Proceedings of the ACM on Management of Data最新文献

The Battleship Approach to the Low Resource Entity Matching Problem 低资源实体匹配问题的战舰方法

Proceedings of the ACM on Management of Data Pub Date : 2023-11-27 DOI: 10.1145/3626711

Bar Genossar, Avigdor Gal, Roee Shraga

{"title":"The Battleship Approach to the Low Resource Entity Matching Problem","authors":"Bar Genossar, Avigdor Gal, Roee Shraga","doi":"10.1145/3626711","DOIUrl":"https://doi.org/10.1145/3626711","url":null,"abstract":"Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity. Recent advances in deep learning methods, using pre-trained language models, were proposed for resolving entity matching. Although demonstrating unprecedented results, these solutions suffer from a major drawback as they require large amounts of labeled data for training, and, as such, are inadequate to be applied to low resource entity matching problems. To overcome the challenge of obtaining sufficient labeled data we offer a new active learning approach, focusing on a selection mechanism that exploits unique properties of entity matching. We argue that a distributed representation of a tuple pair indicates its informativeness when considered among other pairs. This is used consequently in our approach that iteratively utilizes space-aware considerations. Bringing it all together, we treat the low resource entity matching problem as a Battleship game, hunting indicative samples, focusing on positive ones, through awareness of the latent space along with careful planning of next sampling iterations. An extensive experimental analysis shows that the proposed algorithm outperforms state-of-the-art active learning solutions to low resource entity matching, and although using less samples, can be as successful as state-of-the-art fully trained known algorithms.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"3 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230756","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

HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 红图在多个 GPU 上进行可扩展的全图 GNN 训练

Proceedings of the ACM on Management of Data Pub Date : 2023-11-25 DOI: 10.1145/3626733

Qiange Wang, Yao Chen, Weng-Fai Wong, Bingsheng He

{"title":"HongTu: Scalable Full-Graph GNN Training on Multiple GPUs","authors":"Qiange Wang, Yao Chen, Weng-Fai Wong, Bingsheng He","doi":"10.1145/3626733","DOIUrl":"https://doi.org/10.1145/3626733","url":null,"abstract":"Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation resources. To accelerate this training process, researchers have proposed employing multi-GPU processing. However the scalability of existing frameworks is limited as they necessitate maintaining the training data for every layer in GPU memory. To efficiently train on large graphs, we present HongTu, a scalable full-graph GNN training system running on GPU-accelerated platforms. HongTu stores vertex data in CPU memory and offloads training to GPUs. HongTu employs a memory-efficient full-graph training framework that reduces runtime memory consumption by using partition-based training and recomputation-caching-hybrid intermediate data management. To address the issue of increased host-GPU communication caused by duplicated neighbor access among partitions, HongTu employs a deduplicated communication framework that converts the redundant host-GPU communication to efficient inter/intra-GPU data access. Further, HongTu uses a cost model-guided graph reorganization method to minimize communication overhead. Experimental results on a 4XA100 GPU server show that HongTu effectively supports billion-scale full-graph GNN training while reducing host-GPU data communication by 25%-71%. Compared to the full-graph GNN system DistGNN running on 16 CPU nodes, HongTu achieves speedups ranging from 7.8X to 20.2X. For small graphs where the training data fits into the GPUs, HongTu achieves performance comparable to existing GPU-based GNN systems.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"28 63","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237490","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

High-Ratio Compression for Machine-Generated Data 机器生成数据的高比率压缩

Proceedings of the ACM on Management of Data Pub Date : 2023-11-23 DOI: 10.1145/3626732

Jiujing Zhang, Zhitao Shen, Shiyu Yang, Lingkai Meng, Chuan Xiao, Wei Jia, Yue Li, Qinhui Sun, Wenjie Zhang, Xuemin Lin

