Proceedings. ACM-SIGMOD International Conference on Management of Data最新文献_第5页

RTP: robust tenant placement for elastic in-memory database clusters RTP:用于弹性内存中数据库集群的健壮的租户安置

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465302

J. Schaffner, Tim Januschowski, Mary H. Kercher, Tim Kraska, H. Plattner, M. Franklin, D. Jacobs

引用次数: 48

Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes 不要害怕:使用具有最大可能性和有限更改的可伸缩自动修复

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463706

M. Yakout, Laure Berti-Équille, A. Elmagarmid

{"title":"Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes","authors":"M. Yakout, Laure Berti-Équille, A. Elmagarmid","doi":"10.1145/2463676.2463706","DOIUrl":"https://doi.org/10.1145/2463676.2463706","url":null,"abstract":"Various computational procedures or constraint-based methods for data repairing have been proposed over the last decades to identify errors and, when possible, correct them. However, these approaches have several limitations including the scalability and quality of the values to be used in replacement of the errors. In this paper, we propose a new data repairing approach that is based on maximizing the likelihood of replacement data given the data distribution, which can be modeled using statistical machine learning techniques. This is a novel approach combining machine learning and likelihood methods for cleaning dirty databases by value modification. We develop a quality measure of the repairing updates based on the likelihood benefit and the amount of changes applied to the database. We propose SCARE (SCalable Automatic REpairing), a systematic scalable framework that follows our approach. SCARE relies on a robust mechanism for horizontal data partitioning and a combination of machine learning techniques to predict the set of possible updates. Due to data partitioning, several updates can be predicted for a single record based on local views on each data partition. Therefore, we propose a mechanism to combine the local predictions and obtain accurate final predictions. Finally, we experimentally demonstrate the effectiveness, efficiency, and scalability of our approach on real-world datasets in comparison to recent data cleaning approaches.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"20 1","pages":"553-564"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73100375","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: 144

On optimal worst-case matching 关于最优最坏匹配

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465321

Cheng Long, R. C. Wong, Philip S. Yu, Minhao Jiang

{"title":"On optimal worst-case matching","authors":"Cheng Long, R. C. Wong, Philip S. Yu, Minhao Jiang","doi":"10.1145/2463676.2465321","DOIUrl":"https://doi.org/10.1145/2463676.2465321","url":null,"abstract":"Bichromatic reverse nearest neighbor (BRNN) queries have been studied extensively in the literature of spatial databases. Given a set P of service-providers and a set O of customers, a BRNN query is to find which customers in O are \"interested\" in a given service-provider in P. Recently, it has been found that this kind of queries lacks the consideration of the capacities of service-providers and the demands of customers. In order to address this issue, some spatial matching problems have been proposed, which, however, cannot be used for some real-life applications like emergency facility allocation where the maximum matching cost (or distance) should be minimized. In this paper, we propose a new problem called Spatial Matching for Minimizing Maximum matching distance (SPM-MM). Then, we design two algorithms for SPM-MM, Threshold-Adapt and Swap-Chain. Threshold-Adapt is simple and easy to understand but not scalable to large datasets due to its relatively high time/space complexity. Swap-Chain, which follows a fundamentally different idea from Threshold-Adapt, runs faster than Threshold-Adapt by orders of magnitude and uses significantly less memory. We conducted extensive empirical studies which verified the efficiency and scalability of Swap-Chain.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"22 1","pages":"845-856"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73900038","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: 30

Enhancements to SQL server column stores 对SQL server列存储的增强

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463708

P. Larson, C. Clinciu, Campbell Fraser, E. Hanson, Mostafa Mokhtar, Michal Nowakiewicz, Vassilis Papadimos, Susan Price, Srikumar Rangarajan, Remus Rusanu, Mayukh Saubhasik

引用次数: 67

Photon: fault-tolerant and scalable joining of continuous data streams Photon:连续数据流的容错和可扩展连接

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465272

R. Ananthanarayanan, Venkatesh Basker, Sumit Das, A. Gupta, H. Jiang, Tianhao Qiu, Alexey Reznichenko, D.Yu. Ryabkov, Manpreet Singh, S. Venkataraman

