Proc. VLDB Endow.最新文献_第7页

ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs ZKSQL:基于零知识证明的可验证高效查询评估

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594513

Xiling Li, Chenkai Weng, Yongxin Xu, Xiao Wang, Jennie Duggan

引用次数: 0

Collective Grounding: Applying Database Techniques to Grounding Templated Models 集体接地:应用数据库技术接地模板模型

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594516

Eriq Augustine, L. Getoor

{"title":"Collective Grounding: Applying Database Techniques to Grounding Templated Models","authors":"Eriq Augustine, L. Getoor","doi":"10.14778/3594512.3594516","DOIUrl":"https://doi.org/10.14778/3594512.3594516","url":null,"abstract":"\u0000 The process of instantiating, or \"grounding\", a first-order model is a fundamental component of reasoning in logic. It has been widely studied in the context of theorem proving, database theory, and artificial intelligence. Within the relational learning community, the concept of grounding has been expanded to apply to models that use more general\u0000 templates\u0000 in the place of first-order logical formulae. In order to perform inference, grounding of these templates is required for instantiating a distribution over possible worlds. However, because of the complex data dependencies stemming from instantiating generalized templates with interconnected data, grounding is often the key computational bottleneck to relational learning. While we motivate our work in the context of relational learning, similar issues arise in probabilistic databases, particularly those that do not make strong tuple independence assumptions. In this paper, we investigate how key techniques from relational database theory can be utilized to improve the computational efficiency of the grounding process. We introduce the notion of\u0000 collective grounding\u0000 which treats logical programs not as a collection of independent rules, but instead as a joint set of interdependent workloads that can be shared. We introduce the theoretical concept of collective grounding, the components necessary in a collective grounding system, implementations of these components, and show how to use database theory to speed up these components. We demonstrate collective groundings effectiveness on seven popular datasets, and show up to a 70% reduction in runtime using collective grounding. Our results are fully reproducible and all code, data, and experimental scripts are included.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83251596","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

BASE: Bridging the Gap between Cost and Latency for Query Optimization BASE:在查询优化的成本和延迟之间架起桥梁

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594525

Xu Chen, Zhen Wang, Shuncheng Liu, Yaliang Li, Kai Zeng, Bolin Ding, Jingren Zhou, Han Su, Kai Zheng

{"title":"BASE: Bridging the Gap between Cost and Latency for Query Optimization","authors":"Xu Chen, Zhen Wang, Shuncheng Liu, Yaliang Li, Kai Zeng, Bolin Ding, Jingren Zhou, Han Su, Kai Zheng","doi":"10.14778/3594512.3594525","DOIUrl":"https://doi.org/10.14778/3594512.3594525","url":null,"abstract":"\u0000 Some recent works have shown the advantages of reinforcement learning (RL) based learned query optimizers. These works often use the cost (i.e., the estimation of cost model) or the latency (i.e., execution time) as guidance signals for training their learned models. However, cost-based learning underperforms in latency and latency-based learning is time-intensive. In order to bypass such a dilemma, researchers attempt to transfer a learned value network from the cost domain to the latency domain. We recognize critical insights in cost/latency-based training, prompting us to transfer the reward function rather than the value network. Based on this idea, we propose a two-stage RL-based framework,\u0000 BASE\u0000 , to bridge the gap between cost and latency. After learning a policy based on cost signals in its first stage,\u0000 BASE\u0000 formulates transferring the reward function as a variant of inverse reinforcement learning. Intuitively,\u0000 BASE\u0000 learns to calibrate the reward function and updates the policy regarding the calibrated one in a mutually-improved manner. Extensive experiments exhibit the superiority of\u0000 BASE\u0000 on two benchmark datasets: Our optimizer outperforms traditional DBMS, using 30% less training time than SOTA methods. Meanwhile, our approach can enhance the efficiency of other learning-based optimizers.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83984260","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

Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging Sim-Piece:通过相似分段合并实现的高精度分段线性逼近

