Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第9页

Efficient itinerary planning with category constraints 有效的行程规划与类别约束

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666411

P. Bolzoni, S. Helmer, Kevin Wellenzohn, J. Gamper, Periklis Andritsos

引用次数: 37

Frequency-based search for public transit 基于频率的公共交通搜索

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666405

H. Bast, Sabine Storandt

{"title":"Frequency-based search for public transit","authors":"H. Bast, Sabine Storandt","doi":"10.1145/2666310.2666405","DOIUrl":"https://doi.org/10.1145/2666310.2666405","url":null,"abstract":"We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstra's algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117091253","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

Persistence based online signal and trajectory simplification for mobile devices 基于持久性的在线信号和移动设备轨迹简化

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666388

P. Katsikouli, Rik Sarkar, Jie Gao

{"title":"Persistence based online signal and trajectory simplification for mobile devices","authors":"P. Katsikouli, Rik Sarkar, Jie Gao","doi":"10.1145/2666310.2666388","DOIUrl":"https://doi.org/10.1145/2666310.2666388","url":null,"abstract":"We describe an online algorithm to simplify large volumes of location and sensor data on the source mobile device, by eliminating redundant data points and saving important ones. Our approach is to use topological persistence to identify large scale sharp features of a data stream. We show that for one-dimensional data streams such as trajectories, simplification based on topologically persistent features can be maintained online, such that each new data-point is processed in O(1) time. Our method extends to multi-resolution simplifications, where it identifies larger scale features that represent more important elements of data, and naturally eliminates noise and small deviations. The multi-resolution simplification is also maintained online in real time, at cost of O(1) per input point. Therefore it is lightweight and suitable for use in embedded sensors and mobile phones. The method can be applied to more general data streams such as sensor data to produce similar simplifications. Our experiments on real data show that this approach when applied to the curvature function of trajectory or sensor data produces compact simplifications with low approximation errors comparable to existing offline methods.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875757","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: 30

TREADS: a safe route recommender using social media mining and text summarization TREADS:使用社交媒体挖掘和文本摘要的安全路线推荐

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666368

Kaiqun Fu, Yen-Cheng Lu, Chang-Tien Lu

引用次数: 33

Secure mutual proximity zone enclosure evaluation 安全相互接近区域外壳评估

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666384

Sunoh Choi, Gabriel Ghinita, E. Bertino

{"title":"Secure mutual proximity zone enclosure evaluation","authors":"Sunoh Choi, Gabriel Ghinita, E. Bertino","doi":"10.1145/2666310.2666384","DOIUrl":"https://doi.org/10.1145/2666310.2666384","url":null,"abstract":"Mobile users engage in novel and exciting location-based social media applications (e.g., geosocial networks, spatial crowdsourcing) in which they interact with other users situated in their proximity. In several application scenarios, users define their own proximity zones of interest (typically in the form of polygonal regions, such as a collection of city blocks), and want to find other users with whom they are in a mutual enclosure relationship with respect to their respective proximity zones. This boils down to evaluating two point-in-polygon enclosure conditions, which is easy to achieve for revealed user locations and proximity zones. However, users may be reluctant to share their whereabouts with their friends and with social media service providers, as location data can help one infer sensitive details such as an individual's health status, financial situation or lifestyle choices. In this paper, we propose a mechanism that allows users to securely evaluate mutual proximity zone enclosure on encrypted location data. Our solution uses homomorphic encryption, and supports convex polygonal proximity zones. We provide a security analysis of the proposed solution, we investigate performance optimizations, and we show experimentally that our approach scales well for datasets of millions of users.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116021235","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: 9

Ambiguity and plausibility: managing classification quality in volunteered geographic information 模糊与似是而非:地理信息分类质量管理

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666392

Ahmed Loai Ali, Falko Schmid, R. Al-Salman, Tomi Kauppinen

{"title":"Ambiguity and plausibility: managing classification quality in volunteered geographic information","authors":"Ahmed Loai Ali, Falko Schmid, R. Al-Salman, Tomi Kauppinen","doi":"10.1145/2666310.2666392","DOIUrl":"https://doi.org/10.1145/2666310.2666392","url":null,"abstract":"With the ubiquity of technology and tools, current Volunteered Geographic Information (VGI) projects allow the public to contribute, maintain, and use geo-spatial data. One of the most prominent and successful VGI project is OpenStreetMap (OSM), where more than one million volunteers collected and contributed data that is obtainable for everybody. However, this kind of contribution mechanism is usually associated with data quality issues, e.g., geographic entities such as gardens or parks can be assigned with inappropriate classification by volunteers. Based on the observation that geographic features usually inherit certain properties and characteristics, we propose a novel classification-based approach allowing the identification of entities with inappropriate classification. We use the rich data set of OSM to analyze the properties of geographic entities with respect to their implicit characteristics in order to develop classifiers based on them. Our developed classifiers show high detection accuracies. However, due to the absence of proper training data we additionally performed a user study to verify our findings by means of intra-user-agreement. The results of our study support the detections of our classifiers and show that our classification-based approaches can be a valuable tool for managing and improving VGI data.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129607042","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: 52

