19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)最新文献

Adaptive-Size Reservoir Sampling over Data Streams 数据流上自适应大小的储层采样

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.29

Mohammed Al-Kateb, B. Lee, X. Wang

{"title":"Adaptive-Size Reservoir Sampling over Data Streams","authors":"Mohammed Al-Kateb, B. Lee, X. Wang","doi":"10.1109/SSDBM.2007.29","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.29","url":null,"abstract":"Reservoir sampling is a well-known technique for sequential random sampling over data streams. Conventional reservoir sampling assumes a fixed-size reservoir. There are situations, however, in which it is necessary and/or advantageous to adaptively adjust the size of a reservoir in the middle of sampling due to changes in data characteristics and/or application behavior. This paper studies adaptive size reservoir sampling over data streams considering two main factors: reservoir size and sample uniformity. First, the paper conducts a theoretical study on the effects of adjusting the size of a reservoir while sampling is in progress. The theoretical results show that such an adjustment may bring a negative impact on the probability of the sample being uniform (called uniformity confidence herein). Second, the paper presents a novel algorithm for maintaining the reservoir sample after the reservoir size is adjusted such that the resulting uniformity confidence exceeds a given threshold. Third, the paper extends the proposed algorithm to an adaptive multi-reservoir sampling algorithm for a practical application in which samples are collected from memory-limited wireless sensor networks using a mobile sink. Finally, the paper empirically examines the adaptivity of the multi-reservoir sampling algorithm with regard to reservoir size and sample uniformity using real sensor networks data sets.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116907025","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: 57

A Distributed Algorithm for Joins in Sensor Networks 传感器网络中的分布式连接算法

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.26

Alexandru Coman, M. Nascimento

{"title":"A Distributed Algorithm for Joins in Sensor Networks","authors":"Alexandru Coman, M. Nascimento","doi":"10.1109/SSDBM.2007.26","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.26","url":null,"abstract":"Given their autonomy, flexibility and large range of functionality, wireless sensor networks can be used as an effective and discrete means for monitoring data in many domains. Typical sensor nodes are very constrained, in particular regarding their energy and memory resources. Thus, any query processing solution over these devices should consider their limitations. We investigate the problem of processing join queries within a sensor network. Due to the limited memory at nodes, joins are typically processed in a distributed manner over a set of nodes. Previous approaches have either assumed that the join processing nodes have sufficient memory to buffer the subset of the join relations assigned to them, or that the amount of available memory at nodes is known in advance. These assumptions are not realistic for most scenarios. In this context we propose and investigate DIJ, a distributed algorithm for join processing that considers the memory limitations at nodes and does not make a priori assumptions on the available memory at the processing nodes. At the same time, our algorithm still aims at minimizing the energy cost of query processing.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570212","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: 22

Reliable Hierarchical Data Storage in Sensor Networks 传感器网络中可靠的分层数据存储

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.39

Song Lin, Benjamin Arai, D. Gunopulos

{"title":"Reliable Hierarchical Data Storage in Sensor Networks","authors":"Song Lin, Benjamin Arai, D. Gunopulos","doi":"10.1109/SSDBM.2007.39","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.39","url":null,"abstract":"The ability to provide reliable in-network storage while balancing the energy consumption of individual sensors is a primary concern when deploying a sensor network. The main concern with data-centric storage in sensor networks is the ability to provide reliable and load balanced storage. Energy and wireless range constraints make centralized approaches for storage impractical, and in-network data-centric solutions can be used to reduce the number of messages sent over the network. However, these solutions quickly become expensive when combined with fault- tolerance, load balancing and routing. In this paper, we present a novel data-centric storage and query routing mechanism for sensor networks. The routing mechanism is constructed upon the neighborhood information of individual sensors and is completely independent of geographical information. Our data resilient algorithm is capable of recovering from multiple simultaneous failures in the network while adaptively adjusting the load distribution of the newly generated sensor data. Comprehensive experiments on both real-world and synthetic data sets indicate that our approach is more effective and efficient than the previously proposed solutions.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121207853","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: 10

Information-Aware 2^n-Tree for Efficient Out-of-Core Indexing of Very Large Multidimensional Volumetric Data 基于信息感知的2^n-树的超大多维体积数据的高效外核索引

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.15

Jusub Kim, J. JáJá

引用次数: 3

MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries MAMCost:全局和局部估计导致相似查询的鲁棒成本估计

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.17

Gisele Busichia Baioco, A. Traina, C. Traina

引用次数: 17

Cost-based Optimization of Complex Scientific Queries 基于成本的复杂科学查询优化

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.8

R. Fomkin, T. Risch

引用次数: 4

Update Conscious Bitmap Indices 更新有意识位图索引

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.24

G. Canahuate, Michael Gibas, H. Ferhatosmanoğlu

{"title":"Update Conscious Bitmap Indices","authors":"G. Canahuate, Michael Gibas, H. Ferhatosmanoğlu","doi":"10.1109/SSDBM.2007.24","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.24","url":null,"abstract":"Bitmap indices have been widely used in several domains such as data warehousing and scientific applications due to their efficiency in answering certain query types over large data sets. However, their utilization has been largely limited to read-only data sets or to static snapshots of data due to the cost associated with the update and append of new data. Typically, several bitmaps are associated with each indexed attribute in a table, i.e. one for each attribute value, bin, or range. Each one of these bitmaps needs to be updated to reflect a new, appended row. Since a given table could be represented by hundreds or even thousands of bitmaps, the insertion of a single record can be prohibitively costly. In order to transfer the fast query response times offered by bitmap indices to dynamic database domains, we propose an update conscious bitmap index that provides a mechanism to quickly update bitmaps to reflect dynamic database changes. For an insert operation only the bitmaps that represent the values being inserted need to be updated. We formalize the insert and delete operations of the proposed technique and provide a cost model for bitmap updates. We compare the update conscious bitmaps to traditional bitmaps in terms of storage space, update performance, and query execution time.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"147 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128836061","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: 21

Gene Ontology-Based Annotation Analysis and Categorization of Metabolic Pathways 基于基因本体论的代谢途径注释分析与分类

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.35

A. Cakmak

引用次数: 9

Mining RNA Tertiary Motifs with Structure Graphs 利用结构图挖掘RNA三级基序

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.38

Xueyi Wang, Jun Huan, J. Snoeyink, Wei Wang

引用次数: 8

Maintaining K-Anonymity against Incremental Updates 维护k -匿名对抗增量更新

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.16

J. Pei, Jian Xu, Zhibin Wang, Wei Wang, Ke Wang

引用次数: 99