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

Quantiles over data streams: an experimental study 数据流的分位数:一项实验研究

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465312

Lu Wang, Ge Luo, K. Yi, Graham Cormode

引用次数: 58

Generalized scale independence through incremental precomputation 基于增量预计算的广义尺度无关性

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465333

Michael Armbrust, Eric Liang, Tim Kraska, A. Fox, M. Franklin, D. Patterson

{"title":"Generalized scale independence through incremental precomputation","authors":"Michael Armbrust, Eric Liang, Tim Kraska, A. Fox, M. Franklin, D. Patterson","doi":"10.1145/2463676.2465333","DOIUrl":"https://doi.org/10.1145/2463676.2465333","url":null,"abstract":"Developers of rapidly growing applications must be able to anticipate potential scalability problems before they cause performance issues in production environments. A new type of data independence, called scale independence, seeks to address this challenge by guaranteeing a bounded amount of work is required to execute all queries in an application, independent of the size of the underlying data. While optimization strategies have been developed to provide these guarantees for the class of queries that are scale-independent when executed using simple indexes, there are important queries for which such techniques are insufficient.\u0000 Executing these more complex queries scale-independently requires precomputation using incrementally-maintained materialized views. However, since this precomputation effectively shifts some of the query processing burden from execution time to insertion time, a scale-independent system must be careful to ensure that storage and maintenance costs do not threaten scalability. In this paper, we describe a scale-independent view selection and maintenance system, which uses novel static analysis techniques that ensure that created views do not themselves become scaling bottlenecks. Finally, we present an empirical analysis that includes all the queries from the TPC-W benchmark and validates our implementation's ability to maintain nearly constant high-quantile query and update latency even as an application scales to hundreds of machines.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"195 1","pages":"625-636"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78060049","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: 41

PrivGene: differentially private model fitting using genetic algorithms PrivGene:使用遗传算法的差分私有模型拟合

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465330

Jun Zhang, Xiaokui Xiao, Y. Yang, Zhenjie Zhang, M. Winslett

{"title":"PrivGene: differentially private model fitting using genetic algorithms","authors":"Jun Zhang, Xiaokui Xiao, Y. Yang, Zhenjie Zhang, M. Winslett","doi":"10.1145/2463676.2465330","DOIUrl":"https://doi.org/10.1145/2463676.2465330","url":null,"abstract":"epsilon-differential privacy is rapidly emerging as the state-of-the-art scheme for protecting individuals' privacy in published analysis results over sensitive data. The main idea is to perform random perturbations on the analysis results, such that any individual's presence in the data has negligible impact on the randomized results. This paper focuses on analysis tasks that involve model fitting, i.e., finding the parameters of a statistical model that best fit the dataset. For such tasks, the quality of the differentially private results depends upon both the effectiveness of the model fitting algorithm, and the amount of perturbations required to satisfy the privacy guarantees. Most previous studies start from a state-of-the-art, non-private model fitting algorithm, and develop a differentially private version. Unfortunately, many model fitting algorithms require intensive perturbations to satisfy -differential privacy, leading to poor overall result quality.\u0000 Motivated by this, we propose PrivGene, a general-purpose differentially private model fitting solution based on genetic algorithms (GA). PrivGene needs significantly less perturbations than previous methods, and it achieves higher overall result quality, even for model fitting tasks where GA is not the first choice without privacy considerations. Further, PrivGene performs the random perturbations using a novel technique called the enhanced exponential mechanism, which improves over the exponential mechanism by exploiting the special properties of model fitting tasks. As case studies, we apply PrivGene to three common analysis tasks involving model fitting: logistic regression, SVM classification, and k-means clustering. Extensive experiments using real data confirm the high result quality of PrivGene, and its superiority over existing methods.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"33 1","pages":"665-676"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82028188","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: 87

