Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.
{"title":"Modern Recommender Systems: from Computing Matrices to Thinking with Neurons","authors":"G. Koutrika","doi":"10.1145/3183713.3197389","DOIUrl":"https://doi.org/10.1145/3183713.3197389","url":null,"abstract":"Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75172031","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}
{"title":"Session details: Research 1: Data Integration & Cleaning","authors":"E. Rahm","doi":"10.1145/3258004","DOIUrl":"https://doi.org/10.1145/3258004","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75288817","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}
As a proof-of-concept for the vision “SSD as SQL Engine” (SaS in short), we demonstrate that SQLite [4], a popular mobile database engine, in its entirety can run inside a real SSD development platform. By turning storage device into database engine, SaS allows applications to directly interact with full SQL database server running inside storage device. In SaS, the SQL language itself, not the traditional dummy block interface, will be provided as new interface between applications and storage device. In addition, since SaS plays the role of the uni ed platform of database computing node and storage node, the host and the storage need not be segregated any more as separate physical computing components.
{"title":"SSD as SQLite Engine","authors":"Soyee Choi","doi":"10.1145/3183713.3183720","DOIUrl":"https://doi.org/10.1145/3183713.3183720","url":null,"abstract":"As a proof-of-concept for the vision “SSD as SQL Engine” (SaS in short), we demonstrate that SQLite [4], a popular mobile database engine, in its entirety can run inside a real SSD development platform. By turning storage device into database engine, SaS allows applications to directly interact with full SQL database server running inside storage device. In SaS, the SQL language itself, not the traditional dummy block interface, will be provided as new interface between applications and storage device. In addition, since SaS plays the role of the uni ed platform of database computing node and storage node, the host and the storage need not be segregated any more as separate physical computing components.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73576387","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}
Xinan Yan, Linguan Yang, Hongbo Zhang, X. Lin, B. Wong, K. Salem, Tim Brecht
The trend towards global applications and services has created an increasing demand for transaction processing on globally-distributed data. Many database systems, such as Spanner and CockroachDB, support distributed transactions but require a large number of wide-area network roundtrips to commit each transaction and ensure the transaction's state is durably replicated across multiple datacenters. This can significantly increase transaction completion time, resulting in developers replacing database-level transactions with their own error-prone application-level solutions. This paper introduces Carousel, a distributed database system that provides low-latency transaction processing for multi-partition globally-distributed transactions. Carousel shortens transaction processing time by reducing the number of sequential wide-area network round trips required to commit a transaction and replicate its results while maintaining serializability. This is possible in part by using information about a transaction's potential write set to enable transaction processing, including any necessary remote read operations, to overlap with 2PC and state replication. Carousel further reduces transaction completion time by introducing a consensus protocol that can perform state replication in parallel with 2PC. For a multi-partition 2-round Fixed-set Interactive (2FI) transaction, Carousel requires at most two wide-area network roundtrips to commit the transaction when there are no failures, and only one round trip in the common case if local replicas are available.
{"title":"Carousel: Low-Latency Transaction Processing for Globally-Distributed Data","authors":"Xinan Yan, Linguan Yang, Hongbo Zhang, X. Lin, B. Wong, K. Salem, Tim Brecht","doi":"10.1145/3183713.3196912","DOIUrl":"https://doi.org/10.1145/3183713.3196912","url":null,"abstract":"The trend towards global applications and services has created an increasing demand for transaction processing on globally-distributed data. Many database systems, such as Spanner and CockroachDB, support distributed transactions but require a large number of wide-area network roundtrips to commit each transaction and ensure the transaction's state is durably replicated across multiple datacenters. This can significantly increase transaction completion time, resulting in developers replacing database-level transactions with their own error-prone application-level solutions. This paper introduces Carousel, a distributed database system that provides low-latency transaction processing for multi-partition globally-distributed transactions. Carousel shortens transaction processing time by reducing the number of sequential wide-area network round trips required to commit a transaction and replicate its results while maintaining serializability. This is possible in part by using information about a transaction's potential write set to enable transaction processing, including any necessary remote read operations, to overlap with 2PC and state replication. Carousel further reduces transaction completion time by introducing a consensus protocol that can perform state replication in parallel with 2PC. For a multi-partition 2-round Fixed-set Interactive (2FI) transaction, Carousel requires at most two wide-area network roundtrips to commit the transaction when there are no failures, and only one round trip in the common case if local replicas are available.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84234232","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}
Alexandre Verbitski, Anurag Gupta, D. Saha, James Corey, K. Gupta, Murali Brahmadesam, Raman Mittal, S. Krishnamurthy, Sandor Maurice, T. Kharatishvili, Xiaofeng Bao
Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.