{"title":"Photon: fault-tolerant and scalable joining of continuous data streams","authors":"R. Ananthanarayanan, Venkatesh Basker, Sumit Das, A. Gupta, H. Jiang, Tianhao Qiu, Alexey Reznichenko, D.Yu. Ryabkov, Manpreet Singh, S. Venkataraman","doi":"10.1145/2463676.2465272","DOIUrl":"https://doi.org/10.1145/2463676.2465272","url":null,"abstract":"Web-based enterprises process events generated by millions of users interacting with their websites. Rich statistical data distilled from combining such interactions in near real-time generates enormous business value. In this paper, we describe the architecture of Photon, a geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency, where the streams may be unordered or delayed. The system fully tolerates infrastructure degradation and datacenter-level outages without any manual intervention. Photon guarantees that there will be no duplicates in the joined output (at-most-once semantics) at any point in time, that most joinable events will be present in the output in real-time (near-exact semantics), and exactly-once semantics eventually.\u0000 Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"132 1","pages":"577-588"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76421080","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: 146

CS2: a new database synopsis for query estimation CS2:用于查询估计的新数据库概要

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463701

Feng Yu, W. Hou, Cheng Luo, D. Che, Mengxia Zhu

引用次数: 36

Big data in capital markets 资本市场的大数据

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2486082

A. Nazaruk, M. Rauchman

引用次数: 19

CARTILAGE: adding flexibility to the Hadoop skeleton 软骨:为Hadoop骨架增加灵活性

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465258

Alekh Jindal, Jorge-Arnulfo Quiané-Ruiz, S. Madden

{"title":"CARTILAGE: adding flexibility to the Hadoop skeleton","authors":"Alekh Jindal, Jorge-Arnulfo Quiané-Ruiz, S. Madden","doi":"10.1145/2463676.2465258","DOIUrl":"https://doi.org/10.1145/2463676.2465258","url":null,"abstract":"Modern enterprises have to deal with a variety of analytical queries over very large datasets. In this respect, Hadoop has gained much popularity since it scales to thousand of nodes and terabytes of data. However, Hadoop suffers from poor performance, especially in I/O performance. Several works have proposed alternate data storage for Hadoop in order to improve the query performance. However, many of these works end up making deep changes in Hadoop or HDFS. As a result, they are (i) difficult to adopt by several users, and (ii) not compatible with future Hadoop releases. In this paper, we present CARTILAGE, a comprehensive data storage framework built on top of HDFS. CARTILAGE allows users full control over their data storage, including data partitioning, data replication, data layouts, and data placement. Furthermore, CARTILAGE can be layered on top of an existing HDFS installation. This means that Hadoop, as well as other query engines, can readily make use of CARTILAGE. We describe several use-cases of CARTILAGE and propose to demonstrate the flexibility and efficiency of CARTILAGE through a set of novel scenarios.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"85 1","pages":"1057-1060"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79703805","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: 13

A demonstration of SQLVM: performance isolation in multi-tenant relational database-as-a-service SQLVM:多租户关系数据库即服务中的性能隔离演示

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463686

Vivek R. Narasayya, Sudipto Das, M. Syamala, B. Chandramouli, S. Chaudhuri

{"title":"A demonstration of SQLVM: performance isolation in multi-tenant relational database-as-a-service","authors":"Vivek R. Narasayya, Sudipto Das, M. Syamala, B. Chandramouli, S. Chaudhuri","doi":"10.1145/2463676.2463686","DOIUrl":"https://doi.org/10.1145/2463676.2463686","url":null,"abstract":"Sharing resources of a single database server among multiple tenants is common in multi-tenant Database-as-a-Service providers, such as Microsoft SQL Azure. Multi-tenancy enables cost reduction for the cloud service provider which it can pass on as savings to the tenants. However, resource sharing can adversely affect a tenant's performance due to other tenants' workloads contending for shared resources. Service providers today do not provide any assurances to a tenant in terms of isolating its performance from other co-located tenants. SQLVM, a project at Microsoft Research, is an abstraction for performance isolation which is built on a promise of reserving key database server resources, such as CPU, I/O and memory, for each tenant. The key challenge is in supporting this abstraction within a RDBMS without statically allocating resources to tenants, while ensuring low overheads and scaling to large numbers of tenants. This demonstration will show how SQLVM can effectively isolate a tenant's performance from other tenant workloads co-located at the same database server. Our demonstration will use various scripted scenarios and a data collection and visualization framework to illustrate performance isolation using SQLVM.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"12 1","pages":"1077-1080"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89322704","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: 74

PARAS: interactive parameter space exploration for association rule mining PARAS:关联规则挖掘的交互式参数空间探索

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465245

Abhishek Mukherji, Xika Lin, Christopher R. Botaish, Jason Whitehouse, Elke A. Rundensteiner, M. Ward, Carolina Ruiz

引用次数: 7