22nd International Conference on Data Engineering (ICDE'06)最新文献_第9页

Faster In-Network Evaluation of Spatial Aggregationin Sensor Networks 传感器网络空间聚合的快速网络评价

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.70

Dina Q. Goldin

引用次数: 19

Technique for Optimal Adaptation of Time-Dependent Workflows with Security Constraints 具有安全约束的时间相关工作流的最优自适应技术

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.156

Basit Shafiq, Arjmand Samuel, E. Bertino, A. Ghafoor

引用次数: 0

Scalable Exploration of Physical Database Design 物理数据库设计的可扩展探索

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.133

A. König, Shubha U. Nabar

引用次数: 13

Taming Compliance with Sarbanes-Oxley Internal Controls Using Database Technology 使用数据库技术驯服遵守萨班斯-奥克斯利内部控制

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.155

R. Agrawal, Christopher M. Johnson, J. Kiernan, F. Leymann

引用次数: 90

Super-Scalar RAM-CPU Cache Compression 超标量RAM-CPU缓存压缩

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.150

M. Zukowski, S. Héman, N. Nes, P. Boncz

引用次数: 517

Clean Answers over Dirty Databases: A Probabilistic Approach 干净的答案胜于肮脏的数据库:一种概率方法

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.35

Periklis Andritsos, A. Fuxman, Renée J. Miller

{"title":"Clean Answers over Dirty Databases: A Probabilistic Approach","authors":"Periklis Andritsos, A. Fuxman, Renée J. Miller","doi":"10.1109/ICDE.2006.35","DOIUrl":"https://doi.org/10.1109/ICDE.2006.35","url":null,"abstract":"The detection of duplicate tuples, corresponding to the same real-world entity, is an important task in data integration and cleaning. While many techniques exist to identify such tuples, the merging or elimination of duplicates can be a difficult task that relies on ad-hoc and often manual solutions. We propose a complementary approach that permits declarative query answering over duplicated data, where each duplicate is associated with a probability of being in the clean database. We rewrite queries over a database containing duplicates to return each answer with the probability that the answer is in the clean database. Our rewritten queries are sensitive to the semantics of duplication and help a user understand which query answers are most likely to be present in the clean database. The semantics that we adopt is independent of the way the probabilities are produced, but is able to effectively exploit them during query answering. In the absence of external knowledge that associates each database tuple with a probability, we offer a technique, based on tuple summaries, that automates this task. We experimentally study the performance of our rewritten queries. Our studies show that the rewriting does not introduce a significant overhead in query execution time. This work is done in the context of the ConQuer project at the University of Toronto, which focuses on the efficient management of inconsistent and dirty databases.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82904403","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: 202

Approximating Aggregation Queries in Peer-to-Peer Networks 对等网络中近似聚合查询

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.23

Benjamin Arai, Gautam Das, D. Gunopulos, V. Kalogeraki

{"title":"Approximating Aggregation Queries in Peer-to-Peer Networks","authors":"Benjamin Arai, Gautam Das, D. Gunopulos, V. Kalogeraki","doi":"10.1109/ICDE.2006.23","DOIUrl":"https://doi.org/10.1109/ICDE.2006.23","url":null,"abstract":"Peer-to-peer databases are becoming prevalent on the Internet for distribution and sharing of documents, applications, and other digital media. The problem of answering large scale, ad-hoc analysis queries ― e.g., aggregation queries ― on these databases poses unique challenges. Exact solutions can be time consuming and difficult to implement given the distributed and dynamic nature of peer-to-peer databases. In this paper we present novel sampling-based techniques for approximate answering of ad-hoc aggregation queries in such databases. Computing a high-quality random sample of the database efficiently in the P2P environment is complicated due to several factors ― the data is distributed (usually in uneven quantities) across many peers, within each peer the data is often highly correlated, and moreover, even collecting a random sample of the peers is difficult to accomplish. To counter these problems, we have developed an adaptive two-phase sampling approach, based on random walks of the P2P graph as well as block-level sampling techniques. We present extensive experimental evaluations to demonstrate the feasibility of our proposed solutio","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82910368","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: 47

