引用次数: 7

Mobile Data Collection Frameworks: A Survey 移动数据收集框架:调查

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757396

Paul Y. Cao, Gang Li, Guoxing Chen, Biao Chen

引用次数: 10

A Mobile Retail POS: Design and Implementation 移动零售POS:设计与实现

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757391

Wesley C. Davis, Z. Wang

引用次数: 1

Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing 分布式分析和边缘智能:雾计算时代的普遍健康监测

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757398

Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen

{"title":"Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing","authors":"Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen","doi":"10.1145/2757384.2757398","DOIUrl":"https://doi.org/10.1145/2757384.2757398","url":null,"abstract":"Biomedical research and clinical practice are entering a data-driven era. One of the major applications of biomedical big data research is to utilize inexpensive and unobtrusive mobile biomedical sensors and cloud computing for pervasive health monitoring. However, real-world user experiences with mobile cloud-based health monitoring were poor, due to the factors such as excessive networking latency and longer response time. On the other hand, fog computing, a newly proposed computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, storage, communication, and etc. This new computing paradigm, if successfully applied for pervasive health monitoring, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. In this paper, we employ a real-world pervasive health monitoring application (pervasive fall detection for stroke mitigation) to demonstrate the effectiveness and efficacy of fog computing paradigm in health monitoring. Fall is a major source of morbidity and mortality among stroke patients. Hence, detecting falls automatically and in a timely manner becomes crucial for stroke mitigation in daily life. In this paper, we set to (1) investigate and develop new fall detection algorithms and (2) design and employ a real-time fall detection system employing fog computing paradigm (e.g., distributed analytics and edge intelligence), which split the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud). Experimental results show that distributed analytics and edge intelligence, supported by fog computing paradigm, are very promising solutions for pervasive health monitoring.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 86

Session details: Mobile Computing and Data Collection 会议详情:移动计算和数据收集

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/3260493

Qun A. Li

引用次数: 0

Proceedings of the 2015 Workshop on Mobile Big Data 2015移动大数据研讨会论文集

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 1900-01-01 DOI: 10.1145/2757384

引用次数: 0