Amazon Aurora是作为Amazon Web Services (AWS)的一部分提供的高吞吐量云原生关系数据库。Aurora与其他关系型数据库之间的一个比较新颖的区别是，它如何将重做处理推送到一个多租户的横向扩展存储服务上，这是专门为Aurora构建的。这样做可以减少网络流量，避免检查点和崩溃恢复，支持故障转移到副本而不会丢失数据，并支持容错存储，无需数据库参与即可进行修复。利用分布式存储的传统实现将使用分布式共识算法进行提交、读取、复制和成员变更，并增加底层存储的成本。在本文中，我们描述了Aurora如何在大多数情况下通过建立不变量和利用局部瞬态来避免分布式共识。这样做可以提高性能、减少可变性并降低成本。
{"title":"Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes","authors":"Alexandre Verbitski, Anurag Gupta, D. Saha, James Corey, K. Gupta, Murali Brahmadesam, Raman Mittal, S. Krishnamurthy, Sandor Maurice, T. Kharatishvili, Xiaofeng Bao","doi":"10.1145/3183713.3196937","DOIUrl":"https://doi.org/10.1145/3183713.3196937","url":null,"abstract":"Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85726682","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}
{"title":"Session details: Research 7: Tuning, Monitoring & Query Optimization","authors":"Sudipto Das","doi":"10.1145/3258013","DOIUrl":"https://doi.org/10.1145/3258013","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84587873","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}
Xiaoyu Ge, Panos K. Chrysanthis, K. Pelechrinis, D. Zeinalipour-Yazti
Recent studies in urban navigation have revealed new demands (e.g., diversity, safety, happiness, serendipity) for the navigation services that are critical to providing useful recommendations to travelers. This exposes the need to design next-generation navigation services that accommodate these newly emerging aspects. In this paper, we present a prototype system, namely, EPUI (an Experimental Platform of Urban Informatics), which provides a testbed for exploring and evaluating venues and route recommendation solutions that balance between different objectives (i.e., demands) including the newly discovered ones. In addition, EPUI incorporates a modularized design, enabling researchers to upload their own algorithms and compare them to well-known algorithms using different performance metrics. Its user interface makes it easily usable by both end-user and experienced researchers.
{"title":"EPUI: Experimental Platform for Urban Informatics","authors":"Xiaoyu Ge, Panos K. Chrysanthis, K. Pelechrinis, D. Zeinalipour-Yazti","doi":"10.1145/3183713.3193560","DOIUrl":"https://doi.org/10.1145/3183713.3193560","url":null,"abstract":"Recent studies in urban navigation have revealed new demands (e.g., diversity, safety, happiness, serendipity) for the navigation services that are critical to providing useful recommendations to travelers. This exposes the need to design next-generation navigation services that accommodate these newly emerging aspects. In this paper, we present a prototype system, namely, EPUI (an Experimental Platform of Urban Informatics), which provides a testbed for exploring and evaluating venues and route recommendation solutions that balance between different objectives (i.e., demands) including the newly discovered ones. In addition, EPUI incorporates a modularized design, enabling researchers to upload their own algorithms and compare them to well-known algorithms using different performance metrics. Its user interface makes it easily usable by both end-user and experienced researchers.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81479454","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}
Recent expansion of database technology demands a convenient framework for non-expert users to explore datasets. Several approaches exist to assist these non-expert users where they can express their query intent by providing example tuples for their intended query output. However, these approaches treat the structural similarity among the example tuples as the only factor specifying query intent and ignore the richer context present in the data. In this demo, we present SQuID, a system for Semantic similarity-aware Query Intent Discovery. SQuID takes a few example tuples from the user as input, through a simple interface, and consults the database to discover deeper associations among these examples. These data-driven associations reveal the semantic context of the provided examples, allowing SQuID to infer the user's intended query precisely and effectively. SQuID further explains its inference, by displaying the discovered semantic context to the user, who can then provide feedback and tune the result. We demonstrate how SQuID can capture even esoteric and complex semantic contexts, alleviating the need for constructing complex SQL queries, while not requiring the user to have any schema or query language knowledge.
