2015 31st IEEE International Conference on Data Engineering Workshops最新文献_第5页

Monitoring the citizens' perception on urban security in Smart City environments 监测智慧城市环境下市民对城市安全的感知

2015 31st IEEE International Conference on Data Engineering Workshops Pub Date : 2015-04-13 DOI: 10.1109/ICDEW.2015.7129559

Luca Cagliero, T. Cerquitelli, S. Chiusano, P. Garino, Marco Nardone, B. Pralio, Luca Venturini

{"title":"Monitoring the citizens' perception on urban security in Smart City environments","authors":"Luca Cagliero, T. Cerquitelli, S. Chiusano, P. Garino, Marco Nardone, B. Pralio, Luca Venturini","doi":"10.1109/ICDEW.2015.7129559","DOIUrl":"https://doi.org/10.1109/ICDEW.2015.7129559","url":null,"abstract":"Sensing the perception of citizens on urban security is a key point in Smart City management. To address non-emergency issues municipalities commonly acquire citizens' reports and then analyze them offline to perform targeted actions. However, since non-emergency data potentially scale towards Big Data there is a need for open standards and technologies to enable data mining and Business Intelligence analyses. The paper presents an integrated data mining and Business Intelligence architecture, relying on open technologies, for the analysis of non-emergency open data acquired in a Smart City context. Non-emergency data are first enriched with additional information related to the context of the warning reports and then analyzed offline to generate (i) informative dashboards based on a selection of Key Performance Indicators (KPIs), and (iii) association rules representing implications between warning categories and contextual information (e.g., city areas, districts, time slots). KPIs and rules are exploited to selectively notify to municipality actors (assessors, area operators) potentially critical situations, according to their role and authority. The experiments demonstrate the effectiveness of the proposed approach in a real Smart City context.","PeriodicalId":333151,"journal":{"name":"2015 31st IEEE International Conference on Data Engineering Workshops","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131220715","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: 18

SemTree: An index for supporting semantic retrieval of documents SemTree:支持文档语义检索的索引

2015 31st IEEE International Conference on Data Engineering Workshops Pub Date : 2015-04-01 DOI: 10.1109/ICDEW.2015.7129546

F. Amato, Aniello De Santo, F. Gargiulo, V. Moscato, Fabio Persia, A. Picariello, S. Poccia

引用次数: 6

Challenges in Chinese knowledge graph construction 中文知识图谱构建面临的挑战

2015 31st IEEE International Conference on Data Engineering Workshops Pub Date : 2015-04-01 DOI: 10.1109/ICDEW.2015.7129545

Chengyu Wang, Ming Gao, Xiaofeng He, Rong Zhang

引用次数: 33

Multi-scale population and mobility estimation with geo-tagged Tweets 地理标记推文的多尺度人口和流动性估计

2015 31st IEEE International Conference on Data Engineering Workshops Pub Date : 2014-11-30 DOI: 10.1109/ICDEW.2015.7129551

Jiajun Liu, Kun Zhao, Saeed Khan, M. Cameron, R. Jurdak

{"title":"Multi-scale population and mobility estimation with geo-tagged Tweets","authors":"Jiajun Liu, Kun Zhao, Saeed Khan, M. Cameron, R. Jurdak","doi":"10.1109/ICDEW.2015.7129551","DOIUrl":"https://doi.org/10.1109/ICDEW.2015.7129551","url":null,"abstract":"Recent outbreaks of Ebola and Dengue viruses have again elevated the significance of the capability to quickly predict disease spread in an emergent situation. However, existing approaches usually rely heavily on the time-consuming census processes, or the privacy-sensitive call logs, leading to their unresponsive nature when facing the abruptly changing dynamics in the event of an outbreak. In this paper we study the feasibility of using large-scale Twitter data as a proxy of human mobility to model and predict disease spread. We report that for Australia, Twitter users' distribution correlates well the census-based population distribution, and that the Twitter users' travel patterns appear to loosely follow the gravity law at multiple scales of geographic distances, i.e. national level, state level and metropolitan level. The radiation model is also evaluated on this dataset though it has shown inferior fitness as a result of Australia's sparse population and large landmass. The outcomes of the study form the cornerstones for future work towards a model-based, responsive prediction method from Twitter data for disease spread.","PeriodicalId":333151,"journal":{"name":"2015 31st IEEE International Conference on Data Engineering Workshops","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132057718","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: 29