Index-supported pattern matching on symbolic trajectories 符号轨迹上索引支持的模式匹配

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666402

Fabio Valdés, R. H. Güting

{"title":"Index-supported pattern matching on symbolic trajectories","authors":"Fabio Valdés, R. H. Güting","doi":"10.1145/2666310.2666402","DOIUrl":"https://doi.org/10.1145/2666310.2666402","url":null,"abstract":"Recording mobility data with GPS-enabled devices, e.g., smart phones or vehicles, has become a common issue for private persons, companies, and institutions. Consequently, the requirements for managing these enormous datasets have increased drastically, so trajectory management has become an active research field. In order to avoid querying raw trajectories, which is neither convenient nor efficient, a symbolic representation of the geometric data has been introduced. A comprehensive framework for describing and querying symbolic trajectories including an expressive pattern language as well as an efficient matching algorithm was presented lately. A symbolic trajectory, basically being a time-dependent symbolic value (e.g., a label), can contain names of traversed roads, a speed profile, transportation modes, behaviors of animals, or cells inside a cellular network. The quality and efficiency of transportation systems, targeted advertising, animal research, crime investigation, etc. may be improved by analyzing such data. The main contribution of this paper is an improvement of our previous approach, featuring algorithms and data structures optimizing the matching of symbolic trajectories for any kind of pattern with the help of two indexes. More specifically, a trie is applied for the symbolic values (i.e., labels or places), while the time intervals are stored in a one-dimensional R-tree. Hence, we avoid the linear scan of every trajectory, being necessary without index support. As a result, the computation cost for the pattern matching is nearly independent from the trajectory size. Our work details the concept and the implementation of the new approach, followed by an experimental evaluation.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127588981","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

BioenergyKDF: enabling spatiotemporal data synthesis and research collaboration 生物能源kdf:实现时空数据综合和研究合作

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666488

Aaron T. Myers, S. Movva, R. Karthik, B. Bhaduri, D. White, N. Thomas, Adrian S Z Chase

{"title":"BioenergyKDF: enabling spatiotemporal data synthesis and research collaboration","authors":"Aaron T. Myers, S. Movva, R. Karthik, B. Bhaduri, D. White, N. Thomas, Adrian S Z Chase","doi":"10.1145/2666310.2666488","DOIUrl":"https://doi.org/10.1145/2666310.2666488","url":null,"abstract":"The Bioenergy Knowledge Discovery Framework (BioenergyKDF) is a scalable, web-based collaborative environment for scientists working on bioenergy related research in which the connections between data, literature, and models can be explored and more clearly understood. The fully-operational and deployed system, built on multiple open source libraries and architectures, stores contributions from the community of practice and makes them easy to find, but that is just its base functionality. The BioenergyKDF provides a national spatiotemporal decision support capability that enables data sharing, analysis, modeling, and visualization as well as fosters the development and management of the U.S. bioenergy infrastructure, which is an essential component of the national energy infrastructure. The BioenergyKDF is built on a flexible, customizable platform that can be extended to support the requirements of any user community---especially those that work with spatiotemporal data. While there are several community data-sharing software platforms available, some developed and distributed by national governments, none of them have the full suite of capabilities available in BioenergyKDF. For example, this component-based platform and database independent architecture allows it to be quickly deployed to existing infrastructure and to connect to existing data repositories (spatial or otherwise). As new data, analysis, and features are added; the BioenergyKDF will help lead research and support decisions concerning bioenergy into the future, but will also enable the development and growth of additional communities of practice both inside and outside of the Department of Energy. These communities will be able to leverage the substantial investment the agency has made in the KDF platform to quickly stand up systems that are customized to their data and research needs.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635152","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: 1

Community detection from location-tagged networks 社区检测从位置标记的网络

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666496

Zhi Liu, Y. Huang

引用次数: 5

SATO: a spatial data partitioning framework for scalable query processing SATO:用于可扩展查询处理的空间数据分区框架

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666365

Hoang Vo, Ablimit Aji, Fusheng Wang

{"title":"SATO: a spatial data partitioning framework for scalable query processing","authors":"Hoang Vo, Ablimit Aji, Fusheng Wang","doi":"10.1145/2666310.2666365","DOIUrl":"https://doi.org/10.1145/2666310.2666365","url":null,"abstract":"Scalable spatial query processing relies on effective spatial data partitioning for query parallelization, data pruning, and load balancing. These are often challenged by the intrinsic characteristics of spatial data, such as high skew in data distribution and high complexity of irregular multi-dimensional objects. In this demo, we present SATO, a spatial data partitioning framework that can quickly analyze and partition spatial data with an optimal spatial partitioning strategy for scalable query processing. SATO works in following steps: 1) Sample, which samples a small fraction of input data for analysis, 2) Analyze, which quickly analyzes sampled data to find an optimal partition strategy, 3) Tear, which provides data skew aware partitioning and supports MapReduce based scalable partitioning, and 4) Optimize, which collects succinct partition statistics for potential query optimization. SATO also provides multiple level partitioning, which can be used to significantly improve window based queries in cloud based spatial query processing systems. SATO comes with a visualization component that provides heat maps and histograms for qualitative evaluation. SATO has been implemented within the Hadoop-GIS, a high performance spatial data warehousing system over MapReduce. SATO is also released as an independent software package to support various scalable spatial query processing systems. Our experiments have demonstrated that SATO can generate much balanced partitioning that can significantly improve spatial query performance with MapReduce comparing to traditional spatial partitioning approaches.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471351","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: 69