Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data最新文献

An empirical study of workers' behavior in spatial crowdsourcing 空间众包中员工行为的实证研究

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948657

Hien To, Rúben Geraldes, C. Shahabi, S. H. Kim, H. Prendinger

{"title":"An empirical study of workers' behavior in spatial crowdsourcing","authors":"Hien To, Rúben Geraldes, C. Shahabi, S. H. Kim, H. Prendinger","doi":"10.1145/2948649.2948657","DOIUrl":"https://doi.org/10.1145/2948649.2948657","url":null,"abstract":"With the ubiquity of smartphones, spatial crowdsourcing (SC) has emerged as a new paradigm that engages mobile users to perform tasks in the physical world. Thus, various SC techniques have been studied for performance optimization. However, little research has been done to understand workers' behavior in the real world. In this study, we designed and performed two real world SC campaigns utilizing our mobile app, called Genkii, which is a GPS-enabled app for users to report their affective state (e.g., happy, sad). We used Yahoo! Japan Crowdsourcing as the payment platform to reward users for reporting their affective states at different locations and times. We studied the relationship between incentives and participation by analyzing the impact of offering a fixed reward versus an increasing reward scheme. We observed that users tend to stay in a campaign longer when the provided incentives gradually increase over time. We also found that the degree of mobility is correlated with the reported information. For example, users who travel more are observed to be happier than the ones who travel less. Furthermore, analyzing the spatiotemporal information of the reports reveals interesting mobility patterns that are unique to spatial crowdsourcing.","PeriodicalId":336205,"journal":{"name":"Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133534890","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

Taming twisted cubes 驯服扭曲的立方体

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948650

P. Baumann, E. Hirschorn, J. Masó, A. Dumitru, Vlad Merticariu

引用次数: 4

Prediction of user app usage behavior from geo-spatial data 根据地理空间数据预测用户应用程序的使用行为

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948656

Xiao-Xing Zhao, Yuanyuan Qiao, Zhongwei Si, Jie Yang, Anders Lindgren

{"title":"Prediction of user app usage behavior from geo-spatial data","authors":"Xiao-Xing Zhao, Yuanyuan Qiao, Zhongwei Si, Jie Yang, Anders Lindgren","doi":"10.1145/2948649.2948656","DOIUrl":"https://doi.org/10.1145/2948649.2948656","url":null,"abstract":"In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet.","PeriodicalId":336205,"journal":{"name":"Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121098539","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

Geodata supported classification of patent applications 地理数据支持专利申请的分类

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948653

J. Stutzki, Matthias Schubert

{"title":"Geodata supported classification of patent applications","authors":"J. Stutzki, Matthias Schubert","doi":"10.1145/2948649.2948653","DOIUrl":"https://doi.org/10.1145/2948649.2948653","url":null,"abstract":"The automatic classification of patent applications into a particular patent classification system remains a challenge with many practical applications. From a computer science point of view, the task is a multi-label hierarchical classification problem, i.e. each patent application might belong to multiple classes within the class hierarchy. The problem is still especially difficult for purely text-based classifiers because patents and patent applications are often formulated in a rather generic way. Thus, additional sources of information should be used to improve class prediction. In our approach, we propose the use of location information contained in the meta data of a patent application in combination with text-based patent classification. We argue that certain technological areas often cluster in geographic regions. For example, space travel technology is often collocated at Houston, Texas due to the NASA facilities in this area. In many cases, the addresses of the inventors are correlated to the technological area of a given patent. Thus, the addresses can be exploited to provide additional information about the technological area. We present a geo-enriched classifier joining established methods for text-based classification with location-based topic prediction. Since the location-based prediction is not applicable to all cases, we provide a method to regulate the impact of the spatial predictor for these cases. Our experiments indicate that spatial prediction is applicable to a considerable amount of patent applications and that the combination of spatial prediction and text-based classification significantly improves the prediction accuracy.","PeriodicalId":336205,"journal":{"name":"Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126641234","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: 8

Geo-fingerprinting social media content 地理指纹识别社交媒体内容

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948654

Hatim Gazaz, A. Croitoru, P. Delamater, D. Pfoser

引用次数: 1

GeoSocialBound: an efficient framework for estimating social POI boundaries using spatio--textual information GeoSocialBound:一个使用空间文本信息估计社会POI边界的有效框架

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948652

Dung D. Vu, Hien To, Won-Yong Shin, C. Shahabi

引用次数: 20

So far away and yet so close: augmenting toponym disambiguation and similarity with text-based networks 如此遥远而又如此接近:与基于文本的网络增强地名消歧和相似性

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948651

Andreas Spitz, Johanna Geiß, Michael Gertz

{"title":"So far away and yet so close: augmenting toponym disambiguation and similarity with text-based networks","authors":"Andreas Spitz, Johanna Geiß, Michael Gertz","doi":"10.1145/2948649.2948651","DOIUrl":"https://doi.org/10.1145/2948649.2948651","url":null,"abstract":"Place similarity has a central role in geographic information retrieval and geographic information systems, where spatial proximity is frequently just a poor substitute for semantic relatedness. For applications such as toponym disambiguation, alternative measures are thus required to answer the non-trivial question of place similarity in a given context. In this paper, we discuss a novel approach to the construction of a network of locations from unstructured text data. By deriving similarity scores based on the textual distance of toponyms, we obtain a kind of relatedness that encodes the importance of the co-occurrences of place mentions. Based on the text of the English Wikipedia, we construct and provide such a network of place similarities, including entity linking to Wikidata as an augmentation of the contained information. In an analysis of centrality, we explore the networks capability of capturing the similarity between places. An evaluation of the network for the task of toponym disambiguation on the AIDA CoNLL-YAGO dataset reveals a performance that is in line with state-of-the-art methods.","PeriodicalId":336205,"journal":{"name":"Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126411107","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: 21

Dynamically ranked top-k spatial keyword search 动态排名前k的空间关键字搜索

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2016-06-26 DOI: 10.1145/2948649.2948655

S. Ray, B. Nickerson

引用次数: 9

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data 第三届国际ACM SIGMOD管理和挖掘丰富的地理空间数据研讨会论文集

Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 1900-01-01 DOI: 10.1145/2948649

引用次数: 0