Proceedings of the 25th International Conference on Scientific and Statistical Database Management最新文献_第2页

GIPSY: joining spatial datasets with contrasting density GIPSY:连接具有对比密度的空间数据集

Proceedings of the 25th International Conference on Scientific and Statistical Database Management Pub Date : 2013-07-29 DOI: 10.1145/2484838.2484855

Mirjana Pavlovic, F. Tauheed, T. Heinis, A. Ailamaki

{"title":"GIPSY: joining spatial datasets with contrasting density","authors":"Mirjana Pavlovic, F. Tauheed, T. Heinis, A. Ailamaki","doi":"10.1145/2484838.2484855","DOIUrl":"https://doi.org/10.1145/2484838.2484855","url":null,"abstract":"Many scientific and geographical applications rely on the efficient execution of spatial joins. Past research has produced several efficient spatial join approaches and while each of them can join two datasets, the problem of efficiently joining two datasets with contrasting density, i.e., with the same spatial extent but with a wildly different number of spatial elements, has so far been overlooked. State-of-the-art data-oriented spatial join approaches (e.g., based on the R-Tree) suffer from degraded performance due to overlap, whereas space-oriented approaches excessively read data from disk. In this paper we develop GIPSY, a novel approach for the spatial join of two datasets with contrasting density. GIPSY uses fine-grained data-oriented partitioning and thus only retrieves the data needed for the join. At the same time it avoids the overlap related problems associated with data-oriented partitioning by using a crawling approach, i.e., without using a hierarchical tree. Our experiments show that GIPSY outperforms state-of-the-art disk-based spatial join algorithms by a factor of 2 to 18 and is particularly efficient when joining a dense dataset with several sparse datasets.","PeriodicalId":269347,"journal":{"name":"Proceedings of the 25th International Conference on Scientific and Statistical Database Management","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121861212","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

GPS: a graph processing system GPS:一个图形处理系统

Proceedings of the 25th International Conference on Scientific and Statistical Database Management Pub Date : 2013-07-29 DOI: 10.1145/2484838.2484843

S. Salihoglu, J. Widom

引用次数: 567

Data vaults: a database welcome to scientific file repositories 数据库:欢迎加入科学文件存储库的数据库

Proceedings of the 25th International Conference on Scientific and Statistical Database Management Pub Date : 2013-07-29 DOI: 10.1145/2484838.2484876

M. Ivanova, Y. Kargin, M. Kersten, S. Manegold, Y. Zhang, M. Datcu, Daniela Espinoza

{"title":"Data vaults: a database welcome to scientific file repositories","authors":"M. Ivanova, Y. Kargin, M. Kersten, S. Manegold, Y. Zhang, M. Datcu, Daniela Espinoza","doi":"10.1145/2484838.2484876","DOIUrl":"https://doi.org/10.1145/2484838.2484876","url":null,"abstract":"Efficient management and exploration of high-volume scientific file repositories have become pivotal for advancement in science. We propose to demonstrate the Data Vault, an extension of the database system architecture that transparently opens scientific file repositories for efficient in-database processing and exploration. The Data Vault facilitates science data analysis using high-level declarative languages, such as the traditional SQL and the novel array-oriented SciQL. Data of interest are loaded from the attached repository in a just-in-time manner without need for up-front data ingestion. The demo is built around concrete implementations of the Data Vault for two scientific use cases: seismic time series and Earth observation images. The seismic Data Vault uses the queries submitted by the audience to illustrate the internals of Data Vault functioning by revealing the mechanisms of dynamic query plan generation and on-demand external data ingestion. The image Data Vault shows an application view from the perspective of data mining researchers.","PeriodicalId":269347,"journal":{"name":"Proceedings of the 25th International Conference on Scientific and Statistical Database Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122254487","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

Shortlisting top-K assignments 入围top-K任务

Proceedings of the 25th International Conference on Scientific and Statistical Database Management Pub Date : 2013-07-29 DOI: 10.1145/2484838.2484859

Yimin Lin, K. Mouratidis

{"title":"Shortlisting top-K assignments","authors":"Yimin Lin, K. Mouratidis","doi":"10.1145/2484838.2484859","DOIUrl":"https://doi.org/10.1145/2484838.2484859","url":null,"abstract":"In this paper we identify a novel query type, the top-K assignment query (αTop-K). Consider a set of objects and a set of suppliers, where each object must be assigned to one supplier. Assume that there is a cost associated with every object-supplier pair. If we allocate each object to the server with the smallest cost (for the specific object), the derived overall assignment will have the minimum total cost. In many scenarios, however, runner-up assignments may be required too, like for example when a decision maker needs to make additional considerations, not captured by individual object-supplier costs. In this case, it is necessary to examine several shortlisted assignments before choosing one. This motivates the αTop-K query, which computes the K best assignments, i.e., those achieving the K smallest total costs. Algorithms for the traditional assignment ranking problem could be adapted to process the query, but their time requirements are prohibitive for large datasets (cubic to the input size). In this work we exploit the specific properties of the αTop-K problem and develop scalable methods for its processing. We also consider its incremental version, where K is not specified in advance; instead, the best assignments are iteratively computed on demand. An empirical evaluation with real data verifies the practicality and efficiency of our framework.","PeriodicalId":269347,"journal":{"name":"Proceedings of the 25th International Conference on Scientific and Statistical Database Management","volume":"14 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120964333","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

Proceedings of the 25th International Conference on Scientific and Statistical Database Management 第25届科学与统计数据库管理国际会议论文集

Proceedings of the 25th International Conference on Scientific and Statistical Database Management Pub Date : 1900-01-01 DOI: 10.1145/2484838

引用次数: 2