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}
The formulation of structured queries in knowledge graphs is a challenging task that presupposes familiarity with the syntax of the query language and the contents of the knowledge graph. To alleviate this difficulty in this paper we introduce RDF-ANALYTICS , a novel system that enables plain users to formulate analytic queries over complex, i.e. not necessarily star-schema based, RDF knowledge graphs. To come up with an intuitive interface, we leverage the familiarity of users with Faceted Search (FS) systems, i.e. we extend FS with actions that enable users to formulate analytic queries, too. Distinctive characteristics of the approach is the ability to include arbitrarily long paths in the analytic query (accompanied with count information), interactive formulation of HAVING restrictions, the support of both Faceted Search (i.e. the locating of the desired resources in a faceted search manner) and analytic queries, and the ability to formulate nested analytic queries. Finally, we present the results of a preliminary task-based evaluation with users, which are very promising.
{"title":"RDF-Analytics: Interactive Analytics over RDF Knowledge Graphs","authors":"Maria-Evangelia Papadaki, Yannis Tzitzikas","doi":"10.48786/edbt.2023.70","DOIUrl":"https://doi.org/10.48786/edbt.2023.70","url":null,"abstract":"The formulation of structured queries in knowledge graphs is a challenging task that presupposes familiarity with the syntax of the query language and the contents of the knowledge graph. To alleviate this difficulty in this paper we introduce RDF-ANALYTICS , a novel system that enables plain users to formulate analytic queries over complex, i.e. not necessarily star-schema based, RDF knowledge graphs. To come up with an intuitive interface, we leverage the familiarity of users with Faceted Search (FS) systems, i.e. we extend FS with actions that enable users to formulate analytic queries, too. Distinctive characteristics of the approach is the ability to include arbitrarily long paths in the analytic query (accompanied with count information), interactive formulation of HAVING restrictions, the support of both Faceted Search (i.e. the locating of the desired resources in a faceted search manner) and analytic queries, and the ability to formulate nested analytic queries. Finally, we present the results of a preliminary task-based evaluation with users, which are very promising.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"323 1","pages":"807-810"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76296786","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}
Graph databases are increasingly receiving attention from industry and academia, due in part to their flexibility; a schema is often not required. However, schemas can significantly benefit query optimization, data integrity, and documentation. There currently does not exist a formal framework which captures the design space of state-of-the-art schema solutions. We present a formal design framework for property graph schema languages based on first-order logic rules, which balances expressivity and practicality. We show how this framework can be adapted to integrate a core set of constraints common in conceptual data modeling methods. To demonstrate practical feasibility, this model is imple-mented using graph queries for modern graph database systems, which we evaluate through a controlled experiment. We find that validation time scales linearly with the size of the data, while only using unoptimized straightforward implementations.
{"title":"A Formal Design Framework for Practical Property Graph Schema Languages","authors":"Nimo Beeren, G. Fletcher","doi":"10.48786/edbt.2023.40","DOIUrl":"https://doi.org/10.48786/edbt.2023.40","url":null,"abstract":"Graph databases are increasingly receiving attention from industry and academia, due in part to their flexibility; a schema is often not required. However, schemas can significantly benefit query optimization, data integrity, and documentation. There currently does not exist a formal framework which captures the design space of state-of-the-art schema solutions. We present a formal design framework for property graph schema languages based on first-order logic rules, which balances expressivity and practicality. We show how this framework can be adapted to integrate a core set of constraints common in conceptual data modeling methods. To demonstrate practical feasibility, this model is imple-mented using graph queries for modern graph database systems, which we evaluate through a controlled experiment. We find that validation time scales linearly with the size of the data, while only using unoptimized straightforward implementations.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"19 1","pages":"478-484"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81536390","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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