19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)最新文献_第4页

Duplicate Elimination in Space-partitioning Tree Indexes 空间分区树索引中的重复消除

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.10

M. Eltabakh, M. Ouzzani, Walid G. Aref

{"title":"Duplicate Elimination in Space-partitioning Tree Indexes","authors":"M. Eltabakh, M. Ouzzani, Walid G. Aref","doi":"10.1109/SSDBM.2007.10","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.10","url":null,"abstract":"Space-partitioning trees, like the disk-based trie, quadtree, kd-tree and their variants, are a family of access methods that index multi-dimensional objects. In the case of indexing non-zero extent objects, e.g., line segments and rectangles, space-partitioning trees may replicate objects over multiple space partitions, e.g., PMR quadtree, expanded MX-CIF quadtree, and extended kd-tree. As a result, the answer to a query over these indexes may include duplicates that need to be eliminated, i.e., the same object may be reported more than once. In this paper, we propose generic duplicate elimination techniques for the class of space-partitioning trees in the context of SP-GiST; an extensible indexing framework for realizing space-partitioning trees. The proposed techniques are embedded inside the INDEX-SCAN operator. Therefore, duplicate copies of the same object do not propagate in the query plan, and the elimination process is transparent to the end-users. Two cases for the index structures are considered based on whether or not the objects' coordinates are stored inside the index tree. The theoretical and experimental analysis illustrate that the proposed techniques achieve savings in the storage requirements, I/O operations, and processing time when compared to adding a separate duplicate elimination operator in the query plan.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"468 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":"134061852","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: 1

Some Challenges in Integrating Information on Protein Interactions and a Partial Solution 蛋白质相互作用信息整合的若干挑战及部分解决方案

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.23

H. Jagadish

{"title":"Some Challenges in Integrating Information on Protein Interactions and a Partial Solution","authors":"H. Jagadish","doi":"10.1109/SSDBM.2007.23","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.23","url":null,"abstract":"Summary form only given. Independently constructed sources of (scientific) data frequently have overlapping, and sometimes contradictory, information content. Current methods of use fall into two categories: force the integration step onto the user, or merely collate the data, at most transforming it into a common format. The first method places an undue burden on the user to fit all of the jigsaw puzzle pieces together. The second leads to redundancy and possible inconsistency. We propose a third: deep data integration. The idea is to provide a cohesive view of all information currently available for a protein, interaction, or other object of scientific interest. Doing so requires that multiple pieces of data about the object, in different sources, first be identified as referring to the same object, if required through \"third party\" information; then that a single \"record\" be created comprising the union of the information in multiple matched records, keeping track of differences where these occur; and finally by tracking the provenance of every value in the dataset so scientists can judge what items to use, and how to resolve differences.In this talk, the I will describe our experiences with this approach in MiMI (http://mimi.ncibi.org). I will also discuss barriers to domain scientist use of the system, and my thoughts regarding how to make systems truly \"usable\".","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"33 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":"127868934","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

Component-based Data Layout for Efficient Slicing of Very Large Multidimensional Volumetric Data 基于组件的数据布局用于超大多维体积数据的高效切片

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.7

Jusub Kim, J. JáJá

引用次数: 1

Boosting k-Nearest Neighbor Queries Estimating Suitable Query Radii 提高k近邻查询估计合适的查询半径

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.5

Marcos R. Vieira, C. Traina, A. Traina, Adriano S. Arantes, C. Faloutsos

引用次数: 10

An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays 一种从异构微阵列中挖掘相干模式的有效算法

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.30

Xiang Zhang, Wei Wang

{"title":"An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays","authors":"Xiang Zhang, Wei Wang","doi":"10.1109/SSDBM.2007.30","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.30","url":null,"abstract":"DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"200 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":"115016610","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: 2