Proceedings of the 2006 ACM SIGMOD international conference on Management of data最新文献_第8页

OMCAT: optimal maintenance of continuous queries' answers for trajectories OMCAT:对轨迹的连续查询的答案进行最佳维护

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142575

Hui Ding, Goce Trajcevski, P. Scheuermann

引用次数: 8

Contour map matching for event detection in sensor networks 面向传感器网络事件检测的等高线图匹配

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142491

Wenwei Xue, Qiong Luo, Lei Chen, Yunhao Liu

引用次数: 213

Design, implementation, and evaluation of the linear road bnchmark on the stream processing core 流处理核心上线性道路基准的设计、实现和评估

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142522

Navendu Jain, Lisa Amini, H. Andrade, Richard P. King, Yoonho Park, P. Selo, C. Venkatramani

{"title":"Design, implementation, and evaluation of the linear road bnchmark on the stream processing core","authors":"Navendu Jain, Lisa Amini, H. Andrade, Richard P. King, Yoonho Park, P. Selo, C. Venkatramani","doi":"10.1145/1142473.1142522","DOIUrl":"https://doi.org/10.1145/1142473.1142522","url":null,"abstract":"Stream processing applications have recently gained significant attention in the networking and database community. At the core of these applications is a stream processing engine that performs resource allocation and management to support continuous tracking of queries over collections of physically-distributed and rapidly-updating data streams. While numerous stream processing systems exist, there has been little work on understanding the performance characteristics of these applications in a distributed setup. In this paper, we examine the performance bottlenecks of streaming data applications, in particular the Linear Road stream data management benchmark, in achieving good performance in large-scale distributed environments, using the Stream Processing Core (SPC), a stream processing middleware we have developed. First, we present the design and implementation of the Linear Road benchmark on the SPC middleware. SPC has been designed to scale to tens of thousands of processing nodes, while supporting concurrent applications and multiple simultaneous queries. Second, we identify the main performance bottlenecks in the Linear Road application in achieving scalability and low query response latency. Our results show that data locality, buffer capacity, physical allocation of processing elements to infrastructure nodes, and packaging for transporting streamed data are important factors in achieving good application performance. Though we evaluate our system primarily for the Linear Road application, we believe it also provides useful insights into the overall system behavior for supporting other distributed and large-scale continuous streaming data applications. Finally, we examine how SPC can be used and tuned to enable a very efficient implementation of the Linear Road application in a distributed environment.","PeriodicalId":416090,"journal":{"name":"Proceedings of the 2006 ACM SIGMOD international conference on Management of data","volume":"474 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132318458","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: 219

Automatic client-server partitioning of data-driven web applications 数据驱动的web应用程序的自动客户机-服务器分区

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142580

Nicholas Gerner, Fan Yang, A. Demers, J. Gehrke, Mirek Riedewald, J. Shanmugasundaram

{"title":"Automatic client-server partitioning of data-driven web applications","authors":"Nicholas Gerner, Fan Yang, A. Demers, J. Gehrke, Mirek Riedewald, J. Shanmugasundaram","doi":"10.1145/1142473.1142580","DOIUrl":"https://doi.org/10.1145/1142473.1142580","url":null,"abstract":"Current application development tools provide completely different programming models for the application server (e.g., Java and J2EE) and the client web browser (e.g., JavaScript and HTML). Consequently, the application developer is forced to partition the application code between the server and client at the time of writing the application. However, the partitioning of the code between the client and server may have to be changed during the evolution of the application for performance reasons (it may be better to push more functionality to the client), for correctness reasons (data that conflicts with multiple clients cannot always be pushed to clients), and for supporting clients with different computing power (browsers on desktops vs. PDAs). Since the client and server use different programming models, moving application code from client to server (and vice versa) reduces programmer productivity and also has the potential to introduce concurrency bugs. In this demonstration, we advocate an alternative solution to this problem: we propose developing applications using a unified declarative high-level language called Hilda, and show how a Hilda compiler can automatically (and correctly) partition Hilda code between the client and the server using a real Course Management System application. We illustrate our techniques using two clients: a powerful laptop machine and a less powerful PDA.","PeriodicalId":416090,"journal":{"name":"Proceedings of the 2006 ACM SIGMOD international conference on Management of data","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154356","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

Optimal multi-scale patterns in time series streams 时间序列流的最优多尺度模式

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142545

S. Papadimitriou, Philip S. Yu

引用次数: 121

Record linkage: similarity measures and algorithms 记录链接:相似性度量和算法

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142599

Nick Koudas, Sunita Sarawagi, D. Srivastava

引用次数: 346

Data delivery in a service-oriented world: the BEA aquaLogic data services platform 面向服务世界中的数据交付:BEA aquaLogic数据服务平台

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142551

M. Carey

引用次数: 84

Continuous query processing in data streams using duality of data and queries 在数据流中使用数据和查询的对偶性进行连续查询处理

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142509

Hyo-Sang Lim, Jae-Gil Lee, Min-Jae Lee, K. Whang, I. Song

{"title":"Continuous query processing in data streams using duality of data and queries","authors":"Hyo-Sang Lim, Jae-Gil Lee, Min-Jae Lee, K. Whang, I. Song","doi":"10.1145/1142473.1142509","DOIUrl":"https://doi.org/10.1145/1142473.1142509","url":null,"abstract":"Recent data stream systems such as TelegraphCQ have employed the well-known property of duality between data and queries. In these systems, query processing methods are classified into two dual categories -- data-initiative and query-initiative -- depending on whether query processing is initiated by selecting a data element or a query. Although the duality property has been widely recognized, previous data stream systems do not fully take advantages of this property since they use the two dual methods independently: data-initiative methods only for continuous queries and query-initiative methods only for ad-hoc queries. We contend that continuous query processing can be better optimized by adopting an approach that integrates the two dual methods. Our primary contribution is based on the observation that spatial join is a powerful tool for achieving this objective. In this paper, we first present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We then present a continuous query processing algorithm based on spatial join, which we name Spatial Join CQ. This algorithm processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries, both defined as regions in the multi-dimensional space. The algorithm achieves the advantages of the two dual methods simultaneously. Experimental results show that the proposed algorithm outperforms earlier algorithms by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join queries.","PeriodicalId":416090,"journal":{"name":"Proceedings of the 2006 ACM SIGMOD international conference on Management of data","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128323633","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: 53

MauveDB: supporting model-based user views in database systems MauveDB:支持数据库系统中基于模型的用户视图

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142483

A. Deshpande, S. Madden

{"title":"MauveDB: supporting model-based user views in database systems","authors":"A. Deshpande, S. Madden","doi":"10.1145/1142473.1142483","DOIUrl":"https://doi.org/10.1145/1142473.1142483","url":null,"abstract":"Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.","PeriodicalId":416090,"journal":{"name":"Proceedings of the 2006 ACM SIGMOD international conference on Management of data","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127140288","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: 236

Testing database applications 测试数据库应用程序

Proceedings of the 2006 ACM SIGMOD international conference on Management of data Pub Date : 2006-06-27 DOI: 10.1145/1142473.1142572

Carsten Binnig, Donald Kossmann, Eric Lo

引用次数: 26