{"title":"High-Ratio Compression for Machine-Generated Data","authors":"Jiujing Zhang, Zhitao Shen, Shiyu Yang, Lingkai Meng, Chuan Xiao, Wei Jia, Yue Li, Qinhui Sun, Wenjie Zhang, Xuemin Lin","doi":"10.1145/3626732","DOIUrl":"https://doi.org/10.1145/3626732","url":null,"abstract":"Machine-generated data is rapidly growing and poses challenges for data-intensive systems, especially as the growth of data outpaces the growth of storage space. To cope with the storage issue, compression plays a critical role in storage engines, particularly for data-intensive applications, where a high compression ratio and efficient random access are essential. However, existing compression techniques tend to focus on general-purpose and data block approaches, but overlook the inherent structure of machine-generated data and hence result in low compression ratios or limited lookup efficiency. To address these limitations, we introduce the Pattern-Based Compression (PBC) algorithm, which specifically targets patterns in machine-generated data to achieve Pareto-optimality in most cases. Unlike traditional data block-based methods, PBC compresses data on a per-record basis, facilitating rapid random access. Our experimental evaluation demonstrates that PBC, on average, achieves a compression ratio twice as high as the state-of-the-art techniques while maintaining competitive compression and decompression speeds. We also integrate PBC to a production database system and achieve improvements on both comparison ratio and throughput.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"18 1","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139245506","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

Hierarchical Cut Labelling - Scaling Up Distance Queries on Road Networks 分层剪切标签--扩大路网距离查询的规模

Proceedings of the ACM on Management of Data Pub Date : 2023-11-18 DOI: 10.1145/3626731

Muhammad Farhan, Henning Koehler, Robert Ohms, Qing Wang

{"title":"Hierarchical Cut Labelling - Scaling Up Distance Queries on Road Networks","authors":"Muhammad Farhan, Henning Koehler, Robert Ohms, Qing Wang","doi":"10.1145/3626731","DOIUrl":"https://doi.org/10.1145/3626731","url":null,"abstract":"Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based labellings, highway-based labellings, and tree decomposition labellings. Hub-based and highway-based labellings exploit hierarchical structures of road networks with the aim to reduce labelling size for improving query efficiency. However, these solutions still result in large search spaces on distance labels at query time, particularly when road networks are large. Tree decomposition labellings leverage a hierarchy of vertices to reduce search spaces over distance labels at query time, but such a hierarchy is generated using tree decomposition techniques, which may yield very large labelling sizes and slow querying. In this paper, we propose a novel solution hierarchical cut 2-hop labelling (HC2L) to address the drawbacks of the existing works. Our solution combines the benefits of hierarchical structures from both perspectives - reduce the size of a distance labelling at preprocessing time and further reduce the search space on a distance labelling at query time. At its core, we propose a new hierarchy, balanced tree hierarchy, which enables a fast, efficient data structure to reduce the size of distance labelling and to select a very small subset of labels to compute the shortest-path distance at query time. To speed up the construction process of HC2L, we further propose a parallel variant of our method, namely HC2L^p. We have evaluated our solution on 10 large real-world road networks through extensive experiments. The results show that our method is 1.5-4 times faster in terms of query processing while being comparable in terms of labelling construction time and achieving up to 60% smaller labelling size compared to the state-of-the-art approaches.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"23 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261014","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

DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries DP-starJ：面向分析性星形连接查询的差分私有方案

Proceedings of the ACM on Management of Data Pub Date : 2023-10-07 DOI: 10.1145/3626725

Congcong Fu, Hui Li, Jian Lou, Huizhen Li, Jia Cui

{"title":"DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries","authors":"Congcong Fu, Hui Li, Jian Lou, Huizhen Li, Jia Cui","doi":"10.1145/3626725","DOIUrl":"https://doi.org/10.1145/3626725","url":null,"abstract":"Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest dp efforts specifically designed for join, if directly applied to star-join query, will suffer from extremely large estimation errors and expensive computational cost. In this paper, we are thus motivated to propose DP-starJ, a novel Differentially Private framework for star-Join queries. DP-starJ consists of a series of strategies tailored to specific features of star-join, including 1) we unveil the different effects of fact and dimension tables on the neighboring database instances, and accordingly revisit the definitions tailored to different cases of star-join; 2) we propose Predicate Mechanism (PM), which utilizes predicate perturbation to inject noise into the join procedure instead of the results; 3) to further boost the robust performance, we propose a dp-compliant star-join algorithm for various types of star-join tasks based on PM. We provide both theoretical analysis and empirical study, which demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of accuracy, efficiency, and scalability.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"1 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139322025","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

NOCAP: Near-Optimal Correlation-Aware Partitioning Joins NOCAP：近最优相关性感知分区连接

Proceedings of the ACM on Management of Data Pub Date : 2023-10-04 DOI: 10.1145/3626739

