Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks最新文献

An efficient technique for event location identification using multiple sources of urban data 一种利用多源城市数据进行事件位置识别的有效技术

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148158

Dimitrios Tomaras, V. Kalogeraki, Nikolas Zygouras, D. Gunopulos

{"title":"An efficient technique for event location identification using multiple sources of urban data","authors":"Dimitrios Tomaras, V. Kalogeraki, Nikolas Zygouras, D. Gunopulos","doi":"10.1145/3148150.3148158","DOIUrl":"https://doi.org/10.1145/3148150.3148158","url":null,"abstract":"The proliferation of smart technologies has produced significant changes in the way people interact in a city. Smart traffic monitoring systems allow citizens and city operators to acquire a real-time view of the city traffic state. Furthermore, alternative means of transport, such as bike sharing systems, have enjoyed tremendous success in many major cities around the world today and provide real-time information regarding the mobility of the users. Such sources of urban data may act as human mobility sensors. Detecting the location and extent of large events in urban environments is a challenging problem. Previous work focuses mainly on identifying traffic flows and extract possible event sources. However, these solutions lack the ability to capture large areas of events, as they rely only on single-source data to identify user mobility or focus on identifying single locations rather than areas. In this paper we model the behavior of two different real-time data sources and we illustrate how they may be combined to acquire the area affected from a social event. We propose \"fEEL\" (Efficient Event Location identification), a novel algorithm to identify affected areas from social events using multiple heterogeneous sources of urban data. Our experimental evaluations show that fEEL is efficient and practical.","PeriodicalId":176579,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117271133","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: 4

Recommending OSM Tags To Improve Metadata Quality 推荐OSM标签以提高元数据质量

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148159

Doris Silbernagl, Nikolaus Krismer, Nikolaus Augsten, Günther Specht

{"title":"Recommending OSM Tags To Improve Metadata Quality","authors":"Doris Silbernagl, Nikolaus Krismer, Nikolaus Augsten, Günther Specht","doi":"10.1145/3148150.3148159","DOIUrl":"https://doi.org/10.1145/3148150.3148159","url":null,"abstract":"In this paper an application is developed that functions similar to a recommender system and allows to find appropriate OpenStreetMap (OSM) tags by querying co-occurring keys and tags, as well as similar sets of tags in the database. A user may enter key(s) or key-value pair(s), even using wildcard substitution for both, in order to find keys or key-value pairs that are used in combination with the entered ones. Moreover, the top-k matching tag sets are also presented. The results are then top-k ranked, based on the frequency of the occurrence of each distinct set in the database. This information may enable a user to find the most comprehensive and best fitting tag set for an OSM element. This assumption is examined in an evaluation where the precision and recall metrics for both approaches are computed and compared. Our approach helps discovering combinations of tags and their usage frequency in contrast to common recommender systems that focus on classifying or clustering elements and finding the most accurate (single) class or cluster rather than sets of tags.","PeriodicalId":176579,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650712","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: 3

Temporal Signature for Location Similarity 位置相似度的时间签名

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148155

Liyue Fan

引用次数: 0

Identifying Short-Names for Place Entities from Social Networks 从社交网络中识别地方实体的短名称

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148157

Faizan Wajid, Hong Wei, H. Samet

引用次数: 2

New monitoring scheme for persons with dementia through monitoring-area adaptation according to stage of disease 基于监测区域适应的痴呆症患者监测新方案

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148151

S. Kamada, Y. Matsuo, Sunao Hara, M. Abe

{"title":"New monitoring scheme for persons with dementia through monitoring-area adaptation according to stage of disease","authors":"S. Kamada, Y. Matsuo, Sunao Hara, M. Abe","doi":"10.1145/3148150.3148151","DOIUrl":"https://doi.org/10.1145/3148150.3148151","url":null,"abstract":"In this paper, we propose a new monitoring scheme for a person with dementia (PwD). The novel aspect of this monitoring scheme is that the size of the monitoring area changes for different stages of dementia, and the monitoring area is automatically generated using global positioning system (GPS) data collected by the PwD. The GPS data are quantized using the GeoHex code, which breaks down the map of the entire world into regular hexagons. The monitoring area is defined as a set of GeoHex codes, and the size of the monitoring area is controlled by the granularity of hexagons in the GeoHex code. The stages of dementia are estimated by analyzing the monitoring area to determine how frequently the PwD wanders. In this paper, we also examined two aspects of the implementation of the proposed scheme. First, we proposed an algorithm to estimate the monitoring area and evaluate its performance. The experimental results showed that the proposed algorithm can estimate the monitoring area with a precision of 0.82 and recall of 0.86 compared with the ground truth. Second, to investigate privacy considerations, we showed that different persons have different preferences for the granularity of the hexagons in the monitoring systems. 1The results indicate that the size of the monitoring area also should be changed for PwDs.","PeriodicalId":176579,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132443367","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: 4

Protecting User Privacy: Obfuscating Discriminative Spatio-Temporal Footprints 保护用户隐私:混淆区分时空足迹

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 2017-11-07 DOI: 10.1145/3148150.3148152

Jinhyung D. Park, E. Seglem, Eric Lin, Andreas Züfle

{"title":"Protecting User Privacy: Obfuscating Discriminative Spatio-Temporal Footprints","authors":"Jinhyung D. Park, E. Seglem, Eric Lin, Andreas Züfle","doi":"10.1145/3148150.3148152","DOIUrl":"https://doi.org/10.1145/3148150.3148152","url":null,"abstract":"In recent years, applications that collect and store location data have become ubiquitous, allowing users to engage in a variety of interactions with other users and services in their digital or physical vicinity. However, usage of these geolocation services put users at risk of serious privacy threats. For instance, state-of-the-art user-identification methods use geospatial trajectories derived from location based services to identify users at an alarmingly high accuracy. In this work, we address the problem of protecting user identities by presenting methods for obfuscating discriminative location data in users' profiles. We utilize data provided by the public Twitter API, collecting tweets with geolocation tags from a select group of prolific users in a 12-week time period. To minimize the amount of data obfuscated, we present two methods to identify the most discriminative tweets. The first solution is to use an Entropy-Maximizing Observation Function based on the number of tweets the user has posted and the number of people who have posted in that specific location. This ensures tweets by infrequent users in unique locations are changed first. The other solution is to use the identification algorithm to figure out what users can be identified and only change tweets from those users. For both methods, to perturb a tweet, we move it to a location with more tweets to mask the identity of the user. A thorough experimentation of other baseline approaches shows that our model exhibits a significant decrease in user identification accuracy while keeping the percentage of changed data at a minimum.","PeriodicalId":176579,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296773","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: 7

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks 第一届ACM SIGSPATIAL研讨会论文集，关于基于位置的服务和社交网络的建议

Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks Pub Date : 1900-01-01 DOI: 10.1145/3148150

引用次数: 0