{"title":"SQuID: Semantic Similarity-Aware Query Intent Discovery","authors":"Anna Fariha, Sheikh Muhammad Sarwar, A. Meliou","doi":"10.1145/3183713.3193548","DOIUrl":"https://doi.org/10.1145/3183713.3193548","url":null,"abstract":"Recent expansion of database technology demands a convenient framework for non-expert users to explore datasets. Several approaches exist to assist these non-expert users where they can express their query intent by providing example tuples for their intended query output. However, these approaches treat the structural similarity among the example tuples as the only factor specifying query intent and ignore the richer context present in the data. In this demo, we present SQuID, a system for Semantic similarity-aware Query Intent Discovery. SQuID takes a few example tuples from the user as input, through a simple interface, and consults the database to discover deeper associations among these examples. These data-driven associations reveal the semantic context of the provided examples, allowing SQuID to infer the user's intended query precisely and effectively. SQuID further explains its inference, by displaying the discovered semantic context to the user, who can then provide feedback and tune the result. We demonstrate how SQuID can capture even esoteric and complex semantic contexts, alleviating the need for constructing complex SQL queries, while not requiring the user to have any schema or query language knowledge.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78954987","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}
We present Maverick, a general, extensible framework that discovers exceptional facts about entities in knowledge graphs. To the best of our knowledge, there was no previous study of the problem. We model an exceptional fact about an entity of interest as a context-subspace pair, in which a subspace is a set of attributes and a context is defined by a graph query pattern of which the entity is a match. The entity is exceptional among the entities in the context, with regard to the subspace. The search spaces of both patterns and subspaces are exponentially large. Maverick conducts beam search on the patterns which uses a match-based pattern construction method to evade the evaluation of invalid patterns. It applies two heuristics to select promising patterns to form the beam in each iteration. Maverick traverses and prunes the subspaces organized as a set enumeration tree by exploiting the upper bound properties of exceptionality scoring functions. Results of experiments and user studies using real-world datasets demonstrated substantial performance improvement of the proposed framework over the baselines as well as its effectiveness in discovering exceptional facts.
{"title":"Maverick: Discovering Exceptional Facts from Knowledge Graphs","authors":"Gensheng Zhang, Damian Jimenez, Chengkai Li","doi":"10.1145/3183713.3183730","DOIUrl":"https://doi.org/10.1145/3183713.3183730","url":null,"abstract":"We present Maverick, a general, extensible framework that discovers exceptional facts about entities in knowledge graphs. To the best of our knowledge, there was no previous study of the problem. We model an exceptional fact about an entity of interest as a context-subspace pair, in which a subspace is a set of attributes and a context is defined by a graph query pattern of which the entity is a match. The entity is exceptional among the entities in the context, with regard to the subspace. The search spaces of both patterns and subspaces are exponentially large. Maverick conducts beam search on the patterns which uses a match-based pattern construction method to evade the evaluation of invalid patterns. It applies two heuristics to select promising patterns to form the beam in each iteration. Maverick traverses and prunes the subspaces organized as a set enumeration tree by exploiting the upper bound properties of exceptionality scoring functions. Results of experiments and user studies using real-world datasets demonstrated substantial performance improvement of the proposed framework over the baselines as well as its effectiveness in discovering exceptional facts.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76390576","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}
Zhuoyue Zhao, Robert Christensen, Feifei Li, Xiao Hu, K. Yi
Joins are expensive, especially on large data and/or multiple relations. One promising approach in mitigating their high costs is to just return a simple random sample of the full join results, which is sufficient for many tasks. Indeed, in as early as 1999, Chaudhuri et al. posed the problem of sampling over joins as a fundamental challenge in large database systems. They also pointed out a fundamental barrier for this problem, that the sampling operator cannot be pushed through a join, i.e., sample( R bowtie S )≠ sample( R ) bowtie sample( S ). To overcome this barrier, they used precomputed statistics to guide the sampling process, but only showed how this works for two-relation joins. This paper revisits this classic problem for both acyclic and cyclic multi-way joins. We build upon the idea of Chaudhuri et al., but extend it in several nontrivial directions. First, we propose a general framework for random sampling over multi-way joins, which includes the algorithm of Chaudhuri et al. as a special case. Second, we explore several ways to instantiate this framework, depending on what prior information is available about the underlying data, and offer different tradeoffs between sample generation latency and throughput. We analyze the properties of different instantiations and evaluate them against the baseline methods; the results clearly demonstrate the superiority of our new techniques.
{"title":"Random Sampling over Joins Revisited","authors":"Zhuoyue Zhao, Robert Christensen, Feifei Li, Xiao Hu, K. Yi","doi":"10.1145/3183713.3183739","DOIUrl":"https://doi.org/10.1145/3183713.3183739","url":null,"abstract":"Joins are expensive, especially on large data and/or multiple relations. One promising approach in mitigating their high costs is to just return a simple random sample of the full join results, which is sufficient for many tasks. Indeed, in as early as 1999, Chaudhuri et al. posed the problem of sampling over joins as a fundamental challenge in large database systems. They also pointed out a fundamental barrier for this problem, that the sampling operator cannot be pushed through a join, i.e., sample( R bowtie S )≠ sample( R ) bowtie sample( S ). To overcome this barrier, they used precomputed statistics to guide the sampling process, but only showed how this works for two-relation joins. This paper revisits this classic problem for both acyclic and cyclic multi-way joins. We build upon the idea of Chaudhuri et al., but extend it in several nontrivial directions. First, we propose a general framework for random sampling over multi-way joins, which includes the algorithm of Chaudhuri et al. as a special case. Second, we explore several ways to instantiate this framework, depending on what prior information is available about the underlying data, and offer different tradeoffs between sample generation latency and throughput. We analyze the properties of different instantiations and evaluate them against the baseline methods; the results clearly demonstrate the superiority of our new techniques.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87632320","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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