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594521

Xenophon Kitsios, Panagiotis Liakos, Katia Papakonstantinopoulou, Y. Kotidis

{"title":"Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging","authors":"Xenophon Kitsios, Panagiotis Liakos, Katia Papakonstantinopoulou, Y. Kotidis","doi":"10.14778/3594512.3594521","DOIUrl":"https://doi.org/10.14778/3594512.3594521","url":null,"abstract":"Approximating series of timestamped data points using a sequence of line segments with a maximum error guarantee is a fundamental data compression problem, termed as piecewise linear approximation (PLA). Due to the increasing need to analyze massive collections of time-series data in diverse domains, the problem has recently received significant attention, and recent PLA algorithms that have emerged do help us handle the overwhelming amount of information, at the cost of some precision loss. More specifically, these algorithms entail a trade-off between the maximum precision loss and the space savings achieved. However, advances in the area of lossless compression are undercutting the offerings of PLA techniques in real datasets. In this work, we propose Sim-Piece, a novel lossy compression algorithm for time-series data that optimizes the space requirements of representing PLA line segments, by finding the minimum number of groups we can organize these segments into, to represent them jointly. Our experimental evaluation demonstrates that our approach readily outperforms competing techniques, attaining compression ratios with more than twofold improvement on average over what PLA algorithms can offer. This allows for providing significantly higher accuracy with equivalent space requirements. Moreover, our algorithm, due to the simplicity of its merging phase, imposes little overhead while compacting the PLA description, offering a significantly improved trade-off between space and running time. The aforementioned benefits of our approach significantly improve the efficiency in which we can store time-series data, while allowing a tight maximum error in the representation of their values.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87813495","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

Learning and Deducing Temporal Orders 学习和演绎时间顺序

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594524

W. Fan, Resul Tugay, Yaoshu Wang, Min Xie, M. Ali

引用次数: 0

Pollock: A Data Loading Benchmark Pollock:一个数据加载基准

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594518

Gerardo Vitagliano, Mazhar Hameed, Lan Jiang, Lucas Reisener, Eugene Wu, Felix Naumann

引用次数: 2

An Experimental Evaluation of Process Concept Drift Detection 过程概念漂移检测的实验评价

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594517

Jan Niklas Adams, Cameron Pitsch, T. Brockhoff, Wil M.P. van der Aalst

{"title":"An Experimental Evaluation of Process Concept Drift Detection","authors":"Jan Niklas Adams, Cameron Pitsch, T. Brockhoff, Wil M.P. van der Aalst","doi":"10.14778/3594512.3594517","DOIUrl":"https://doi.org/10.14778/3594512.3594517","url":null,"abstract":"\u0000 Process mining provides techniques to learn models from event data. These models can be descriptive (e.g., Petri nets) or predictive (e.g., neural networks). The learned models offer operational support to process owners by conformance checking, process enhancement, or predictive monitoring. However, processes are frequently subject to significant changes, making the learned models outdated and less valuable over time. To tackle this problem,\u0000 Process Concept Drift\u0000 (PCD) detection techniques are employed. By identifying when the process changes occur, one can replace learned models by relearning, updating, or discounting pre-drift knowledge. Various techniques to detect PCDs have been proposed. However, each technique's evaluation focuses on different evaluation goals out of accuracy, latency, versatility, scalability, parameter sensitivity, and robustness. Furthermore, the employed evaluation techniques and data sets differ. Since many techniques are not evaluated against more than one other technique, this lack of comparability raises one question:\u0000 How do PCD detection techniques compare against each other?\u0000 With this paper, we propose, implement, and apply a unified evaluation framework for PCD detection. We do this by collecting evaluation goals and evaluation techniques together with data sets. We derive a representative sample of techniques from a taxonomy for PCD detection. The implemented techniques and proposed evaluation framework are provided in a publicly available repository. We present the results of our experimental evaluation and observe that none of the implemented techniques works well across all evaluation goals. However, the results indicate future improvement points of algorithms and guide practitioners.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76536911","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

