Most of the world’s cloud data service workloads are currently being backed by replicated state machines. Production-grade log replication protocols used for the job impose heavy data transfer duties on the primary server which need to disseminate the log commands to all the replica servers. UniCache proposes a principal solution to this problem using a learned replicated cache which enables commands to be sent over the network as compressed encodings. UniCache takes advantage of that each replica has access to a consistent prefix of the replicated log which allows them to build a uniform lookup cache used for compressing and decompressing commands consistently. UniCache achieves effective speedups, lowering the primary load in application workloads with a skewed data distribution. Our experimental studies showcase a low pre-processing overhead and the highest performance gains in cross-data center deployments over wide area networks.
{"title":"UniCache: Efficient Log Replication through Learning Workload Patterns","authors":"Harald Ng, Kun Wu, Paris Carbone","doi":"10.48786/edbt.2023.39","DOIUrl":"https://doi.org/10.48786/edbt.2023.39","url":null,"abstract":"Most of the world’s cloud data service workloads are currently being backed by replicated state machines. Production-grade log replication protocols used for the job impose heavy data transfer duties on the primary server which need to disseminate the log commands to all the replica servers. UniCache proposes a principal solution to this problem using a learned replicated cache which enables commands to be sent over the network as compressed encodings. UniCache takes advantage of that each replica has access to a consistent prefix of the replicated log which allows them to build a uniform lookup cache used for compressing and decompressing commands consistently. UniCache achieves effective speedups, lowering the primary load in application workloads with a skewed data distribution. Our experimental studies showcase a low pre-processing overhead and the highest performance gains in cross-data center deployments over wide area networks.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"30 3 1","pages":"471-477"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90875060","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}
The establishment of the AGM bound on the size of intermediate results of natural join queries has led to the development of several so-called worst-case join algorithms. These algorithms provably produce intermediate results that are (asymptotically) no larger than the final result of the join. The most notable ones are the Recursive Join , its successor, the Generic Join and the Leapfrog-Trie-Join . While algorithmically efficient, however, all of these algorithms require the availability of index structures that allow tuple lookups using the prefix of a key. Key-prefix-lookups in relational database systems are commonly supported by tree-based index structures since hash-based indices only support full-key lookups. In this paper, we study a wide variety of main-memory-oriented index structures that support key-prefix-lookups with a specific focus on supporting the Generic Join. Based on that study, we develop a novel, best-of-breed index structure called Sonic that combines the fast build and point lookup properties of hashtables with the prefix-lookups capabilities of trees and tries. To evaluate the performance of a variety of indices for worst-case optimal joins in a modern code-generating DBMS, we leveraged flexible, compile-time metaprogramming features to build a framework that creates highly efficient code, interweaving (at a microarchitectural level) a generic join implementation with any appropriate index structure. We demonstrate experimentally that in that framework, Sonic outperforms the fastest existing approaches by up to 2.5 times when supporting the Generic Join algorithm.
{"title":"SonicJoin: Fast, Robust and Worst-case Optimal","authors":"Ahmad Khazaie, H. Pirk","doi":"10.48786/edbt.2023.46","DOIUrl":"https://doi.org/10.48786/edbt.2023.46","url":null,"abstract":"The establishment of the AGM bound on the size of intermediate results of natural join queries has led to the development of several so-called worst-case join algorithms. These algorithms provably produce intermediate results that are (asymptotically) no larger than the final result of the join. The most notable ones are the Recursive Join , its successor, the Generic Join and the Leapfrog-Trie-Join . While algorithmically efficient, however, all of these algorithms require the availability of index structures that allow tuple lookups using the prefix of a key. Key-prefix-lookups in relational database systems are commonly supported by tree-based index structures since hash-based indices only support full-key lookups. In this paper, we study a wide variety of main-memory-oriented index structures that support key-prefix-lookups with a specific focus on supporting the Generic Join. Based on that study, we develop a novel, best-of-breed index structure called Sonic that combines the fast build and point lookup properties of hashtables with the prefix-lookups capabilities of trees and tries. To evaluate the performance of a variety of indices for worst-case optimal joins in a modern code-generating DBMS, we leveraged flexible, compile-time metaprogramming features to build a framework that creates highly efficient code, interweaving (at a microarchitectural level) a generic join implementation with any appropriate index structure. We demonstrate experimentally that in that framework, Sonic outperforms the fastest existing approaches by up to 2.5 times when supporting the Generic Join algorithm.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"10 1","pages":"540-551"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75143850","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}
Teodoro Baldazzi, Luigi Bellomarini, Emanuel Sallinger
{"title":"Reasoning over Financial Scenarios with the Vadalog System","authors":"Teodoro Baldazzi, Luigi Bellomarini, Emanuel Sallinger","doi":"10.48786/edbt.2023.66","DOIUrl":"https://doi.org/10.48786/edbt.2023.66","url":null,"abstract":"","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"53 1","pages":"782-791"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84585796","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}
Maja Schneider, P. Christen, E. Rahm, Jonathan Schneider, Lea Löffelmann
Trajectory data, often collected on a large scale with mobile sensors in smartphones and vehicles, are a valuable source for realiz-ing smart city applications, or for improving the user experience in mobile apps. But such data can also leak private information, such as a person’s whereabouts and their points of interest (POI). These in turn can reveal sensitive information, for example a person’s age, gender, religion, or home and work address. Location privacy preserving mechanisms (LPPM) can mitigate this issue by transforming data so that private details are protected. But privacy-preservation typically comes at the cost of a loss of utility. It can be challenging to find a suitable mechanism and the right settings to satisfy privacy as well as utility. In this work, we present Privacy Tuna, an interactive open-source framework to visualize trajectory data, and intuitively estimate data utility and privacy while applying various LPPMs. Our tool makes it easy for data owners to investigate the value of their data, choose a suitable privacy-preserving mechanism and tune its parameters to achieve a good utility-privacy trade-off.
