Cross-Trust-Domain Processing. Data is now a commodity. We know how to compute and store it efficiently and reliably at scale. We have, however, paid less attention to the notion of trust. Yet, data owners today are no longer the entities storing or processing their data (medical records are stored on the cloud, data is shared across banks, etc.). In fact, distributed systems today consist of many different parties, whether it is cloud providers, jurisdictions, organisations or humans. Modern data processing and storage always straddles trust domains.
{"title":"Efficient Data Sharing across Trust Domains","authors":"Natacha Crooks","doi":"10.1145/3615952.3615962","DOIUrl":"https://doi.org/10.1145/3615952.3615962","url":null,"abstract":"Cross-Trust-Domain Processing. Data is now a commodity. We know how to compute and store it efficiently and reliably at scale. We have, however, paid less attention to the notion of trust. Yet, data owners today are no longer the entities storing or processing their data (medical records are stored on the cloud, data is shared across banks, etc.). In fact, distributed systems today consist of many different parties, whether it is cloud providers, jurisdictions, organisations or humans. Modern data processing and storage always straddles trust domains.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125726507","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}
L. Bertossi, B. Kimelfeld, Ester Livshits, Mikaël Monet
Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice.
{"title":"The Shapley Value in Database Management","authors":"L. Bertossi, B. Kimelfeld, Ester Livshits, Mikaël Monet","doi":"10.1145/3615952.3615954","DOIUrl":"https://doi.org/10.1145/3615952.3615954","url":null,"abstract":"Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705565","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}
Wei Dong, Juanru Fang, K. Yi, Yuchao Tao, Ashwin Machanavajjhala
Answering SPJA queries under differential privacy (DP), including graph pattern counting under node-DP as an important special case, has received considerable attention in recent years. The dual challenge of foreign-key constraints and self-joins is particularly tricky to deal with, and no existing DP mechanisms can correctly handle both. For the special case of graph pattern counting under node-DP, the existing mechanisms are correct (i.e., satisfy DP), but they do not offer nontrivial utility guarantees or are very complicated and costly. In this paper, we propose the first DP mechanism for answering arbitrary SPJA queries in a database with foreign-key constraints. Meanwhile, it achieves a fairly strong notion of optimality, which can be considered as a small and natural relaxation of instance optimality. Finally, our mechanism is simple enough that it can be easily implemented on top of any RDBMS and an LP solver. Experimental results show that it offers order-of-magnitude improvements in terms of utility over existing techniques, even those specifically designed for graph pattern counting.
{"title":"R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys","authors":"Wei Dong, Juanru Fang, K. Yi, Yuchao Tao, Ashwin Machanavajjhala","doi":"10.1145/3604437.3604462","DOIUrl":"https://doi.org/10.1145/3604437.3604462","url":null,"abstract":"Answering SPJA queries under differential privacy (DP), including graph pattern counting under node-DP as an important special case, has received considerable attention in recent years. The dual challenge of foreign-key constraints and self-joins is particularly tricky to deal with, and no existing DP mechanisms can correctly handle both. For the special case of graph pattern counting under node-DP, the existing mechanisms are correct (i.e., satisfy DP), but they do not offer nontrivial utility guarantees or are very complicated and costly. In this paper, we propose the first DP mechanism for answering arbitrary SPJA queries in a database with foreign-key constraints. Meanwhile, it achieves a fairly strong notion of optimality, which can be considered as a small and natural relaxation of instance optimality. Finally, our mechanism is simple enough that it can be easily implemented on top of any RDBMS and an LP solver. Experimental results show that it offers order-of-magnitude improvements in terms of utility over existing techniques, even those specifically designed for graph pattern counting.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134499446","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}
Increased use of data to inform decision making has brought with it a rising awareness of the importance of privacy, and the need for appropriate mitigations to be put in place to protect the interests of individuals whose data is being processed. From the demographic statistics that are produced by national censuses, to the complex predictive models built by "big tech" companies, data is the fuel that powers these applications. A majority of such uses rely on data that is derived from the properties and actions of individual people. This data is therefore considered sensitive, and in need of protections to prevent inappropriate use or disclosure. Some protections come from enforcing policies, access control, and contractual agreements. But in addition, we also seek technical interventions: definitions and algorithms that can be applied by computer systems in order to protect the private information while still enabling the intended use.
{"title":"Technical Perspective on 'R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys","authors":"Graham Cormode","doi":"10.1145/3604437.3604461","DOIUrl":"https://doi.org/10.1145/3604437.3604461","url":null,"abstract":"Increased use of data to inform decision making has brought with it a rising awareness of the importance of privacy, and the need for appropriate mitigations to be put in place to protect the interests of individuals whose data is being processed. From the demographic statistics that are produced by national censuses, to the complex predictive models built by \"big tech\" companies, data is the fuel that powers these applications. A majority of such uses rely on data that is derived from the properties and actions of individual people. This data is therefore considered sensitive, and in need of protections to prevent inappropriate use or disclosure. Some protections come from enforcing policies, access control, and contractual agreements. But in addition, we also seek technical interventions: definitions and algorithms that can be applied by computer systems in order to protect the private information while still enabling the intended use.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"728 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929916","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}
Mahmoud Abo Khamis, H. Ngo, R. Pichler, Dan Suciu, Y. Wang
Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this paper we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can generalize the semi-na¨ve evaluation algorithm to compute their minimal fixpoints.