Space-efficient Relative Error Order Sketch over Data Streams 数据流上的空间效率相对错误顺序草图

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.145

Ying Zhang, Xuemin Lin, Jian Xu, Flip Korn, Wei Wang

{"title":"Space-efficient Relative Error Order Sketch over Data Streams","authors":"Ying Zhang, Xuemin Lin, Jian Xu, Flip Korn, Wei Wang","doi":"10.1109/ICDE.2006.145","DOIUrl":"https://doi.org/10.1109/ICDE.2006.145","url":null,"abstract":"We consider the problem of continuously maintaining order sketches over data streams with a relative rank error guarantee ∊. Novel space-efficient and one-scan randomised techniques are developed. Our first randomised algorithm can guarantee such a relative error precision ∊ with confidence 1 - delta using O( 1_ in frac{1} {2}2 log 1d log ∊^2N) space, where N is the number of data elements seen so far in a data stream. Then, a new one-scan space compression technique is developed. Combined with the first randomised algorithm, the one-scan space compression technique yields another one-scan randomised algorithm that guarantees the space requirement is O( 1frac{1} { in } log(1frac{1}{ in } log 1begin{gathered} frac{1}{delta } hfill hfill end{gathered} )frac{{log ^{2 + alpha } in N}} {{1 - 1/2^alpha }} (foralpha gt 0) on average while the worst case space remains O( frac{1}{{ in ^2 }}log frac{1} {delta }log in ^2 N). These results are immediately applicable to approximately computing quantiles over data streams with a relative error guarantee in and significantly improve the previous best space bound O( frac{1} {{ in ^3 }}log frac{1}{delta }log N). Our extensive experiment results demonstrate that both techniques can support an on-line computation against high speed data streams.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83005437","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: 26

Compiled Query Execution Engine using JVM 使用JVM的编译查询执行引擎

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.40

Jun Rao, H. Pirahesh, C. Mohan, G. Lohman

{"title":"Compiled Query Execution Engine using JVM","authors":"Jun Rao, H. Pirahesh, C. Mohan, G. Lohman","doi":"10.1109/ICDE.2006.40","DOIUrl":"https://doi.org/10.1109/ICDE.2006.40","url":null,"abstract":"A conventional query execution engine in a database system essentially uses a SQL virtual machine (SVM) to interpret a dataflow tree in which each node is associated with a relational operator. During query evaluation, a single tuple at a time is processed and passed among the operators. Such a model is popular because of its efficiency for pipelined processing. However, since each operator is implemented statically, it has to be very generic in order to deal with all possible queries. Such generality tends to introduce significant runtime inefficiency, especially in the context of memory-resident systems, because the granularity of data commercial system, using SVM. processing (a tuple) is too small compared with the associated overhead. Another disadvantage in such an engine is that each operator code is compiled statically, so query-specific optimization cannot be applied. To improve runtime efficiency, we propose a compiled execution engine, which, for a given query, generates new query-specific code on the fly, and then dynamically compiles and executes the code. The Java platform makes our approach particularly interesting for several reasons: (1) modern Java Virtual Machines (JVM) have Just- In-Time (JIT) compilers that optimize code at runtime based on the execution pattern, a key feature that SVMs lack; (2) because of Java’s continued popularity, JVMs keep improving at a faster pace than SVMs, allowing us to exploit new advances in the Java runtime in the future; (3) Java is a dynamic language, which makes it convenient to load a piece of new code on the fly. In this paper, we develop both an interpreted and a compiled query execution engine in a relational, Java-based, in-memory database prototype, and perform an experimental study. Our experimental results on the TPC-H data set show that, despite both engines benefiting from JIT, the compiled engine runs on average about twice as fast as the interpreted one, and significantly faster than an in-memory","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83224777","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: 72

Updates Through Views: A New Hope 透过视野更新:新的希望

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.167

Y. Kotidis, D. Srivastava, Yannis Velegrakis

引用次数: 27