STEM: a spatio-temporal miner for bursty activity STEM:突发活动的时空挖掘者

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463688

Theodoros Lappas, Marcos R. Vieira, D. Gunopulos, V. Tsotras

{"title":"STEM: a spatio-temporal miner for bursty activity","authors":"Theodoros Lappas, Marcos R. Vieira, D. Gunopulos, V. Tsotras","doi":"10.1145/2463676.2463688","DOIUrl":"https://doi.org/10.1145/2463676.2463688","url":null,"abstract":"Burst identification has been extensively studied in the context of document streams, where a burst is generally exhibited when an unusually high frequency is observed for a term t. Previous works have focused exclusively on either temporal or spatial burstiness patterns. The former represents bursty timeframes within a single stream, while the latter characterizes sets of streams that simultaneously exhibited a bursty behavior for a user-specified timeframe. Our previous work was the first to study the spatiotemporal burstiness of terms. In this context, a burstiness pattern consists of both a timeframe and a set of streams, both of which need to be identified automatically. In this paper we describe STEM (Spatio-TEmporal Miner), a system for finding spatiotemporal burstiness patterns in a collection of spatially distributed frequency streams. STEM implements the full functionality required to mine spatiotemporal burstiness patterns from virtually any collection of geostamped streams. Examples of such collections include document streams (e.g. online newspapers), geo-aware microblogging platforms (e.g. Twitter). This paper describes the STEM system and discusses how its features can be accessed via a user-friendly interface.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"40 1","pages":"1021-1024"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80608131","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: 6

Query containment in entity SQL 实体SQL中的查询包含

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2463711

Guillem Rull, P. Bernstein, I. Santos, Yannis Katsis, S. Melnik, Ernest Teniente

引用次数: 7

Query processing on smart SSDs: opportunities and challenges 智能ssd查询处理:机遇与挑战

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465295

Jaeyoung Do, Yang-Suk Kee, J. Patel, Chanik Park, Kwanghyun Park, D. DeWitt

引用次数: 225

CTrace: semantic comparison of multi-granularity process traces CTrace:多粒度过程轨迹的语义比较

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465268

Qing Liu, K. Taylor, Xiang Zhao, G. Squire, Xuemin Lin, C. Kloppers, Richard Miller

引用次数: 2

Managing database technology at enterprise scale 管理企业规模的数据库技术

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2486083

P. Yaron

{"title":"Managing database technology at enterprise scale","authors":"P. Yaron","doi":"10.1145/2463676.2486083","DOIUrl":"https://doi.org/10.1145/2463676.2486083","url":null,"abstract":"Paul Yaron is responsible for Non-Mainframe, Relational Database Architecture, Engineering and Strategy for JPMC globally. JP Morgan is a leading financial services firm with assets over $2 trillion, operates 40 major datacenters around the globe, servicing over 60 countries with over 250,000 employees. It partners with 170 regulators and manages 230 Petabytes of data, JPMC depends on over 23,000 database instances to service multiple business units. With a deployment of such scope, JPMC leverages solutions from most major database, security and operating system vendors.\u0000 This talk will discuss the challenges and strategies of managing the evolving ecosystem of \"all data\", from information security, to internal virtualization strategies. Engineering reliable globally scalable and compliant data management solutions demands a model for proactively measuring the risk complexity of an ecosystem for expert focus and potential proactive remediation. The research for quantitative measurement of database (or other) ecosystem entropy appears sparse. JPMC is looking to share its ideas in this space with the academic community as the need for such quantitative measures are increasingly important as ecosystems move from islands of single tenant risk into multi-tenant risk clusters.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"13 1","pages":"919-920"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77855682","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

Quality and efficiency for kernel density estimates in large data 大数据核密度估计的质量和效率

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465319

Yan Zheng, Jeffrey Jestes, J. M. Phillips, Feifei Li

引用次数: 62

EBM: an entropy-based model to infer social strength from spatiotemporal data 实证模型:从时空数据推断社会力量的基于熵的模型

Proceedings. ACM-SIGMOD International Conference on Management of Data Pub Date : 2013-06-22 DOI: 10.1145/2463676.2465301

Huy Pham, C. Shahabi, Yan Liu

{"title":"EBM: an entropy-based model to infer social strength from spatiotemporal data","authors":"Huy Pham, C. Shahabi, Yan Liu","doi":"10.1145/2463676.2465301","DOIUrl":"https://doi.org/10.1145/2463676.2465301","url":null,"abstract":"The ubiquity of mobile devices and the popularity of location-based-services have generated, for the first time, rich datasets of people's location information at a very high fidelity. These location datasets can be used to study people's behavior - for example, social studies have shown that people, who are seen together frequently at the same place and at the same time, are most probably socially related. In this paper, we are interested in inferring these social connections by analyzing people's location information, which is useful in a variety of application domains from sales and marketing to intelligence analysis. In particular, we propose an entropy-based model (EBM) that not only infers social connections but also estimates the strength of social connections by analyzing people's co-occurrences in space and time. We examine two independent ways: diversity and weighted frequency, through which co-occurrences contribute to social strength. In addition, we take the characteristics of each location into consideration in order to compensate for cases where only limited location information is available. We conducted extensive sets of experiments with real-world datasets including both people's location data and their social connections, where we used the latter as the ground-truth to verify the results of applying our approach to the former. We show that our approach outperforms the competitors.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"14 1","pages":"265-276"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86860599","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: 157