Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming最新文献

Mining frequent trajectory patterns from online footprints 从在线足迹中挖掘频繁的轨迹模式

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003431

Qunying Huang, Zhenglong Li, Jing Li, Charles Chang

引用次数: 12

Categorizing spatiotemporal aggregates for moving regions 移动区域时空聚集体的分类

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003430

Mark McKenney, Khalil Khobrani, Pr Rangaraju

引用次数: 0

A survey of techniques and open-source tools for processing streams of spatio-temporal events 时空事件流处理技术和开源工具综述

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003432

James N. Hughes, Matthew D. Zimmerman, Christopher N. Eichelberger, Anthony D. Fox

引用次数: 13

ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams ST-DCONTOUR:一种基于序列密度轮廓的时空聚类方法，用于聚类位置流

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003429

Yongli Zhang, C. Eick

引用次数: 4

Mining frequent episodes from multivariate spatiotemporal event sequences 从多变量时空事件序列中挖掘频繁事件

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003428

Shahab Helmi, F. Kashani

{"title":"Mining frequent episodes from multivariate spatiotemporal event sequences","authors":"Shahab Helmi, F. Kashani","doi":"10.1145/3003421.3003428","DOIUrl":"https://doi.org/10.1145/3003421.3003428","url":null,"abstract":"Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals and vehicles. In particular, mining patterns from interdependent co-movements of objects in a group/team (such as players of a sports team, ants of a colony in search of food, and cars in a congested downtown district) can lead to the discovery of interesting patterns (e.g., offense tactics and strategies of a sports team). Various trajectory mining, and in particular frequent episode mining (FEM), approaches have been proposed to discover such patterns from trajectory datasets. However, the existing FEM approaches neither are applicable to multivariate spatial (MVS) event sequences nor consider and leverage all spatial features of the input data. In this paper, we first introduce a Spatial Apriori property which extends the well-known Apriori property to consider the spatial properties of the input data. We present a data preprocessing technique that leverages the aforementioned Spatial Apriori to reduce the search space of our problem by filtering out irrelevant events from a given MVS event sequence. Second, we present the MVS-FEM framework which efficiently discovers co-movements patterns from MVS datasets. The efficiency of our proposed solutions is evaluated using a real dataset.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127037704","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: 5

A general feature-based map matching framework with trajectory simplification 一种通用的基于特征的轨迹简化地图匹配框架

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003426

Yifang Yin, R. Shah, Roger Zimmermann

{"title":"A general feature-based map matching framework with trajectory simplification","authors":"Yifang Yin, R. Shah, Roger Zimmermann","doi":"10.1145/3003421.3003426","DOIUrl":"https://doi.org/10.1145/3003421.3003426","url":null,"abstract":"Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s ∼ 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121773759","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

Formalization of network-constrained moving object queries with application to benchmarking 基于基准测试的网络约束移动对象查询的形式化

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003427

M. Fouladgar, R. Elmasri

{"title":"Formalization of network-constrained moving object queries with application to benchmarking","authors":"M. Fouladgar, R. Elmasri","doi":"10.1145/3003421.3003427","DOIUrl":"https://doi.org/10.1145/3003421.3003427","url":null,"abstract":"In this paper, we first categorize the various types of Network-constrained moving object queries. We then propose benchmarks that can be used to compare the performance of systems and indexing schemes that are proposed for handling these types of queries. Network-constrained moving objects are objects that move in a specific network, such as vehicles that are constrained to move in a road (traffic) network. Our query categories are based on the Network-constrained moving object model presented by [4, 6, 14]. We formally define comprehensive categories of typical queries, based on whether the conditions involve space (point versus region), time (point versus interval), and object id. The categories are based on the various combinations of these features. We describe the types of queries as Relational Calculus expressions, based on the query constraints. We focus on three main constraints: Spatial constraints, Temporal constraints, or/and moving object ID constraints. For each types of query, we identify the types of results, and give examples to clarify the query types. This work can define a benchmark for the performance of different types of systems and indexes that are designed to answer queries on Network-constrained moving objects data. Certain indexes/systems may work well for some query categories but perform poorly for other types of queries.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324124","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: 0

Continuous detection of black holes for moving objects at sea 连续探测海上移动物体的黑洞

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003423

Loïc Salmon, C. Ray, Christophe Claramunt

引用次数: 8

On computing temporal functions for time-dependent networks using trajectory data streams 利用轨迹数据流计算时变网络的时间函数

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003425

Samara Martins do Nascimento, J. Macêdo, Mirla Rafaela Rafael Braga Chucre, M. Casanova, Javam C. Machado

引用次数: 1

MobiDict: a mobility prediction system leveraging realtime location data streams MobiDict:利用实时位置数据流的移动预测系统

Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2016-10-31 DOI: 10.1145/3003421.3003424

Vaibhav Kulkarni, A. Moro, B. Garbinato

{"title":"MobiDict: a mobility prediction system leveraging realtime location data streams","authors":"Vaibhav Kulkarni, A. Moro, B. Garbinato","doi":"10.1145/3003421.3003424","DOIUrl":"https://doi.org/10.1145/3003421.3003424","url":null,"abstract":"Mobility prediction is becoming one of the key elements of location-based services. In the near future, it will also facilitate tasks such as resource management, logistics administration and urban planning. To predict human mobility, many techniques have been proposed. However, existing techniques are usually driven by large volumes of data to train user mobility models computed over a long duration and stored in a centralized server. This results in inherently long waiting times before the prediction model kicks in. Over this large training data, small time bounded user movements are shadowed, due to their marginality, thus impacting the granularity of predictions. Transferring highly sensitive location data to third party entities also exposes the user to several privacy risks. To address these issues, we propose MobiDict, a realtime mobility prediction system that is constantly adapting to the user mobility behaviour, by taking into account the movement periodicity and the evolution of frequently visited places. Compared to the existing training approaches, our system utilises less data to generate the evolving mobility models, which in turn lowers the computational complexity and enables implementation on handheld devices, thus preserving privacy. We test our system using mobility traces collected around Lake Geneva region from 168 users and demonstrate the performance of our approach by evaluating MobiDict with six different prediction techniques. We find a satisfactory prediction accuracy as compared to the baseline results obtained with 70% of the user dataset for majority of the users.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668738","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: 17