Individuals and organizations are using databases to store personal information at an unprecedented rate. This creates a quandary for data providers. They are responsible for protecting the privacy of individuals described in their database. On the other hand, data providers are sometimes required to provide statistics about their data instead of sharing it wholesale with strong assurances that these answers are correct and complete such as in regulatory filings for the US SEC and other goverment organizations. We introduce a system, ZKSQL , that provides authenticated answers to ad-hoc SQL queries with zero-knowledge proofs. Its proofs show that the answers are correct and sound with respect to the database's contents and they do not divulge any information about its input records. This system constructs proofs over the steps in a query's evaluation and it accelerates this process with authenticated set operations. We validate the efficiency of this approach over a suite of TPC-H queries and our results show that ZKSQL achieves two orders of magnitude speedup over the baseline.
{"title":"ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs","authors":"Xiling Li, Chenkai Weng, Yongxin Xu, Xiao Wang, Jennie Duggan","doi":"10.14778/3594512.3594513","DOIUrl":"https://doi.org/10.14778/3594512.3594513","url":null,"abstract":"Individuals and organizations are using databases to store personal information at an unprecedented rate. This creates a quandary for data providers. They are responsible for protecting the privacy of individuals described in their database. On the other hand, data providers are sometimes required to provide statistics about their data instead of sharing it wholesale with strong assurances that these answers are correct and complete such as in regulatory filings for the US SEC and other goverment organizations.\u0000 \u0000 We introduce a system,\u0000 ZKSQL\u0000 , that provides authenticated answers to ad-hoc SQL queries with zero-knowledge proofs. Its proofs show that the answers are correct and sound with respect to the database's contents and they do not divulge any information about its input records. This system constructs proofs over the steps in a query's evaluation and it accelerates this process with authenticated set operations. We validate the efficiency of this approach over a suite of TPC-H queries and our results show that ZKSQL achieves two orders of magnitude speedup over the baseline.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"199 1","pages":"1804-1816"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82811134","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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594516
Eriq Augustine, L. Getoor
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 templates 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 collective grounding 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.
{"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":"5 1","pages":"1843-1855"},"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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594525
Xu Chen, Zhen Wang, Shuncheng Liu, Yaliang Li, Kai Zeng, Bolin Ding, Jingren Zhou, Han Su, Kai Zheng
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, BASE , to bridge the gap between cost and latency. After learning a policy based on cost signals in its first stage, BASE formulates transferring the reward function as a variant of inverse reinforcement learning. Intuitively, BASE 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 BASE 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.
{"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":"81 1","pages":"1958-1966"},"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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594521
Xenophon Kitsios, Panagiotis Liakos, Katia Papakonstantinopoulou, Y. Kotidis
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.
{"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":"2674 1","pages":"1910-1922"},"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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594524
W. Fan, Resul Tugay, Yaoshu Wang, Min Xie, M. Ali
This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with currency constraints; it also provides augmented training data as feedback for the creator to improve the ranking in the next round. The process proceeds until the temporal order obtained becomes stable. Using real-life and synthetic datasets, we show that GATE is able to determine temporal orders with F -measure above 80%, improving deep learning by 7.8% and rule-based methods by 34.4%.
