19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)最新文献_第3页

Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems 在地理信息检索（GIR）系统中处理空间关键词（SK）查询

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.22

Ramaswamy Hariharan, B. Hore, Chen Li, S. Mehrotra

{"title":"Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems","authors":"Ramaswamy Hariharan, B. Hore, Chen Li, S. Mehrotra","doi":"10.1109/SSDBM.2007.22","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.22","url":null,"abstract":"Location-based information contained in publicly available GIS databases is invaluable for many applications such as disaster response, national infrastructure protection, crime analysis, and numerous others. The information entities of such databases have both spatial and textual descriptions. Likewise, queries issued to the databases also contain spatial and textual components, for example, \"Find shelters with emergency medical facilities in Orange County,\" or \"Find earthquake-prone zones in Southern California.\" We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has been generated in efficient processing of SK queries for a variety of applications from Web-search to GIS decision support systems. We refer to systems built for enabling such applications as Geographic Information Retrieval (GIR) Systems. An example GIR system that we address in this paper is a search engine built on top of hundreds of thousands of publicly available GIS databases. Building a search engine over such large repositories is a challenge. One of the key aspects of such a search engine is the performance. In this paper, we propose a framework for GIR systems and focus on indexing strategies that can process SK queries efficiently. We show through experiments that our indexing strategies lead to significant improvement in efficiency of answering SK queries over existing techniques.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"15 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":"127628594","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: 286

Database Support for Weighted Match Joins 数据库对加权匹配连接的支持

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.31

A. Kini, J. Naughton

引用次数: 2

Reservoir Sampling over Memory-Limited Stream Joins 在内存有限的流连接上进行储层采样

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.40

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

{"title":"Reservoir Sampling over Memory-Limited Stream Joins","authors":"Mohammed Al-Kateb, B. Lee, X. Wang","doi":"10.1109/SSDBM.2007.40","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.40","url":null,"abstract":"In stream join processing with limited memory, uniform random sampling is useful for approximate query evaluation. In this paper, we address the problem of reservoir sampling over memory-limited stream joins. We present two sampling algorithms, reservoir join-sampling (RJS) and progressive reservoir join-sampling (PRJS). RJS is designed straightforwardly by using a fixed-size reservoir sampling on a join-sample (i.e., random sample of a join output stream). Anytime the sample in the reservoir is used, RJS always gives a uniform random sample of the original join output stream. With limited memory, however, the available memory may not be large enough even for the join buffer, thereby severely limiting the reservoir size. PRJS alleviates this problem by increasing the reservoir size during the join-sampling. This increasing is possible since the memory requirement by the join-sampling algorithm decreases over time. A larger reservoir provides a closer representation of the original join output stream. However, it comes with a negative impact on the probability of the sample being uniform. Through experiments we examine the tradeoffs and compare the two algorithms in terms of the aggregation error on the reservoir sample.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"55 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":"134116795","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: 8

Efficient Evaluation of Inbreeding Queries on Pedigree Data 系谱数据近交查询的高效评估

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.12

Brendan Elliott, Suleyman Fatih Akgul, Stephen Mayes, Z. M. Özsoyoglu

引用次数: 9

A Fast Algorithm for Approximate Quantiles in High Speed Data Streams 高速数据流中近似分位数的快速算法

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.27

Qi Zhang, Wei Wang

引用次数: 40

Enabling Real-Time Querying of Live and Historical Stream Data 支持实时查询实时流数据和历史流数据

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.34

Frederick Reiss, Kurt Stockinger, Kesheng Wu, A. Shoshani, J. Hellerstein

{"title":"Enabling Real-Time Querying of Live and Historical Stream Data","authors":"Frederick Reiss, Kurt Stockinger, Kesheng Wu, A. Shoshani, J. Hellerstein","doi":"10.1109/SSDBM.2007.34","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.34","url":null,"abstract":"Applications that query data streams in order to identify trends, patterns, or anomalies can often benefit from comparing the live stream data with archived historical stream data. However, searching this historical data in real time has been considered so far to be prohibitively expensive. One of the main bottlenecks is the update costs of the indices over the archived data. In this paper, we address this problem by using our highly-efficient bitmap indexing technology (called FastBit) and demonstrate that the index update operations are sufficiently efficient for this bottleneck to be removed. We describe our prototype system based on the TelegraphCQ streaming query processor and the FastBit bitmap index. We present a detailed performance evaluation of our system using a complex query workload for analyzing real network traffic data. The combined system uses TelegraphCQ to analyze streams of traffic information and FastBit to correlate current behaviors with historical trends. We demonstrate that our system can simultaneously analyze (1) live streams with high data rates and (2) a large repository of historical stream data.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"7 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":"121340763","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: 76

CSR+-tree: Cache-conscious Indexing for High-dimensional Similarity Search CSR+-tree:高维相似度搜索的缓存敏感索引

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.9

Junfeng Dong, Xiaohui Yu

引用次数: 5

iSEE: Efficient Continuous K-Nearest-Neighbor Monitoring over Moving Objects iSEE:移动对象的高效连续k近邻监测

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.37

Wei Wu, K. Tan

引用次数: 16

What Constitutes a Scientific Database? 什么构成科学数据库?

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.25

J. Pfaltz

引用次数: 4

Sensor Scheduling for Aggregate Monitoring inWireless Sensor Networks 无线传感器网络中聚合监控的传感器调度

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.42

Xingbo Yu, S. Mehrotra, N. Venkatasubramanian

{"title":"Sensor Scheduling for Aggregate Monitoring inWireless Sensor Networks","authors":"Xingbo Yu, S. Mehrotra, N. Venkatasubramanian","doi":"10.1109/SSDBM.2007.42","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.42","url":null,"abstract":"Most of the applications of wireless sensor networks involve primarily data collection with in-network processing in which continuous aggregate queries are posed and processed. There are two principle concerns with this type of applications. First, due to the use of batteries, limited power resource has been identified as a major challenge in deploying wireless sensor networks. Second, data is usually expected to be gathered as soon as possible to facilitate the monitoring of and the response to the physical phenomena. In this paper, we tackle these challenges through sensor state scheduling. The proposed technique is based on the observation that there are two types of traffic in sensor networks designed for data aggregation, bottom-up and top-down within an abstract tree structure. We show that it is possible to achieve deterministic schedules for data aggregation with very good performance. Specifically, we develop greedy algorithms to schedule transmission and listening operations for each sensor node to achieve collision- free communication. We show that the schedules can maximize the time sensor nodes spent on low-power states which helps achieve great energy efficiency, as well as allow fast data aggregation.","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":"125949664","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: 20