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

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Assembly Queries: Planning and Discovering Assemblies of Moving Objects Using Partial Information 装配查询:使用部分信息规划和发现移动对象的装配
R. Uddin, Michael N. Rice, C. Ravishankar, V. Tsotras
{"title":"Assembly Queries: Planning and Discovering Assemblies of Moving Objects Using Partial Information","authors":"R. Uddin, Michael N. Rice, C. Ravishankar, V. Tsotras","doi":"10.1145/3139958.3139993","DOIUrl":"https://doi.org/10.1145/3139958.3139993","url":null,"abstract":"Consider objects moving in a road network (e.g., groups of people or delivery vehicles), who may be free to choose routes, yet be required to arrive at certain locations at certain times. Such objects may need to assemble in groups within the network (friends meet while visiting a city, vehicles need to exchange items or information) without violating arrival constraints. Planning for such assemblies is hard when the network or the number of objects is large. Conversely, discovering actual or potential assemblies of such objects is important in many surveillance, security, and law-enforcement applications. This can be hard when object arrival observations are sparse due to inadequate sensor coverage or object countermeasures. We propose the novel class of assembly queries to model these scenarios, and present a unified scheme that addresses both of these complementary challenges. Given a set of objects and arrival constraints, we show how to first obtain the set of all possible locations visited by each moving object (the travel corridor), and then determine all possible assemblies, including the participants, locations, and durations. We present a formal model for various tracking strategies and several algorithms for using these strategies. We achieve excellent performance on these queries by preprocessing the network, using Contraction Hierarchies. Experimental results on real-world road networks show that we can efficiently and rapidly infer assembly information for very large networks and object groups.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"17 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":"124699884","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
SparkGIS: Resource Aware Efficient In-Memory Spatial Query Processing SparkGIS:资源感知的高效内存空间查询处理
Furqan Baig, Hoang Vo, T. Kurç, J. Saltz, Fusheng Wang
{"title":"SparkGIS: Resource Aware Efficient In-Memory Spatial Query Processing","authors":"Furqan Baig, Hoang Vo, T. Kurç, J. Saltz, Fusheng Wang","doi":"10.1145/3139958.3140019","DOIUrl":"https://doi.org/10.1145/3139958.3140019","url":null,"abstract":"Much effort has been devoted to support high performance spatial queries on large volumes of spatial data in distributed spatial computing systems, especially in the MapReduce paradigm. Recent works have focused on extending spatial MapReduce frameworks to leverage high performance in-memory distributed processing capabilities of systems such as Spark. However, the performance advantage comes with the requirement of having enough memory and comprehensive configuration. Failing to fulfill this falls back to disk IO, defeating the purpose of such systems or in worst case gets out of memory and fails the job. The problem is aggravated further for spatial processing since the underlying in-memory systems are oblivious of spatial data features and characteristics. In this paper we present SparkGIS - an in-memory oriented spatial data querying system for high throughput and low latency spatial query handling by adapting Apache Spark's distributed processing capabilities. It supports basic spatial queries including containment, spatial join and k-nearest neighbor and allows extending these to complex query pipelines. SparkGIS mitigates skew in distributed processing by supporting several dynamic partitioning algorithms suitable for a rich set of contemporary application scenarios. Multilevel global and local, pre-generated and on-demand in-memory indexes, allow SparkGIS to prune input data and apply compute intensive operations on a subset of relevant spatial objects only. Finally, SparkGIS employs dynamic query rewriting to gracefully manage large spatial query workflows that exceed available distributed resources. Our comparative evaluation has shown that the performance of SparkGIS is on par with contemporary Spark based platforms for relatively smaller queries and outperforms them for larger data and memory intensive workflows by dynamic query rewriting and efficient spatial data management.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"6 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":"134027522","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: 44
Leveraging Classification Models for River Forecasting 利用分类模型进行河流预报
Ruizhou Ding, Diana Marculescu
