{"title":"An extensible index for spatial databases","authors":"Walid G. Aref, I. Ilyas","doi":"10.1109/SSDM.2001.938537","DOIUrl":"https://doi.org/10.1109/SSDM.2001.938537","url":null,"abstract":"Emerging database applications require the use of new indexing structures beyond B-trees and R-trees. Examples are the k-D tree, the trie, the quadtree, and their variants. They are often proposed as supporting structures in data mining, GIS, and CAD/CAM applications. A common feature of all these indexes is that they recursively divide the spare into partitions. A novel extensible index structure, termed SP-GiST, is presented that supports this class of data structure, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Issues related to clustering tree nodes into pages as well as concurrency control for SP-GiST are addressed. A dynamic minimum-height clustering technique is applied to minimize disk accesses and to make using such trees in database systems possible and efficient. A prototype implementation of SP-GiST is presented as well as performance studies of the various SP-GiST's tuning parameters.","PeriodicalId":129323,"journal":{"name":"Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122427076","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}
Ranga Raju Vatsavai, T. Burk, S. Shekhar, M. Hansen
{"title":"An efficient query strategy for integrated remote sensing and inventory (spatial) databases","authors":"Ranga Raju Vatsavai, T. Burk, S. Shekhar, M. Hansen","doi":"10.1109/SSDM.2001.938544","DOIUrl":"https://doi.org/10.1109/SSDM.2001.938544","url":null,"abstract":"The integration of disparate heterogeneous spatial databases for extending queries is a challenging task. The authors present a novel framework, based on a k-nearest neighbor (kNN) algorithm, for integrating remote sensing imagery with Forest Inventory Analysis (FIA) sample point/plot data managed in a relational database system. We then demonstrate how queries to this system may be extended over any arbitrary region of interest in a Web based geographical information system. To build the integrated database, spectral signatures are collected at FIA plot locations from the Landsat TM image. A plot-id image is produced by assigning each pixel to the closest FIA plot in multi-dimensional spectral space. The resulting image provides an interface to the Forest Inventory Analysis Data-Base (FIADB) and allows generalizations of the estimates for any user defined query window or region of interest (ROI). This methodology, along with geostatistical analysis, is integrated into a client/server Web based geographical information system, which provides Internet users with an easy to use query interface for the FIADB and spatial databases.","PeriodicalId":129323,"journal":{"name":"Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131951739","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}
{"title":"A feasible method to find areas with constraints using hierarchical depth-first clustering","authors":"Kwang-Su Yang, Ruixin Yang, M. Kafatos","doi":"10.1109/SSDM.2001.938559","DOIUrl":"https://doi.org/10.1109/SSDM.2001.938559","url":null,"abstract":"Addresses a reliable, feasible method to find geographical areas with constraints using hierarchical depth-first clustering. The method involves multi-level hierarchical clustering with a depth-first strategy, depending on whether the area of each cluster satisfies the given constraints. The attributes used in the hierarchical clustering are the coordinates of the grid data points. The constraints are an average value range and the minimum size of an area with a small proportion of missing data points. Convex-hull and point-in-polygon algorithms are involved in examining the constraint satisfaction. The method is implemented for an Earth science data set for vegetation studies - the Normalized Difference Vegetation Index (NVDI).","PeriodicalId":129323,"journal":{"name":"Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001","volume":"48 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132851459","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}
{"title":"Two approaches to representing multiple overlapping classifications: a comparison [plant taxonomy]","authors":"C. Raguenaud, Martin Graham, J. Kennedy","doi":"10.1109/SSDM.2001.938556","DOIUrl":"https://doi.org/10.1109/SSDM.2001.938556","url":null,"abstract":"One of the tasks of plant taxonomy is the creation of classifications of organisms that allows the understanding of the evolutionary relationships between them. In this paper, we describe two different data models that have been designed to support two aspects of taxonomic work: the storage of the information and the visualisation of that information. We show that these two models are different because of their constraints and aims, and we compare their abilities using a number of typical tasks that users perform. We also show that, although different and able to perform different tasks, each of these models is well adapted to its purpose, and tight integration is difficult.","PeriodicalId":129323,"journal":{"name":"Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122757793","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}