国际时空数据库专题讨论会(SSTD)是每两年召开一次的专题讨论会,讨论了时空数据管理和相关技术方面的新研究,旨在设定未来的研究方向。SSTD专题讨论会主要关注时空数据库技术的理论基础、设计、实现和应用方面的原始结果。SSTD还欢迎来自应用专家和商业社区的经验报告,其中描述了在实际和创新应用中开发,运行和维护实际系统的经验教训。目的是在学术界,行业和政府之间,从不同的地理位置和职业阶段交换研究思想和成果。 官网地址:http://dblp.uni-trier.de/db/conf/ssd/

最新论文

Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs and groups, called bundles, over co-evolving time series. These are pairs or groups of subsequences whose values do not differ by more than {\epsilon} for at least delta consecutive timestamps, thus indicating common local patterns and trends. We first present a baseline algorithm that performs a sweep line scan across all timestamps to identify matches. Then, we propose a filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. Specifically, we introduce two block scanning algorithms for discovering local pairs and bundles respectively, which leverage the potential of checkpoints to aggressively prune the search space. We experimentally evaluate our methods against real-world and synthetic datasets, demonstrating a speed-up in execution time by an order of magnitude over the baseline. This paper has been published in the 16th International Symposium on Spatial and Temporal Databases (SSTD19).

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