{"title":"Tuning the Utility-Privacy Trade-Off in Trajectory Data","authors":"Maja Schneider, P. Christen, E. Rahm, Jonathan Schneider, Lea Löffelmann","doi":"10.48786/edbt.2023.78","DOIUrl":"https://doi.org/10.48786/edbt.2023.78","url":null,"abstract":"Trajectory data, often collected on a large scale with mobile sensors in smartphones and vehicles, are a valuable source for realiz-ing smart city applications, or for improving the user experience in mobile apps. But such data can also leak private information, such as a person’s whereabouts and their points of interest (POI). These in turn can reveal sensitive information, for example a person’s age, gender, religion, or home and work address. Location privacy preserving mechanisms (LPPM) can mitigate this issue by transforming data so that private details are protected. But privacy-preservation typically comes at the cost of a loss of utility. It can be challenging to find a suitable mechanism and the right settings to satisfy privacy as well as utility. In this work, we present Privacy Tuna, an interactive open-source framework to visualize trajectory data, and intuitively estimate data utility and privacy while applying various LPPMs. Our tool makes it easy for data owners to investigate the value of their data, choose a suitable privacy-preserving mechanism and tune its parameters to achieve a good utility-privacy trade-off.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"108 1","pages":"839-842"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85339441","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}
Data aggregations enable privacy-aware data analytics for moving objects. A spatiotemporal range count query is a fundamental query that aggregates the count of objects in a given spatial region and a time interval. Existing works are designed for centralized systems, which lead to issues with extensive communication and the potential for data leaks. Current in-network systems suffer from the distinct count problem (counting the same objects multiple times) and the dead space problem (excessive intra-communication from ill-suited spatial subdivisions). We propose a novel framework based on a planar graph representation for efficient privacy-aware in-network aggregate queries. Unlike conventional spatial decomposition methods, our framework uses sensor placement techniques to select sensors to reduce dead space. A submodular maximization-based method is introduced when the query distribution is known and a host of sampling methods are used when the query distribution is unknown or dynamic. We avoid double counting by tracking movements along the graph edges using discrete differential forms. We support queries with arbitrary temporal intervals with a constant-sized regression model that accelerates the query performance and reduces the storage size. We evaluate our method on real-world mobility data, which yields us a relative error of at most 13 . 8% with 25 . 6% of sensors while achieving a speedup of 3 . 5 × , 69 . 81% reduction in sensors accessed, and a storage reduction of 99 . 96% compared to finding the exact count.
