Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第2页

A Dynamic Data Structure for Approximate Proximity Queries in Trajectory Data 弹道数据中近似接近查询的动态数据结构

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140023

M. D. Berg, Joachim Gudmundsson, Ali D. Mehrabi

{"title":"A Dynamic Data Structure for Approximate Proximity Queries in Trajectory Data","authors":"M. D. Berg, Joachim Gudmundsson, Ali D. Mehrabi","doi":"10.1145/3139958.3140023","DOIUrl":"https://doi.org/10.1145/3139958.3140023","url":null,"abstract":"Let S be a set of n polygonal trajectories in the plane and k be a fixed constant. We present a data structure to store S so that, given a k-vertex query trajectory Q, we can answer the following queries approximately: • Nearest neighbor query: given a query trajectory Q, report the trajectory in S with minimum Fréchet distance to Q. • Top-j query: given a query trajectory Q and a positive integer j, report the j trajectories in S with minimum Fréchet distance to Q. • Range reporting query: given a query trajectory Q and a number δmax, report all trajectories in S with Fréchet distance at most δmax to Q. • Similarity query: given a query trajectory Q and a trajectory τ ∈ S, report the Fréchet distance between Q and τ. Our data structure answers these queries approximately with an additive error that is at most ϵ · reach(Q) for a given fixed constant ϵ > 0, where reach(Q) is the maximum distance between the start vertex of Q and any other vertex of Q. Furthermore, our query procedures ignore trajectories whose Fréchet distance to the query Q is very large. That is, if no trajectory is close to the query trajectory then no answer might be returned. The data structure uses O(n/ϵ2k) space and answers each of the queries above in time O(1 + #answers). Our data structure is the first one that can answer all these queries with provable error guarantees. Moreover, it is fully dynamic: inserting and deleting a trajectory with m vertices takes O(1/ϵ2k (m + log(1/ϵ))) and O(1/ϵ2k) amortized time, respectively. Finally, we empirically evaluate our data structure.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"13 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":"115371899","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: 13

If I build it, will they come?: Predicting new venue visitation patterns through mobility data 如果我建好了，他们会来吗?:通过移动数据预测新的场馆访问模式

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140035

Krittika D’Silva, A. Noulas, Mirco Musolesi, C. Mascolo, Max Sklar

{"title":"If I build it, will they come?: Predicting new venue visitation patterns through mobility data","authors":"Krittika D’Silva, A. Noulas, Mirco Musolesi, C. Mascolo, Max Sklar","doi":"10.1145/3139958.3140035","DOIUrl":"https://doi.org/10.1145/3139958.3140035","url":null,"abstract":"Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse measures such as observing numbers in local venues. The advent of crowdsourced data from devices and services has opened the door to better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from the location-based service Foursquare, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions to forecast weekly popularity dynamics of a new venue establishment. Our evaluation shows that temporally similar areas of a city can be valuable predictors, decreasing error by 41%. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 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":"129707762","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: 16

Ride-sharing is About Agreeing on a Destination 拼车就是在目的地上达成一致

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139972

A. K. M. M. R. Khan, Oscar Correa, E. Tanin, L. Kulik, K. Ramamohanarao

{"title":"Ride-sharing is About Agreeing on a Destination","authors":"A. K. M. M. R. Khan, Oscar Correa, E. Tanin, L. Kulik, K. Ramamohanarao","doi":"10.1145/3139958.3139972","DOIUrl":"https://doi.org/10.1145/3139958.3139972","url":null,"abstract":"Ride-sharing is rapidly becoming an alternative form of transportation mainly due to its economic benefits. Existing research on ridesharing aims to optimally match trajectories between people with pre-selected destinations. In this paper, we show better ride-sharing arrangements are possible when users are presented with more destinations and agree on a common destination. Given a set of points of interest (POIs) and a set of users, our approach presents destination POIs and computes ride-sharing plans. Each arrangement for a subset of users that fit in a car can be presented as a minimum Steiner tree (MST) problem. An optimal solution of the overall problem minimizes the total length of all the MSTs. The problem is a version of the set cover problem and is NP-hard. We first develop a series of baseline methods which use a popular MST algorithm. Then, we propose our method which uses constraints on intermediary points where users can meet to share rides. These constraints reduce the time complexity significantly and our method is up to two orders of magnitude faster than the best baseline method. Since our algorithm finds the subsets of users and POIs for each arrangement, we define and solve a new type of MST problem as a first step. Our experiments show that our method can provide a fast and readily deployable solution for real world large city scenarios.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"63 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":"126819877","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: 12

