SIGMOD Rec.最新文献_第6页

Ron Fagin Speaks Out on His Trajectory as a Database Theoretician 罗恩·费金讲述了他作为数据库理论家的轨迹

SIGMOD Rec. Pub Date : 2017-10-31 DOI: 10.1145/3156655.3156663

M. Winslett, V. Braganholo

引用次数: 0

Uninterruptible Migration of Continuous Queries without Operator State Migration 无操作符状态迁移的连续查询的不间断迁移

SIGMOD Rec. Pub Date : 2017-10-31 DOI: 10.1145/3156655.3156659

Thao N. Pham, Nikos R. Katsipoulakis, Panos K. Chrysanthis, Alexandros Labrinidis

引用次数: 8

The Dresden Database Systems Group 德累斯顿数据库系统集团

SIGMOD Rec. Pub Date : 2017-10-31 DOI: 10.1145/3156655.3156665

Wolfgang Lehner

引用次数: 0

Semantic Optimization in Tractable Classes of Conjunctive Queries 可处理类连接查询的语义优化

SIGMOD Rec. Pub Date : 2017-09-01 DOI: 10.1145/3137586.3137588

P. Barceló, Andreas Pieris, M. Romero

引用次数: 10

Beng Chin Ooi Speaks Out on Building a Strong Database Group 黄明珍谈建立强大的资料库集团

SIGMOD Rec. Pub Date : 2017-09-01 DOI: 10.1145/3137586.3137594

M. Winslett, V. Braganholo

引用次数: 0

A Survey of Traditional and MapReduceBased Spatial Query Processing Approaches 传统和基于mapreduce的空间查询处理方法综述

SIGMOD Rec. Pub Date : 2017-09-01 DOI: 10.1145/3137586.3137590

Hari Singh, S. Bawa

{"title":"A Survey of Traditional and MapReduceBased Spatial Query Processing Approaches","authors":"Hari Singh, S. Bawa","doi":"10.1145/3137586.3137590","DOIUrl":"https://doi.org/10.1145/3137586.3137590","url":null,"abstract":"Various indexing methods of spatial data have come out after rigorous efforts put by many researchers for fast processing of spatial queries. Parallelizing spatial index building and query processing have become very popular for improving efficiency. The MapReduce framework provides a modern way of parallel processing. A MapReduce-based works for spatial queries consider the existing traditional spatial indexing for building spatial indexes in parallel. The majority of the spatial indexes implemented in MapReduce use R-Tree and its variants. Therefore, R-Tree and its variantbased traditional spatial indexes are thoroughly surveyed in the paper. The objective is to search for still less explored spatial indexing approaches, having the potential for parallelism in MapReduce. The review work also provides a detailed survey of MapReduce-based spatial query processing approaches - hierarchical indexed and packed key-value storage based spatial dataset. Both approaches use different data partitioning strategies for distributing data among cluster nodes and managing the partitioned dataset through different indexing. Finally, a number of parameters are selected for comparison and analysis of all the existing approaches in the literature.","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"3 1","pages":"18-29"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77465679","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: 18

Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases 阿基米德:基于概率知识库的高效查询处理

SIGMOD Rec. Pub Date : 2017-09-01 DOI: 10.1145/3137586.3137592

Yang Chen, Xiaofeng Zhou, Kun Li, Daisy Zhe Wang

引用次数: 4

Report from the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR'16) 第三届MapReduce及Beyond算法与系统研讨会报告(BeyondMR’16)

SIGMOD Rec. Pub Date : 2017-09-01 DOI: 10.1145/3137586.3137596

F. Afrati, J. Hidders, C. Ré, J. Sroka, J. Ullman

引用次数: 1

Technical Perspective: Optimized Wandering for Online Aggregation 技术视角:在线聚合优化漫游

SIGMOD Rec. Pub Date : 2017-05-12 DOI: 10.1145/3093754.3093762

J. Naughton

{"title":"Technical Perspective: Optimized Wandering for Online Aggregation","authors":"J. Naughton","doi":"10.1145/3093754.3093762","DOIUrl":"https://doi.org/10.1145/3093754.3093762","url":null,"abstract":"There is a rich history in the DBMS research literature involving sampling to estimate the results of queries faster than they can be computed exactly. A particularly interesting example of this is “Online Aggregation” proposed by Hellerstein et al. in 1997 [2]. There the idea is to combine sampling with a creative and intuitive user interface. Briefly, when a query starts to run, Online Aggregation will quickly present an estimate of the result of the query (based on data sampled up to that point) and will also present a confidence interval around the estimate. As query execution continues, the estimate is refined, and the confidence interval shrinks. Hidden in this attractive idea, however, are some di cult challenges. As an example, for queries that involve joins, the sampling process is in general slow, especially if most of the tuples from one relation participating in the join “match” with only a few tuples in the other relation. For 20 years the state of the art approach to this problem has been the “Ripple Join” [1]. The following paper by Li, Wu, Yi, and Zhao presents a highly e↵ective alternative. The main idea behind the wander join is to use the presence of indexes to speed the sampling, e↵ectively making a random walk through the data join graph. The details of doing this e ciently (both computationally and statistically) are not obvious. The authors of this paper use a clever combination of sampling strategies from the statistical literature and an on-line optimization process to order the paths chosen for the random walk, in the process achieving much better computational and statistical properties than the previously state of the art algorithm. The authors convincingly prove this through experimentation with an open-source implementation in the Postgres database management system.","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"17 1","pages":"32"},"PeriodicalIF":0.0,"publicationDate":"2017-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84967150","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: 0

Scaling Machine Learning via Compressed Linear Algebra 通过压缩线性代数缩放机器学习

SIGMOD Rec. Pub Date : 2017-05-12 DOI: 10.1145/3093754.3093765

Ahmed Elgohary, Matthias Boehm, P. Haas, Frederick Reiss, B. Reinwald

{"title":"Scaling Machine Learning via Compressed Linear Algebra","authors":"Ahmed Elgohary, Matthias Boehm, P. Haas, Frederick Reiss, B. Reinwald","doi":"10.1145/3093754.3093765","DOIUrl":"https://doi.org/10.1145/3093754.3093765","url":null,"abstract":"Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only data access and I/Obound matrix-vector multiplications to converge to an optimal model. It is crucial for performance to fit the data into single-node or distributed main memory and enable very fast matrix-vector operations on in-memory data. Generalpurpose, heavy- and lightweight compression techniques struggle to achieve both good compression ratios and fast decompression speed to enable block-wise uncompressed operations. Compressed linear algebra (CLA) avoids these problems by applying lightweight lossless database compression techniques to matrices and then executing linear algebra operations such as matrix-vector multiplication directly on the compressed representations. The key ingredients are effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Experiments on an initial implementation in SystemML show in-memory operations performance close to the uncompressed case and good compression ratios.We thereby obtain significant end-to-end performance improvements up to 26x or reduced memory requirements.","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"1 1","pages":"42-49"},"PeriodicalIF":0.0,"publicationDate":"2017-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87036495","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: 9