{"title":"In-Network Approximate and Efficient Spatiotemporal Range Queries on Moving Objects","authors":"Guang Yang, Liang Liang","doi":"10.48786/edbt.2024.04","DOIUrl":"https://doi.org/10.48786/edbt.2024.04","url":null,"abstract":"Data aggregations enable privacy-aware data analytics for moving objects. A spatiotemporal range count query is a fundamental query that aggregates the count of objects in a given spatial region and a time interval. Existing works are designed for centralized systems, which lead to issues with extensive communication and the potential for data leaks. Current in-network systems suffer from the distinct count problem (counting the same objects multiple times) and the dead space problem (excessive intra-communication from ill-suited spatial subdivisions). We propose a novel framework based on a planar graph representation for efficient privacy-aware in-network aggregate queries. Unlike conventional spatial decomposition methods, our framework uses sensor placement techniques to select sensors to reduce dead space. A submodular maximization-based method is introduced when the query distribution is known and a host of sampling methods are used when the query distribution is unknown or dynamic. We avoid double counting by tracking movements along the graph edges using discrete differential forms. We support queries with arbitrary temporal intervals with a constant-sized regression model that accelerates the query performance and reduces the storage size. We evaluate our method on real-world mobility data, which yields us a relative error of at most 13 . 8% with 25 . 6% of sensors while achieving a speedup of 3 . 5 × , 69 . 81% reduction in sensors accessed, and a storage reduction of 99 . 96% compared to finding the exact count.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"2 1","pages":"34-46"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90721274","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}
Evangelia Tsoukanara, Georgia Koloniari, E. Pitoura
Graphs offer a generic abstraction for modeling entities as nodes and their interactions and relationships as edges. Since most graphs evolve over time, it is important to study their evolution. To this end, we propose demonstrating TempoGRAPHer, a tool that provides an overview of the evolution of an attributed graph offering aggregation at both the time and the attribute dimensions. The tool also supports a novel exploration strategy that helps in identifying time intervals of significant growth, shrinkage, or stability. Finally, we describe a scenario that showcases the usefulness of the TempoGRAPHer tool in understanding the evolution of contacts between primary school students.
{"title":"TempoGRAPHer: A Tool for Aggregating and Exploring Evolving Graphs","authors":"Evangelia Tsoukanara, Georgia Koloniari, E. Pitoura","doi":"10.48786/edbt.2023.79","DOIUrl":"https://doi.org/10.48786/edbt.2023.79","url":null,"abstract":"Graphs offer a generic abstraction for modeling entities as nodes and their interactions and relationships as edges. Since most graphs evolve over time, it is important to study their evolution. To this end, we propose demonstrating TempoGRAPHer, a tool that provides an overview of the evolution of an attributed graph offering aggregation at both the time and the attribute dimensions. The tool also supports a novel exploration strategy that helps in identifying time intervals of significant growth, shrinkage, or stability. Finally, we describe a scenario that showcases the usefulness of the TempoGRAPHer tool in understanding the evolution of contacts between primary school students.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"128 1","pages":"843-846"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88115076","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}
This paper studies the publication of multi-dimensional data with local differential privacy (LDP). This problem raises tremendous challenges in terms of both computational efficiency and data utility. The state-of-the-art solution addresses this problem by first constructing a junction tree (a kind of probabilistic graphical model, PGM) to generate a set of noisy low-dimensional marginals of the input data and then using them to approximate the distribution of the input dataset for synthetic data generation. However, there are two severe limitations in the existing solution, i.e., calculating a large number of attribute pairs’ marginals to construct the PGM and not solving well in calculating the marginal distribution of large cliques in the PGM, which degrade the quality of synthetic data. To address the above deficiencies, based on the sparseness of the constructed PGM and the divisibility of LDP, we first propose an incremental learning-based PGM construction method. In this method, we gradually prune the edges (attribute pairs) with weak correlation and allocate more data and privacy budgets to the useful edges, thereby improving the model’s accuracy. In this method, we introduce a high-precision data accumulation technique and a low-error edge pruning technique. Second, based on joint distribution decomposition and redundancy elimination, we propose a novel marginal calculation method for the large cliques in the context of LDP. Extensive experiments on real datasets demonstrate that our solution offers desirable data utility.