A Network Model for the Utility Domain 实用领域的网络模型

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139980

P. Bakalov, E. Hoel, Sangho Kim

{"title":"A Network Model for the Utility Domain","authors":"P. Bakalov, E. Hoel, Sangho Kim","doi":"10.1145/3139958.3139980","DOIUrl":"https://doi.org/10.1145/3139958.3139980","url":null,"abstract":"The existing network models in geographic information systems that are used to support the utility domain (e.g., water, wastewater, sewer, gas, electric, and telecommunications) have limitations and constraints that restrict the ability of these utility companies to effectively and accurately model the rapidly increasing complexity and sophistication of their networks. This is caused by the fact that utility domain places a very different set of requirements on a network model and the associated analytic operations compared to those, commonly found in transportation and social networks. Although many utilities have succeeded in implementing production systems on top of simple graph models, the solutions have often involved either having to author considerable amounts of custom application code to go with the model (an expensive and cumbersome proposition), or modifying their workflows in order to compensate for the limitations of the underlying graph model. This paper introduces a new utility-centric graph information model that is designed to directly support the complex modeling of utility infrastructures. The model is focused on supporting additional requirements for improved performance and scalability (by optimized data storage layouts), efficiency and productivity (by modeling of real-world concepts like devices with multiple terminals, inside-plant, etc.), data quality (by enforcing a business rule-based framework which prevents bad data from entering the system), real-time data acquisition (by supporting for field-based telemetry such as Advanced Meter Infrastructure -- AMI, and Supervisory Control","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"77 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":"132357856","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: 10

Best-Compromise In-Route Nearest Neighbor Queries 路由内最近邻查询

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140007

E. Ahmadi, Camila F. Costa, M. Nascimento

{"title":"Best-Compromise In-Route Nearest Neighbor Queries","authors":"E. Ahmadi, Camila F. Costa, M. Nascimento","doi":"10.1145/3139958.3140007","DOIUrl":"https://doi.org/10.1145/3139958.3140007","url":null,"abstract":"Humans are animals of habit, e.g., people follow typical and/or familiar paths in their daily routines. With that in mind we investigate the problem where a user, traveling on his/her preferred path, needs to visit one of many available points-of-interest while (1) minimizing his/her total travel distance and also (2) minimizing the detour distance incurred to reach the chosen point-of-interest. We call this new problem the \"Best-Compromise In-Route Nearest Neighbor\" query in order to emphasize that a route cannot typically optimize both criteria at the same time, but rather find a compromise between them. In fact, the competing nature of these two criteria resembles the notion of skyline queries. In that context, we propose a solution based on using suitable upper-bounds to both cost criteria to prune uninteresting paths. It returns all linearly non-dominated paths that are optimal under any given linear combination of the two competing criteria. Our experiments using real data sets of different sizes show that our proposal can be orders of magnitude faster than a straightforward alternative.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"102 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":"134497747","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: 6

Flying Object Detection and Classification by Monitoring Using Video Images 基于视频图像监控的飞行物检测与分类

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140026

H. Sobue, Yukihiro Fukushima, T. Kashiyama, Y. Sekimoto

引用次数: 2

Inferring the Parametric Weight of a Bicriteria Routing Model from Trajectories 从轨迹推断双准则路由模型的参数权值

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140033

Johannes Oehrlein, Benjamin Niedermann, J. Haunert

引用次数: 3

Deriving Double-Digitized Road Network Geometry from Probe Data 利用探测器数据推导双数字化路网几何图形

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139966

O. H. Dørum

引用次数: 5

CP-ORTHO: An Orthogonal Tensor Factorization Framework for Spatio-Temporal Data CP-ORTHO:一个时空数据的正交张量分解框架

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140047

Ardavan Afshar, Joyce Ho, B. Dilkina, Ioakeim Perros, Elias Boutros Khalil, Li Xiong, V. Sunderam

{"title":"CP-ORTHO: An Orthogonal Tensor Factorization Framework for Spatio-Temporal Data","authors":"Ardavan Afshar, Joyce Ho, B. Dilkina, Ioakeim Perros, Elias Boutros Khalil, Li Xiong, V. Sunderam","doi":"10.1145/3139958.3140047","DOIUrl":"https://doi.org/10.1145/3139958.3140047","url":null,"abstract":"Extracting patterns and deriving insights from spatio-temporal data finds many target applications in various domains, such as in urban planning and computational sustainability. Due to their inherent capability of simultaneously modeling the spatial and temporal aspects of multiple instances, tensors have been successfully used to analyze such spatio-temporal data. However, standard tensor factorization approaches often result in components that are highly overlapping, which hinders the practitioner's ability to interpret them without advanced domain knowledge. In this work, we tackle this challenge by proposing a tensor factorization framework, called CP-ORTHO, to discover distinct and easily-interpretable patterns from multi-modal, spatio-temporal data. We evaluate our approach on real data reflecting taxi drop-off activity. CP-ORTHO provides more distinct and interpretable patterns than prior art, as measured via relevant quantitative metrics, without compromising the solution's accuracy. We observe that CP-ORTHO is fast, in that it achieves this result in 5x less time than the most accurate competing approach.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"13 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":"121806608","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: 25

Next place prediction in unfamiliar places considering contextual factors 考虑到环境因素，在不熟悉的地方预测下一个地点

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139970

Takashi Nicholas Maeda, K. Tsubouchi, F. Toriumi

引用次数: 11