Zichen Zhu, Xiao Hu, Manos Athanassoulis

{"title":"NOCAP: Near-Optimal Correlation-Aware Partitioning Joins","authors":"Zichen Zhu, Xiao Hu, Manos Athanassoulis","doi":"10.1145/3626739","DOIUrl":"https://doi.org/10.1145/3626739","url":null,"abstract":"Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution. In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the optimal partitioning using a new cost-based analysis of partitioning-based joins that is tailored for primary key - foreign key (PK-FK) joins, one of the most common join types. This optimal partitioning strategy has a high memory cost, thus, we further derive an approximate algorithm that has tunable memory cost and leads to near-optimal results. Our algorithm, termed NOCAP (Near-Optimal Correlation-Aware Partitioning) join, outperforms the state of the art for skewed correlations by up to 30%, and the textbook Grace Hash Join by up to 4×. Further, for a limited memory budget, NOCAP outperforms HHJ by up to 10%, even for uniform correlation. Overall, NOCAP dominates state-of-the-art algorithms and mimics the best algorithm for a memory budget varying from below √||relation|| to more than ||relation||.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"24 1","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139323647","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

DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance DProvDB：利用多分析师证明进行差异化私有查询处理

Proceedings of the ACM on Management of Data Pub Date : 2023-09-19 DOI: 10.1145/3626761

Shufan Zhang, Xi He

引用次数: 0

DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks DGC：利用图块分割训练具有时空不均匀性的动态图

Proceedings of the ACM on Management of Data Pub Date : 2023-09-07 DOI: 10.1145/3626724

Fahao Chen, Peng Li, Celimuge Wu

{"title":"DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks","authors":"Fahao Chen, Peng Li, Celimuge Wu","doi":"10.1145/3626724","DOIUrl":"https://doi.org/10.1145/3626724","url":null,"abstract":"Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received considerable attention by AI community and various DGNN models have been proposed, building a distributed system for efficient DGNN training is still challenging. It has been well recognized that how to partition the dynamic graph and assign workloads to multiple GPUs plays a critical role in training acceleration. Existing works partition a dynamic graph into snapshots or temporal sequences, which only work well when the graph has uniform spatio-temporal structures. However, dynamic graphs in practice are not uniformly structured, with some snapshots being very dense while others are sparse. To address this issue, we propose DGC, a distributed DGNN training system that achieves a 1.25× - 7.52× speedup over the state-of-the-art in our testbed. DGC's success stems from a new graph partitioning method that partitions dynamic graphs into chunks, which are essentially subgraphs with modest training workloads and few inter connections. This partitioning algorithm is based on graph coarsening, which can run very fast on large graphs. In addition, DGC has a highly efficient run-time, powered by the proposed chunk fusion and adaptive stale aggregation techniques. Extensive experimental results on 3 typical DGNN models and 4 popular dynamic graph datasets are presented to show the effectiveness of DGC.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"38 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139341779","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

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models SALI：基于概率模型的可扩展自适应学习索引框架

Proceedings of the ACM on Management of Data Pub Date : 2023-08-29 DOI: 10.1145/3626752

Jiake Ge, Huanchen Zhang, Boyu Shi, Yuan Luo, Yunda Guo, Yunpeng Chai, Yuxing Chen, Anqun Pan

{"title":"SALI: A Scalable Adaptive Learned Index Framework based on Probability Models","authors":"Jiake Ge, Huanchen Zhang, Boyu Shi, Yuan Luo, Yunda Guo, Yunpeng Chai, Yuxing Chen, Anqun Pan","doi":"10.1145/3626752","DOIUrl":"https://doi.org/10.1145/3626752","url":null,"abstract":"The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.","PeriodicalId":510828,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":"13 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139348382","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

ChainedFilter: Combining Membership Filters by Chain Rule ChainedFilter：通过链式规则组合成员资格过滤器

Proceedings of the ACM on Management of Data Pub Date : 2023-08-25 DOI: 10.1145/3626721

Haoyu Li, Liuhui Wang, Qizhi Chen, Jianan Ji, Yuhan Wu, Yikai Zhao, Tong Yang, Aditya Akella

引用次数: 0