{"title":"Convergence of Datalog over (Pre-) Semirings","authors":"Mahmoud Abo Khamis, H. Ngo, R. Pichler, Dan Suciu, Y. Wang","doi":"10.1145/3604437.3604454","DOIUrl":"https://doi.org/10.1145/3604437.3604454","url":null,"abstract":"Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this paper we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can generalize the semi-na¨ve evaluation algorithm to compute their minimal fixpoints.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129352182","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}
Query optimization is the process of finding an efficient query execution plan for a given SQL query. The runtime difference between a good and a bad plan can be tremendous. For example, in the case of TPC-H query 5, a query with 5 joins, the difference between the best and the worst plan is more than 10,000×. Therefore, it is vital to avoid bad plans. The dominating factor which differentiates a good from a bad plan is their join order and whether this join order avoids large intermediate results.
{"title":"Technical Perspective: Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems","authors":"Andreas Kipf","doi":"10.1145/3604437.3604459","DOIUrl":"https://doi.org/10.1145/3604437.3604459","url":null,"abstract":"Query optimization is the process of finding an efficient query execution plan for a given SQL query. The runtime difference between a good and a bad plan can be tremendous. For example, in the case of TPC-H query 5, a query with 5 joins, the difference between the best and the worst plan is more than 10,000×. Therefore, it is vital to avoid bad plans. The dominating factor which differentiates a good from a bad plan is their join order and whether this join order avoids large intermediate results.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509450","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}
Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with single edge updates updates.
{"title":"Sortledton: a Universal Graph Data Structure","authors":"Per Fuchs, D. Margan, Jana Giceva","doi":"10.1145/3604437.3604442","DOIUrl":"https://doi.org/10.1145/3604437.3604442","url":null,"abstract":"Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with single edge updates updates.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133722687","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}
Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among 8 popular open-source web applications, we find that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone-53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential to improve performance in contentious workloads by utilizing application semantics such as access patterns. Finally, implications of ad hoc transactions to the database research community are discussed.
web应用程序中的许多事务都是在应用程序代码中特别构造的。例如，开发人员可能显式地使用锁定原语或验证过程来协调关键的代码片段。我们将由应用程序代码协调的数据库操作称为临时事务。到目前为止，人们对它们知之甚少。本文首次对临时交易进行了全面研究。通过研究8个流行的开源web应用程序中的91个临时事务，我们发现(i)每个研究的应用程序都使用临时事务(每个应用程序多达16个)，其中71个起关键作用;(ii)与数据库事务相比，临时事务的并发控制要灵活得多;(iii) AD hoc交易容易出错——其中53个有正确性问题，其中33个被开发人员确认;(iv)通过利用诸如访问模式之类的应用程序语义，特设事务有可能在有争议的工作负载中提高性能。最后，讨论了特设事务对数据库研究界的影响。
{"title":"Ad Hoc Transactions: What They Are and Why We Should Care","authors":"Chuzhe Tang, Zhaoguo Wang, Xiaodong Zhang, Qianmian Yu, B. Zang, Hai-bing Guan, Haibo Chen","doi":"10.1145/3604437.3604440","DOIUrl":"https://doi.org/10.1145/3604437.3604440","url":null,"abstract":"Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among 8 popular open-source web applications, we find that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone-53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential to improve performance in contentious workloads by utilizing application semantics such as access patterns. Finally, implications of ad hoc transactions to the database research community are discussed.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124865297","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}
A. Bonifati, Stefania Dumbrava, G. Fletcher, J. Hidders, Matthias Hofer, W. Martens, Filip Murlak, Joshua Shinavier, S. Staworko, Dominik Tomaszuk
Threshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. We explore how such queries appear in practice and present a method that can be used to significantly improve the asymptotic bounds of their state-of-the-art evaluation algorithms. Our experimental evaluation of these methods shows order-of-magnitude performance improvements.
{"title":"Threshold Queries","authors":"A. Bonifati, Stefania Dumbrava, G. Fletcher, J. Hidders, Matthias Hofer, W. Martens, Filip Murlak, Joshua Shinavier, S. Staworko, Dominik Tomaszuk","doi":"10.1145/3604437.3604452","DOIUrl":"https://doi.org/10.1145/3604437.3604452","url":null,"abstract":"Threshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. We explore how such queries appear in practice and present a method that can be used to significantly improve the asymptotic bounds of their state-of-the-art evaluation algorithms. Our experimental evaluation of these methods shows order-of-magnitude performance improvements.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102337","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}
Query processing, the art of efficiently executing a relational query on a given database, is a foundational and core area in data management research. Established at the dawn of relational database systems in the 1970's, relational query processing remains a highly relevant and vibrant research topic today as recent work shows that, apart from its application in traditional database scenarios, it is also highly effective in optimizing machine learning workloads [1].
{"title":"Technical Perspective: Conjunctive Queries with Comparisons","authors":"Stijn Vansummeren","doi":"10.1145/3604437.3604449","DOIUrl":"https://doi.org/10.1145/3604437.3604449","url":null,"abstract":"Query processing, the art of efficiently executing a relational query on a given database, is a foundational and core area in data management research. Established at the dawn of relational database systems in the 1970's, relational query processing remains a highly relevant and vibrant research topic today as recent work shows that, apart from its application in traditional database scenarios, it is also highly effective in optimizing machine learning workloads [1].","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126365599","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}
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