Towards Efficient Index Construction and Approximate Nearest Neighbor Search in High-Dimensional Spaces 高维空间中高效索引构建与近似最近邻搜索

Proc. VLDB Endow. Pub Date : 2023-04-01 DOI: 10.14778/3594512.3594527

Xi Zhao, Yao Tian, Kai Huang, Bolong Zheng, Xiaofang Zhou

{"title":"Towards Efficient Index Construction and Approximate Nearest Neighbor Search in High-Dimensional Spaces","authors":"Xi Zhao, Yao Tian, Kai Huang, Bolong Zheng, Xiaofang Zhou","doi":"10.14778/3594512.3594527","DOIUrl":"https://doi.org/10.14778/3594512.3594527","url":null,"abstract":"The approximate nearest neighbor (ANN) search in high-dimensional spaces is a fundamental but computationally very expensive problem. Many methods have been designed for solving the ANN problem, such as LSH-based methods and graph-based methods. The LSH-based methods can be costly to reach high query quality due to the hash-boundary issues, while the graph-based methods can achieve better query performance by greedy expansion in an approximate proximity graph (APG). However, the construction cost of these APGs can be one or two orders of magnitude higher than that for building hash-based indexes. In addition, they fail short in incrementally maintaining APGs as the underlying dataset evolves. In this paper, we propose a novel approach named LSH-APG to build APGs and facilitate fast ANN search using a lightweight LSH framework. LSH-APG builds an APG via consecutively inserting points based on their nearest neighbor relationship with an efficient and accurate LSH-based search strategy. A high-quality entry point selection technique and an LSH-based pruning condition are developed to accelerate index construction and query processing by reducing the number of points to be accessed during the search. LSH-APG supports fast maintenance of APGs in lieu of building them from scratch as dataset evolves. Its maintenance cost and query cost for a point is proven to be less affected by dataset cardinality. Extensive experiments on real-world and synthetic datasets demonstrate that LSH-APG incurs significantly less construction cost but achieves better query performance than existing graph-based methods.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77970591","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

Semi-Oblivious Chase Termination for Linear Existential Rules: An Experimental Study 线性存在规则的半遗忘追逐终止:实验研究

Proc. VLDB Endow. Pub Date : 2023-03-22 DOI: 10.48550/arXiv.2303.12851

M. Calautti, Mostafa Milani, Andreas Pieris

引用次数: 0

SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning SUREL+:基于可扩展子图的图表示学习从步行到集合

Proc. VLDB Endow. Pub Date : 2023-03-06 DOI: 10.48550/arXiv.2303.03379

Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li

{"title":"SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning","authors":"Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li","doi":"10.48550/arXiv.2303.03379","DOIUrl":"https://doi.org/10.48550/arXiv.2303.03379","url":null,"abstract":"Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability. Most previous SGRL models face computational issues related to the high cost of extracting subgraphs for each training or testing query. Recently, SUREL was proposed to accelerate SGRL, which samples random walks offline and joins these walks online as a proxy of subgraphs for prediction. Thanks to the reusability of sampled walks across different queries, SUREL achieves state-of-the-art performance in terms of scalability and prediction accuracy. However, SUREL still suffers from high computational overhead caused by node redundancy in sampled walks. In this work, we propose a novel framework SUREL+ that upgrades SUREL by using node sets instead of walks to represent subgraphs. By definition, such set-based representations avoid repeated nodes, but node sets can be irregular in size. To solve this issue, we design a dedicated sparse data structure to efficiently store and access node sets, and provide a specialized operator to join them in parallel batches. SUREL+ is modularized to support multiple types of set samplers, structural features, and neural encoders to complement the loss of structural information after the reduction from walks to sets. Extensive experiments have been performed to verify the effectiveness of SUREL+ in the prediction tasks of links, relation types, and higher-order patterns. SUREL+ achieves 3--11× speedups of SUREL while maintaining comparable or even better prediction performance; compared to other SGRL baselines, SUREL+ achieves ~20× speedups and significantly improves the prediction accuracy.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89015828","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: 2