{"title":"Leveraging Classification Models for River Forecasting","authors":"Ruizhou Ding, Diana Marculescu","doi":"10.1145/3139958.3140048","DOIUrl":"https://doi.org/10.1145/3139958.3140048","url":null,"abstract":"Prior work in river forecasting has focused on applying regression models to gage and discharge prediction since these are naturally continuous dynamical functions. On the other hand, with discretized data, classifiers can be adopted to solve this problem by predicting a conditional probability distribution. Predicting this distribution is important in at least two ways: (1) the variance of the distribution can indicate the confidence of the predicted expected values, and (2) the distribution can be used for computing the probability that the gage or discharge exceeds or falls below some threshold. This paper presents a concrete river forecasting framework with classifiers including probabilistic graphical models (PGMs) and artificial neural network classifiers (ANNCs). The proposed framework is applied on real data for the Guadalupe river basin (Texas) thereby enabling a detailed comparison among various manners of forecasting studied, along with a set of guidelines for their best use.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"4 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":"133012150","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
A Uniform Representation for Trajectory Learning Tasks 轨迹学习任务的统一表示
Qingzhe Li, Jessica Lin, Liang Zhao, H. Rangwala
{"title":"A Uniform Representation for Trajectory Learning Tasks","authors":"Qingzhe Li, Jessica Lin, Liang Zhao, H. Rangwala","doi":"10.1145/3139958.3140017","DOIUrl":"https://doi.org/10.1145/3139958.3140017","url":null,"abstract":"Most trajectory data are collected with a constant sample rate (e.g. GPS data). However, the variance of velocities can be very large, which causes the non-uniformity of the sample points in trajectory dataset. That is, the trajectory dataset can be very sparse in some parts which cause most existing distance measures to get unexpected results. On the other hand, the dataset can be extremely dense in some other parts which results in unnecessarily high computational complexity. Due to the above phenomenon, choosing an appropriate sample rate becomes a difficult challenge. In order to address the dilemma, we propose a Step-Invariant Trajectory (SIT) representation that can provide a dynamic sample rate to represent any trajectories in a uniform way. The translation takes only linear time. We also propose an effective and scalable distance measure for SIT representation. We evaluate the effectiveness and efficiency of our representation along with its distance measure by performing multiple trajectory classification and clustering experiments. These results show that our distance measures on SIT representation is much more accurate and robust than other representations and distance measures on sparse trajectory datasets. Our approach can also achieve competitive accuracy compared with the state of the art model-based trajectory representations on dense datasets. However, the time required to translate the data to our representation is 2 orders of magnitude faster, on average, than translate to other model-based representations. Furthermore, our representation can also serve as a preprocessing step to provide high quality input to all trajectory learning methods.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"55 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":"125735801","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: 2
Inferring Traffic Cascading Patterns 推断流量级联模式
Yuxuan Liang, Zhongyuan Jiang, Yu Zheng
{"title":"Inferring Traffic Cascading Patterns","authors":"Yuxuan Liang, Zhongyuan Jiang, Yu Zheng","doi":"10.1145/3139958.3139960","DOIUrl":"https://doi.org/10.1145/3139958.3139960","url":null,"abstract":"There is an underlying cascading behavior over road networks. Traffic cascading patterns are of great importance to easing traffic and improving urban planning. However, what we can observe is individual traffic conditions on different road segments at discrete time intervals, rather than explicit interactions or propagation (e.g., A→B) between road segments. Additionally, the traffic from multiple sources and the geospatial correlations between road segments make it more challenging to infer the patterns. In this paper, we first model the three-fold influences existing in traffic propagation and then propose a data-driven approach, which finds the cascading patterns through maximizing the likelihood of observed traffic data. As this is equivalent to a submodular function maximization problem, we solve it by using an approximate algorithm with provable near-optimal performance guarantees based on its submodularity. Extensive experiments on real-world datasets demonstrate the advantages of our approach in both effectiveness and efficiency.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"25 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":"130139721","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: 37
The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones 两种定位技术的故事:为低端手机实现精确的低开销wifi定位