本文研究了在没有完整时间戳的情况下，如何确定属于同一实体的一组元组中属性值的时间顺序。我们提出了一个创造者-批评家框架，通过结合深度学习和基于规则的推理来学习和推断时间顺序，称为GATE (Get the lATEst)。GATE的创建者通过深度学习训练排序模型，根据属性之间的相关性学习时间顺序和对属性值进行排序。然后，评论家验证学习到的时间顺序，并通过追踪具有货币约束的数据推断出更多的排名对;它还提供增强的训练数据作为创建者的反馈，以便在下一轮中提高排名。这个过程一直进行，直到获得的时间顺序变得稳定为止。使用现实生活和合成数据集，我们表明GATE能够确定F -measure超过80%的时间顺序，将深度学习提高7.8%，将基于规则的方法提高34.4%。
{"title":"Learning and Deducing Temporal Orders","authors":"W. Fan, Resul Tugay, Yaoshu Wang, Min Xie, M. Ali","doi":"10.14778/3594512.3594524","DOIUrl":"https://doi.org/10.14778/3594512.3594524","url":null,"abstract":"\u0000 This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with currency constraints; it also provides augmented training data as feedback for the creator to improve the ranking in the next round. The process proceeds until the temporal order obtained becomes stable. Using real-life and synthetic datasets, we show that GATE is able to determine temporal orders with\u0000 F\u0000 -measure above 80%, improving deep learning by 7.8% and rule-based methods by 34.4%.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"8 1","pages":"1944-1957"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85255090","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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594518
Gerardo Vitagliano, Mazhar Hameed, Lan Jiang, Lucas Reisener, Eugene Wu, Felix Naumann
Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic "pollution" process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.
{"title":"Pollock: A Data Loading Benchmark","authors":"Gerardo Vitagliano, Mazhar Hameed, Lan Jiang, Lucas Reisener, Eugene Wu, Felix Naumann","doi":"10.14778/3594512.3594518","DOIUrl":"https://doi.org/10.14778/3594512.3594518","url":null,"abstract":"Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic \"pollution\" process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"679 1","pages":"1870-1882"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76867972","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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594517
Jan Niklas Adams, Cameron Pitsch, T. Brockhoff, Wil M.P. van der Aalst
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, Process Concept Drift (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: How do PCD detection techniques compare against each other? 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.
{"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":"410 1","pages":"1856-1869"},"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}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594527
Xi Zhao, Yao Tian, Kai Huang, Bolong Zheng, Xiaofang Zhou
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.
{"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":"371 1","pages":"1979-1991"},"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}
Pub Date : 2023-03-22DOI: 10.48550/arXiv.2303.12851
M. Calautti, Mostafa Milani, Andreas Pieris
The chase procedure is a fundamental algorithmic tool in databases that allows us to reason with constraints, such as existential rules, with a plethora of applications. It takes as input a database and a set of constraints, and iteratively completes the database as dictated by the constraints. A key challenge, though, is the fact that it may not terminate, which leads to the problem of checking whether it terminates given a database and a set of constraints. In this work, we focus on the semi-oblivious version of the chase, which is well-suited for practical implementations, and linear existential rules, a central class of constraints with several applications. In this setting, there is a mature body of theoretical work that provides syntactic characterizations of when the chase terminates, algorithms for checking chase termination, and precise complexity results. Our main objective is to experimentally evaluate the existing chase termination algorithms with the aim of understanding which input parameters affect their performance, clarifying whether they can be used in practice, and revealing their performance limitations.
{"title":"Semi-Oblivious Chase Termination for Linear Existential Rules: An Experimental Study","authors":"M. Calautti, Mostafa Milani, Andreas Pieris","doi":"10.48550/arXiv.2303.12851","DOIUrl":"https://doi.org/10.48550/arXiv.2303.12851","url":null,"abstract":"The chase procedure is a fundamental algorithmic tool in databases that allows us to reason with constraints, such as existential rules, with a plethora of applications. It takes as input a database and a set of constraints, and iteratively completes the database as dictated by the constraints. A key challenge, though, is the fact that it may not terminate, which leads to the problem of checking whether it terminates given a database and a set of constraints. In this work, we focus on the semi-oblivious version of the chase, which is well-suited for practical implementations, and linear existential rules, a central class of constraints with several applications. In this setting, there is a mature body of theoretical work that provides syntactic characterizations of when the chase terminates, algorithms for checking chase termination, and precise complexity results. Our main objective is to experimentally evaluate the existing chase termination algorithms with the aim of understanding which input parameters affect their performance, clarifying whether they can be used in practice, and revealing their performance limitations.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"612 1","pages":"2858-2870"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77022927","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}
Pub Date : 2023-03-06DOI: 10.48550/arXiv.2303.03379
Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li
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.
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