Advances in database technology : proceedings. International Conference on Extending Database Technology最新文献_第4页

Topio Marketplace: Search and Discovery of Geospatial Data Topio市场:地理空间数据的搜索和发现

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.73

Andra Ionescu, A. Alexandridou, Leonidas Ikonomou, Kyriakos Psarakis, Kostas Patroumpas, Georgios Chatzigeorgakidis, Dimitrios Skoutas, Spiros Athanasiou, Rihan Hai, Asterios Katsifodimos

引用次数: 0

FLIRT: A Fast Learned Index for Rolling Time frames 调情:滚动时间框架的快速学习索引

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.19

Guang Yang, Liang Liang, A. Hadian, T. Heinis

{"title":"FLIRT: A Fast Learned Index for Rolling Time frames","authors":"Guang Yang, Liang Liang, A. Hadian, T. Heinis","doi":"10.48786/edbt.2023.19","DOIUrl":"https://doi.org/10.48786/edbt.2023.19","url":null,"abstract":"Efficiently managing and querying sliding windows is a key com-ponent in stream processing systems. Conventional index structures such as the B+Tree are not efficient for handling a stream of time-series data, where the data is very dynamic, and the indexes must be updated on a continuous basis. Stream processing structures such as queues can accommodate large volumes of updates (enqueue and dequeue); however, they are not efficient for fast retrieval. This paper proposes FLIRT, a parameter-free index structure that manages a sliding window over a high-velocity stream of data and simultaneously supports efficient range queries on the sliding window. FLIRT uses learned indexing to reduce the lookup time. This is enabled by organising the incoming stream of time-series data into linearly predictable segments, allowing fast queue operations such as enqueue, dequeue, and search. We further boost the search performance by introducing two multithreaded versions of FLIRT for different query workloads. Experimental results show up to 7 × speedup over conventional indexes, 8 × speedup over queues, and up to 109 × speedup over learned indexes.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"39 1","pages":"234-246"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85503539","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

Smart Derivative Contracts in DatalogMTL DatalogMTL中的智能衍生品合约

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.65

Andrea Colombo, Luigi Bellomarini, S. Ceri, Eleonora Laurenza

引用次数: 0

GAM Forest Explanation GAM森林解说

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.14

C. Lucchese, S. Orlando, R. Perego, Alberto Veneri

引用次数: 0

Fast and Efficient Update Handling for Graph H2TAP 快速和有效的更新处理图H2TAP

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.60

M. Jibril, Hani Al-Sayeh, Alexander Baumstark, K. Sattler

引用次数: 0

EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Synchronization EGG-SynC:基于同步的精确gpu并行网格聚类

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.16

Jakob Rødsgaard Jørgensen, I. Assent

{"title":"EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Synchronization","authors":"Jakob Rødsgaard Jørgensen, I. Assent","doi":"10.48786/edbt.2023.16","DOIUrl":"https://doi.org/10.48786/edbt.2023.16","url":null,"abstract":"Clustering by synchronization (SynC) is a clustering method that is motivated by the natural phenomena of synchronization and is based on the Kuramoto model. The idea is to iteratively drag similar objects closer to each other until they have synchronized. SynC has been adapted to solve several well-known data mining tasks such as subspace clustering, hierarchical clustering, and streaming clustering. This shows that the SynC model is very versatile. Sadly, SynC has an 𝑂 ( 𝑇 × 𝑛 2 × 𝑑 ) complexity, which makes it impractical for larger datasets. E.g., Chen et al. [8] show runtimes of more than 10 hours for just 𝑛 = 70 , 000 data points, but improve this to just above one hour by using R-Trees in their method FSynC. Both are still impractical in real-life scenarios. Furthermore, SynC uses a termination criterion that brings no guarantees that the points have synchronized but instead just stops when most points are close to synchronizing. In this paper, our contributions are manifold. We propose a new termination criterion that guarantees that all points have synchronized. To achieve a much-needed reduction in runtime, we propose a strategy to summarize partitions of the data into a grid structure, a GPU-friendly grid structure to support this and neighborhood queries, and a GPU-parallelized algorithm for clustering by synchronization (EGG-SynC) that utilize these ideas. Furthermore, we provide an extensive evaluation against state-of-the-art showing 2 to 3 orders of magnitude speedup compared to SynC and FSynC.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"27 1","pages":"195-207"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75089729","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

Desis: Efficient Window Aggregation in Decentralized Networks 分布式网络中的高效窗口聚合

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.52

W. Yue, Lawrence Benson, T. Rabl

{"title":"Desis: Efficient Window Aggregation in Decentralized Networks","authors":"W. Yue, Lawrence Benson, T. Rabl","doi":"10.48786/edbt.2023.52","DOIUrl":"https://doi.org/10.48786/edbt.2023.52","url":null,"abstract":"Stream processing is widely applied in industry as well as in research to process unbounded data streams. In many use cases, specific data streams are processed by multiple continuous queries. Current systems group events of an unbounded data stream into bounded windows to produce results of individual queries in a timely fashion. For multiple concurrent queries, multiple concurrent and usually overlapping windows are generated. To reduce redundant computations and share partial results, state-of-the-art solutions divide windows into slices and then share the results of those slices. However, this is only applicable for queries with the same aggregation function and window measure, as in the case of overlaps for sliding windows. For multiple queries on the same stream with different aggregation functions and window measures, partial results cannot be shared. Furthermore, data streams are produced from devices that are distributed in large decentralized networks. Current systems cannot process queries on decentralized data streams efficiently. All queries in a decentralized network are either computed centrally or processed individually without exploiting partial results across queries. We present Desis, a stream processing system that can efficiently process multiple stream aggregation queries. We propose an aggregation engine that can share partial results between multiple queries with different window types, measures, and aggregation functions. In decentralized networks, Desis moves computation to data sources and shares overlapping computation as early as possible between queries. Desis outperforms existing solutions by orders of magnitude in throughput when processing multiple queries and can scale to millions of queries. In a decentralized setup, Desis can save up to 99% of network traffic and scale performance linearly.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"2 1","pages":"618-631"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78974443","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

Demonstrating Interactive SPARQL Formulation through Positive and Negative Examples and Feedback 通过正负例子和反馈演示交互式SPARQL公式

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.71

Akritas Akritidis, Yannis Tzitzikas

引用次数: 0

Learning over Sets for Databases 学习数据库的集合

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2024.07

Angjela Davitkova, Damjan Gjurovski, S. Michel

引用次数: 0

Patched Multi-Key Partitioning for Robust Query Performance 补丁多键分区鲁棒查询性能

Advances in database technology : proceedings. International Conference on Extending Database Technology Pub Date : 2023-01-01 DOI: 10.48786/edbt.2023.26

Steffen Kläbe, K. Sattler

引用次数: 0