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

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Geopriv4j
Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2020-06-14 DOI: 10.1145/3403896.3403968
Liyue Fan, S. Gunja
{"title":"Geopriv4j","authors":"Liyue Fan, S. Gunja","doi":"10.1145/3403896.3403968","DOIUrl":"https://doi.org/10.1145/3403896.3403968","url":null,"abstract":"The breach of users' location privacy can be catastrophic. To prevent privacy breaches, numerous location privacy methods have been developed in the last two decades. However, they have not been widely adopted in location-based applications. As a result, users' true location data is directly shared with untrusted service providers or researchers, raising concerns about location privacy. In this paper, we describe our effort to develop an open source repository, named Geopriv4j, in order to facilitate the adoption of location privacy methods in location-based services and research studies. Geopriv4j emphasizes on the practicality of location privacy, by identifying local, on-the-fly privacy methods under multiple categories. To facilitate adoption, Geopriv4j unifies the implementation of location privacy in Java, and provides usage examples as well as a sample Android app. To validate our implementation, we evaluate the location privacy methods in Geopriv4j with CPU, memory, and run time measures, using synthetically generated location traces.","PeriodicalId":433637,"journal":{"name":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122396202","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: 3
Similarity search over enriched geospatial data 丰富地理空间数据的相似度搜索
Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2020-06-14 DOI: 10.1145/3403896.3403967
Kostas Patroumpas, Dimitrios Skoutas
{"title":"Similarity search over enriched geospatial data","authors":"Kostas Patroumpas, Dimitrios Skoutas","doi":"10.1145/3403896.3403967","DOIUrl":"https://doi.org/10.1145/3403896.3403967","url":null,"abstract":"Enriched geospatial data refers to geospatial entities associated with additional information from various sources, such as textual, numerical or temporal. Exploring such data involves multi-criteria search and ranking across several heterogeneous attributes. In this paper, we model this task as a rank aggregation problem. Our method automatically scales similarity scores across diverse attributes without relying on user-specified parameters. It also allows to retrieve and combine information from multiple sources during query execution. We evaluate our approach using a large real-world dataset of enriched geospatial entities representing news articles.","PeriodicalId":433637,"journal":{"name":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132535806","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
Boosting toponym interlinking by paying attention to both machine and deep learning 通过关注机器学习和深度学习来促进地名的相互关联
Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2020-06-14 DOI: 10.1145/3403896.3403970
Konstantinos Alexis, Vassilis Kaffes, G. Giannopoulos
{"title":"Boosting toponym interlinking by paying attention to both machine and deep learning","authors":"Konstantinos Alexis, Vassilis Kaffes, G. Giannopoulos","doi":"10.1145/3403896.3403970","DOIUrl":"https://doi.org/10.1145/3403896.3403970","url":null,"abstract":"Toponym interlinking is the problem of identifying same spatio-textual entities within two or more different data sources, based exclusively on their names. It comprises a significant task in geospatial data management and integration with application in fields such as geomarketing, cadastration, navigation, etc. Previous works have assessed the effectiveness of unsupervised string similarity functions, while more recent ones have deployed similarity-based Machine Learning techniques and language model-based Deep Learning techniques, achieving significantly higher interlinking accuracy. In this paper, we demonstrate the suitability of Attention-based neural networks on the problem, as well as the fact that all different approaches provide merit to the problem, proposing a hybrid scheme that achieves the highest accuracy reported on toponym interlinking on the widely used Geonames dataset.","PeriodicalId":433637,"journal":{"name":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124942995","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
Evaluating computational geometry libraries for big spatial data exploration 评估大空间数据探索的计算几何库
Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2020-06-14 DOI: 10.1145/3403896.3403969
Yaming Zhang, A. Eldawy
{"title":"Evaluating computational geometry libraries for big spatial data exploration","authors":"Yaming Zhang, A. Eldawy","doi":"10.1145/3403896.3403969","DOIUrl":"https://doi.org/10.1145/3403896.3403969","url":null,"abstract":"With the rise of big spatial data, many systems were developed on Hadoop, Spark, Storm, Flink, and similar big data systems to handle big spatial data. At the core of all these systems, they use a computational geometry library to represent points, lines, and polygons, and to process them to evaluate spatial predicates and spatial analysis queries. This paper evaluates four computational geometry libraries to assess their suitability for various workloads in big spatial data exploration, namely, GEOS, JTS, Esri Geometry API, and GeoLite. The latter is a library that we built specifically for this paper to test some ideas that are not present in other libraries. For all the four libraries, we evaluate their computational efficiency and memory usage using a combination of micro- and macro-benchmarks on Spark. The paper gives recommendations on how to use these libraries for big spatial data exploration.","PeriodicalId":433637,"journal":{"name":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128461020","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
Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data 第六届国际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 Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data Pub Date : 2020-06-14 DOI: 10.1145/3403896
{"title":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","authors":"","doi":"10.1145/3403896","DOIUrl":"https://doi.org/10.1145/3403896","url":null,"abstract":"","PeriodicalId":433637,"journal":{"name":"Proceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759498","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
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