{"title":"Multi-Dimensional Data Publishing With Local Differential Privacy","authors":"Gaoyuan Liu, Peng Tang, Chengyu Hu, Chongshi Jin, Shanqing Guo","doi":"10.48786/edbt.2023.15","DOIUrl":"https://doi.org/10.48786/edbt.2023.15","url":null,"abstract":"This paper studies the publication of multi-dimensional data with local differential privacy (LDP). This problem raises tremendous challenges in terms of both computational efficiency and data utility. The state-of-the-art solution addresses this problem by first constructing a junction tree (a kind of probabilistic graphical model, PGM) to generate a set of noisy low-dimensional marginals of the input data and then using them to approximate the distribution of the input dataset for synthetic data generation. However, there are two severe limitations in the existing solution, i.e., calculating a large number of attribute pairs’ marginals to construct the PGM and not solving well in calculating the marginal distribution of large cliques in the PGM, which degrade the quality of synthetic data. To address the above deficiencies, based on the sparseness of the constructed PGM and the divisibility of LDP, we first propose an incremental learning-based PGM construction method. In this method, we gradually prune the edges (attribute pairs) with weak correlation and allocate more data and privacy budgets to the useful edges, thereby improving the model’s accuracy. In this method, we introduce a high-precision data accumulation technique and a low-error edge pruning technique. Second, based on joint distribution decomposition and redundancy elimination, we propose a novel marginal calculation method for the large cliques in the context of LDP. Extensive experiments on real datasets demonstrate that our solution offers desirable data utility.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"18 1","pages":"183-194"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86073013","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}
Panos Vassiliadis, Fation Shehaj, George Kalampokis, A. Zarras
In this paper, we address the problem of the co-evolution of Free Open Source Software projects with the relational schemata that they encompass. We exploit a data set of 195 publicly available schema histories of FOSS projects hosted in Github, for which we locally cloned their respective project and measured their evolution progress. Our first research question asks which percentage of the projects demonstrates a “hand-in-hand” schema and source code co-evolution? To address this question, we defined synchronicity by allowing a bounded amount of lag between the cumulative evolution of the schema and the entire project. A core finding is that there are all kinds of behaviors with respect to project and schema co-evolution, resulting in only a small number of projects where the evolution of schema and project progress in sync. Moreover, we discovered that after exceeding a 5-year threshold of project life, schemata gravitate to lower rates of evolution, which practically means that, with time, the schemata stop evolving as actively as they originally did. To answer a second question, on whether evolution comes early in the life of a schema, we measured how often does the cumulative progress of schema evolution exceed the respective progress of source change, as well as the respective progress of time. The results indicate that a large majority of schemata demonstrates early advance of schema change with respect to code evolution, and, an even larger majority is also demonstrating an advance of schema evolution with respect to time, too. Third, we asked at which time point in their lives do schemata attain a substantial
{"title":"Joint Source and Schema Evolution: Insights from a Study of 195 FOSS Projects","authors":"Panos Vassiliadis, Fation Shehaj, George Kalampokis, A. Zarras","doi":"10.48786/edbt.2023.03","DOIUrl":"https://doi.org/10.48786/edbt.2023.03","url":null,"abstract":"In this paper, we address the problem of the co-evolution of Free Open Source Software projects with the relational schemata that they encompass. We exploit a data set of 195 publicly available schema histories of FOSS projects hosted in Github, for which we locally cloned their respective project and measured their evolution progress. Our first research question asks which percentage of the projects demonstrates a “hand-in-hand” schema and source code co-evolution? To address this question, we defined synchronicity by allowing a bounded amount of lag between the cumulative evolution of the schema and the entire project. A core finding is that there are all kinds of behaviors with respect to project and schema co-evolution, resulting in only a small number of projects where the evolution of schema and project progress in sync. Moreover, we discovered that after exceeding a 5-year threshold of project life, schemata gravitate to lower rates of evolution, which practically means that, with time, the schemata stop evolving as actively as they originally did. To answer a second question, on whether evolution comes early in the life of a schema, we measured how often does the cumulative progress of schema evolution exceed the respective progress of source change, as well as the respective progress of time. The results indicate that a large majority of schemata demonstrates early advance of schema change with respect to code evolution, and, an even larger majority is also demonstrating an advance of schema evolution with respect to time, too. Third, we asked at which time point in their lives do schemata attain a substantial","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"4 1","pages":"27-39"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90532072","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}
Due to the pervasiveness of group activities in people’s daily life, group recommendation has attracted a massive research effort in both industry and academia. A fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group; this is the focus of this paper. Specifically, we introduce a dual adjustment aggregation score, which measures the relevance of an item to a group. We then propose a recommendation scheme, termed 𝑘 -dual adjustment unanimous skyline, that seeks to retrieve the 𝑘 items with the highest score, while discarding items that are unanimously considered inap-propriate. Furthermore, we design and develop algorithms for computing the 𝑘 -dual adjustment unanimous skyline efficiently. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through an extensive experimental evaluation on real datasets.
{"title":"Recommending Unanimously Preferred Items to Groups","authors":"Karim Benouaret, K. Tan","doi":"10.48786/edbt.2023.29","DOIUrl":"https://doi.org/10.48786/edbt.2023.29","url":null,"abstract":"Due to the pervasiveness of group activities in people’s daily life, group recommendation has attracted a massive research effort in both industry and academia. A fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group; this is the focus of this paper. Specifically, we introduce a dual adjustment aggregation score, which measures the relevance of an item to a group. We then propose a recommendation scheme, termed 𝑘 -dual adjustment unanimous skyline, that seeks to retrieve the 𝑘 items with the highest score, while discarding items that are unanimously considered inap-propriate. Furthermore, we design and develop algorithms for computing the 𝑘 -dual adjustment unanimous skyline efficiently. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through an extensive experimental evaluation on real datasets.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"116 1","pages":"364-377"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89386677","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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