Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data最新文献_第9页

High-Dimensional Vector Similarity Search: From Time Series to Deep Network Embeddings 高维向量相似搜索:从时间序列到深度网络嵌入

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3384402

Karima Echihabi

引用次数: 11

Beyond Analytics: The Evolution of Stream Processing Systems 超越分析:流处理系统的演变

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3383131

Paris Carbone, Marios Fragkoulis, Vasiliki Kalavri, Asterios Katsifodimos

引用次数: 38

vChain: A Blockchain System Ensuring Query Integrity vChain:确保查询完整性的区块链系统

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3384682

Haixin Wang, Cheng Xu, Ce Zhang, Jianliang Xu

引用次数: 8

Data Series Progressive Similarity Search with Probabilistic Quality Guarantees 具有概率质量保证的数据序列渐进式相似性搜索

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3389751

Anna Gogolou, Theophanis Tsandilas, Karima Echihabi, A. Bezerianos, Themis Palpanas

引用次数: 28

Lethe: A Tunable Delete-Aware LSM Engine Lethe:一个可调的删除感知LSM引擎

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3389757

Subhadeep Sarkar, Tarikul Islam Papon, Dimitris Staratzis, Manos Athanassoulis

{"title":"Lethe: A Tunable Delete-Aware LSM Engine","authors":"Subhadeep Sarkar, Tarikul Islam Papon, Dimitris Staratzis, Manos Athanassoulis","doi":"10.1145/3318464.3389757","DOIUrl":"https://doi.org/10.1145/3318464.3389757","url":null,"abstract":"Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17-1.4x) and lower space amplification (2.1-9.8x), with a modest increase in write amplification (between 4% and 25%). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.","PeriodicalId":436122,"journal":{"name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130757551","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}

引用次数: 38

Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines Rhino:流处理引擎的超大分布式状态的有效管理

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3389723

Bonaventura Del Monte, Steffen Zeuch, T. Rabl, V. Markl

引用次数: 39

SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data SpeakQL:面向结构化数据的语音驱动多模式查询

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3389777

Vraj Shah, Side Li, Arun Kumar, L. Saul

{"title":"SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data","authors":"Vraj Shah, Side Li, Arun Kumar, L. Saul","doi":"10.1145/3318464.3389777","DOIUrl":"https://doi.org/10.1145/3318464.3389777","url":null,"abstract":"Speech-driven querying is becoming popular in new device environments such as smartphones, tablets, and even conversational assistants. However, such querying is largely restricted to natural language. Typed SQL remains the gold standard for sophisticated structured querying although it is painful in many environments, which restricts when and how users consume their data. In this work, we propose to bridge this gap by designing a speech-driven querying system and interface for structured data we call SpeakQL. We support a practically useful subset of regular SQL and allow users to query in any domain with novel touch/speech based human-in-the-loop correction mechanisms. Automatic speech recognition (ASR) introduces myriad forms of errors in transcriptions, presenting us with a technical challenge. We exploit our observations of SQL's properties, its grammar, and the queried database to build a modular architecture. We present the first dataset of spoken SQL queries and a generic approach to generate them for any arbitrary schema. Our experiments show that SpeakQL can automatically correct a large fraction of errors in ASR transcriptions. User studies show that SpeakQL can help users specify SQL queries significantly faster with a speedup of average 2.7x and up to 6.7x compared to typing on a tablet device. SpeakQL also reduces the user effort in specifying queries by a factor of average 10x and up to 60x compared to raw typing effort.","PeriodicalId":436122,"journal":{"name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130246078","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}

引用次数: 10

MC3: A System for Minimization of Classifier Construction Cost MC3:分类器建造成本最小化系统

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3384690

Shay Gershtein, T. Milo, Gefen Morami, Slava Novgorodov

{"title":"MC3: A System for Minimization of Classifier Construction Cost","authors":"Shay Gershtein, T. Milo, Gefen Morami, Slava Novgorodov","doi":"10.1145/3318464.3384690","DOIUrl":"https://doi.org/10.1145/3318464.3384690","url":null,"abstract":"Search mechanisms over massive sets of items are the cornerstone of many modern applications, particularly in e-commerce websites. Consumers express in search queries a set of properties, and expect the system to retrieve qualifying items. A common difficulty, however, is that the information on whether or not an item satisfies the search criteria is sometimes not explicitly recorded in the repository. Instead, it may be considered as general knowledge or \"hidden\" in a picture/description, thereby leading to incomplete search results. To overcome these problems companies invest in building dedicated classifiers that determine whether an item satisfies the given search criteria. However, building classifiers typically incurs non-trivial costs due to the required volumes of high-quality labeled training data. In this demo, we introduce MC3, a real-time system that helps data analysts decide which classifiers to construct to minimize the costs of answering a set of search queries. MC3 is interactive and facilitates real-time analysis, by providing detailed classifiers impact information. We demonstrate the effectiveness of MC3 on real-world data and scenarios taken from a large e-commerce system, by interacting with the SIGMOD'20 audience members who act as analysts.","PeriodicalId":436122,"journal":{"name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128253270","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}

引用次数: 1

CHASSIS: Conformity Meets Online Information Diffusion 底盘:一致性满足在线信息扩散

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3389780

Hui Li, Hui Li, S. Bhowmick

引用次数: 9

Towards Scalable UDTFs in Noria 在Noria实现可伸缩udtf

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI: 10.1145/3318464.3384412

Justus Adam

引用次数: 0