A. Shokry, Moustafa Elhamshary, M. Youssef
{"title":"The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones","authors":"A. Shokry, Moustafa Elhamshary, M. Youssef","doi":"10.1145/3139958.3139989","DOIUrl":"https://doi.org/10.1145/3139958.3139989","url":null,"abstract":"WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually by site surveyors. This process is labour intensive, tedious, and needs to be repeated with any change in the environment. While a number of recent systems have been introduced to reduce the calibration effort through RF propagation models and/or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones. In this paper, we present HybridLoc: an accurate low-overhead indoor localization system. The basic idea HybridLoc builds on is to leverage the sensors of high-end phones to enable localization of lower-end phones. Specifically, the WiFi fingerprint is crowdsourced by opportunistically collecting WiFi-scans labeled with location data obtained from BLE-enabled high-end smart phones. These scans are used to automatically construct the WiFi-fingerprint, that is used later to localize any lower-end cell phone with the ubiquitous WiFi technology. HybridLoc also has provisions for handling the inherent error in the estimated BLE locations used in constructing the fingerprint as well as to handle practical deployment issues including the noisy wireless environment, heterogeneous devices, among others. Evaluation of HybridLoc using Android phones shows that it can provide accurate localization in the same range as manual fingerprinting techniques under the same deployment conditions. Moreover, the localization accuracy on low-end phones supporting only WiFi is comparable to that achieved with high-end phones supporting BLE. This accuracy is achieved with no training overhead, is robust to the different user devices, and is consistent under environment changes.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"143 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":"130439220","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: 31
A PostGIS extension to support advanced spatial data types and integrity constraints PostGIS扩展,支持高级空间数据类型和完整性约束
Luís Eduardo Oliveira Lizardo, Clodoveu Augusto Davis Junior
{"title":"A PostGIS extension to support advanced spatial data types and integrity constraints","authors":"Luís Eduardo Oliveira Lizardo, Clodoveu Augusto Davis Junior","doi":"10.1145/3139958.3140020","DOIUrl":"https://doi.org/10.1145/3139958.3140020","url":null,"abstract":"Geometric primitives defined by OGC and ISO standards, implemented in most modern spatially-enabled database management systems (DBMS), are unable to capture the semantics of richer representation types, as found in current geographic data models. Moreover, relational DBMSs do not directly extend referential integrity mechanisms to cover spatial relationships and to support spatial integrity constraints. Rather, they usually assume that all spatial integrity checking will be carried out by the application, during the data entry process. This is not practical if the DBMS supports many applications, and can lead to redundant and inconsistent work. This paper presents AST-PostGIS, an extension for PostgreSQL/PostGIS that incorporates advanced spatial data types and implements spatial integrity constraints. The extension reduces the distance between the conceptual and the physical designs of spatial databases, by providing richer representations for geo-object and geo-field geometries. It also offers procedures to assert the consistency of spatial relationships during data updates. Such procedures can also be used before enforcing spatial integrity constraints for the first time. We illustrate the use of AST-PostGIS on an urban geographic database design problem, mapping its conceptual schema to the physical implementation in extended SQL.","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":"130475396","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
Using a Traffic Simulator for Navigation Service 使用交通模拟器进行导航服务
Abdullah AlDwyish, Hairuo Xie, E. Tanin, S. Karunasekera, K. Ramamohanarao
{"title":"Using a Traffic Simulator for Navigation Service","authors":"Abdullah AlDwyish, Hairuo Xie, E. Tanin, S. Karunasekera, K. Ramamohanarao","doi":"10.1145/3139958.3139998","DOIUrl":"https://doi.org/10.1145/3139958.3139998","url":null,"abstract":"Traffic congestion is a serious problem that is only expected to get worse in the future. Statistics shows that half of traffic congestion is caused by temporary disruptions like accidents. These events have dramatic impact on road network availability and cause huge delays for commuters. Also, they are usually unexpected and hard to manage by traffic authorities. State-of-the-art navigation systems started to provide real-time information about traffic conditions to help users make better routing decisions. However, traffic in the road network changes rapidly and the advice calculated now may not be valid after few minutes. This is especially critical in the presence of traffic incidents, where the impact of the incident could cause traffic to propagate to nearby roads. Thus, it is important for navigation systems to consider the evolution and future impact of traffic events. In this work, we present a navigation system that uses faster than realtime simulations to predict the evolution of traffic events and help drivers proactively avoid congestion caused by events. The system can subscribe to real-time traffic information and forecast the traffic conditions using fast simulations. We evaluate our approach through extensive experiments to test the performance and accuracy of the simulator with real data obtained from TomTom Traffic API. Also, we test the quality of navigation advice in realistic settings and show that our solution is able to help drivers avoid congested areas in cases where even real-time update methods lead drivers to congested routes.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"26 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":"129426971","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
Link Travel Time Prediction from Large Scale Endpoint Data 基于大规模端点数据的链路旅行时间预测
S. Mridha, Niloy Ganguly, Sourangshu Bhattacharya
{"title":"Link Travel Time Prediction from Large Scale Endpoint Data","authors":"S. Mridha, Niloy Ganguly, Sourangshu Bhattacharya","doi":"10.1145/3139958.3140006","DOIUrl":"https://doi.org/10.1145/3139958.3140006","url":null,"abstract":"Existing systems for travel time estimation either use data collected from loop detectors and probe vehicle locations, or from GPS traces from cellphones of \"online\" users. The former methods of data acquisition are expensive, while the latter turns out to be infeasible in connectivity-poor regions. However, many crowdsourced taxi trip datasets (from Boston, Beijing, Rome, etc.) are publicly available which, despite containing limited information, can be made useful for inferring meaningful insights by certain amount of data engineering. The datasets are both cheap to acquire (hence available in large volumes), and impose less heavy connectivity requirements on the end user. One such crowdsourced dataset is the NYC (New York City) Taxi dataset, which contains only the end-point information for each trip. In this paper, a link (road segment) travel time estimation algorithm named Least Square Estimation with Constraint (LSEC) has been developed from such end-point data, which estimates travel time 20% more accurately than existing algorithms. The key idea is to augment a subset of trips with unique paths using logged distance information, as opposed to fitting adhoc \"route-choice\" models.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"378 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":"122866515","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
Efficient Indexing and Querying of Geo-tagged Aerial Videos 地理标记航拍视频的高效索引和查询
Ying Lu, C. Shahabi
{"title":"Efficient Indexing and Querying of Geo-tagged Aerial Videos","authors":"Ying Lu, C. Shahabi","doi":"10.1145/3139958.3140046","DOIUrl":"https://doi.org/10.1145/3139958.3140046","url":null,"abstract":"Driven by the advances in control engineering, material science and sensor technologies, drones are becoming significantly prevalent in daily life (e.g., event coverage, tourism). Consequently, an unprecedented number of drone videos (or aerial videos) are recorded and consumed. In such a large repository, it is difficult to index and search aerial videos in an unstructured form. However, due to the rich sensor instrumentations of drones, aerial videos can be geotagged (e.g., GPS locations, drone rotation angles) at the acquisition time, providing an opportunity for efficient management of aerial videos by exploiting their corresponding spatial structures. Each aerial video frame can thus be represented as its spatial coverage, termed aerial Field-Of-View (aerial-FOV). This effectively converts a challenging aerial video management problem into a spatial database problem on aerial-FOVs. In this paper, we focus on efficient indexing and querying of aerial-FOVs. Unfortunately, aerial-FOVs are shaped in irregular quadrilaterals, and this renders existing spatial indexes inefficient to index aerial-FOVs. Therefore, we propose a new index structure called TetraR-tree that effectively captures the geometric property of aerial-FOVs. Based on the TetraR-tree, we develop two novel search strategies to efficiently process point and range queries on aerial-FOVs. Our experiments using both real-world and large synthetic video datasets (over 30 years' worth of videos) demonstrate the scalability and efficiency of our proposed indexing and querying algorithms.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"22 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132502741","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
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