Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320030
G. Koutrika, Y. Ioannidis
As information becomes available in increasing amounts to a wide spectrum of users, the need for a shift towards a more user-centered information access paradigm arises. We develop a personalization framework for database systems based on user profiles and identify the basic architectural modules required to support it. We define a preference model that assigns to each atomic query condition a personal degree of interest and provide a mechanism to compute the degree of interest in any complex query condition based on the degrees of interest in the constituent atomic ones. Preferences are stored in profiles. At query time, personalization proceeds in two steps: (a) preference selection and (b) preference integration into the original user query. We formulate the main personalization step, i.e. preference selection, as a graph computation problem and provide an efficient algorithm for it. We also discuss results of experimentation with a prototype query personalization system.
{"title":"Personalization of queries in database systems","authors":"G. Koutrika, Y. Ioannidis","doi":"10.1109/ICDE.2004.1320030","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320030","url":null,"abstract":"As information becomes available in increasing amounts to a wide spectrum of users, the need for a shift towards a more user-centered information access paradigm arises. We develop a personalization framework for database systems based on user profiles and identify the basic architectural modules required to support it. We define a preference model that assigns to each atomic query condition a personal degree of interest and provide a mechanism to compute the degree of interest in any complex query condition based on the degrees of interest in the constituent atomic ones. Preferences are stored in profiles. At query time, personalization proceeds in two steps: (a) preference selection and (b) preference integration into the original user query. We formulate the main personalization step, i.e. preference selection, as a graph computation problem and provide an efficient algorithm for it. We also discuss results of experimentation with a prototype query personalization system.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129915537","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320100
D. Florescu, Donald Kossmann
XQuery is starting to gain significant traction as a language for querying and transforming XML data. It is used in a variety of different products. Examples to date include XML database systems, XML document repositories, XML data integation, workflow systems, and publish and subscribe systems. In addition, XPath of which XQuery is a superset is used in various products such as Web browsers. Although the W3C XQuery specification has not yet attained Recommendation status, and the definition of the language has not entirely stabilized, a number of alternative proposals to implement and optimize XQuery have appeared both in industry and in the research community. Given the wide range of applications for which XQuery is applicable, a wide spectrum of alternative techniques have been proposed for XQuery processing. Some of these techniques are only useful for certain applications, other techniques are general-purpose. The goal of this seminar is to give an overview of the existing approaches to process XQuery expressions and to give details of the most important techniques. The presenters have experience from designing and building an industrial-strength XQuery engine [1]. The seminar will give details of that XQuery engine, but the seminar will also give extensive coverage of other XQuery engines and of the state of the art in the research community. The agenda for the seminar is as follows: (1) Introduction to XQuery: Motivation, XQuery data model, XQuery type system, Basic query language concepts; (2) Internal Representation of XML Data: DOM, SAX Events, TokenStream, Skeleton, Vertical Partitioning; (3) XQuery Algebras: XQuery Core vs. Relational Algebra, XQuery Algebras from Research Projects; (4) XPath Query Processing: Transducers, Automata, etc.; (5) XQuery Optimization: XML query equivalence, Rewrite Rules, Cost Models; (6) XQuery Runtime Systems: Iterator Models, Algorithms for XQuery Operators; (7) XML Indexes: Value and path indexes, others; (8) XQuery Products and Prototypes: XQRL/BEA, Galax, Saxon, etc. (as available); (9) Advanced Query Processing Techniques, Related Topics: Querying compressed XML data, Multi-Query Optimization, Publish/Subscribe and XML Information Filter, XML Data Integration, XML Updates, XML integrity constraints; (10) Summary.
{"title":"XML query processing","authors":"D. Florescu, Donald Kossmann","doi":"10.1109/ICDE.2004.1320100","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320100","url":null,"abstract":"XQuery is starting to gain significant traction as a language for querying and transforming XML data. It is used in a variety of different products. Examples to date include XML database systems, XML document repositories, XML data integation, workflow systems, and publish and subscribe systems. In addition, XPath of which XQuery is a superset is used in various products such as Web browsers. Although the W3C XQuery specification has not yet attained Recommendation status, and the definition of the language has not entirely stabilized, a number of alternative proposals to implement and optimize XQuery have appeared both in industry and in the research community. Given the wide range of applications for which XQuery is applicable, a wide spectrum of alternative techniques have been proposed for XQuery processing. Some of these techniques are only useful for certain applications, other techniques are general-purpose. The goal of this seminar is to give an overview of the existing approaches to process XQuery expressions and to give details of the most important techniques. The presenters have experience from designing and building an industrial-strength XQuery engine [1]. The seminar will give details of that XQuery engine, but the seminar will also give extensive coverage of other XQuery engines and of the state of the art in the research community. The agenda for the seminar is as follows: (1) Introduction to XQuery: Motivation, XQuery data model, XQuery type system, Basic query language concepts; (2) Internal Representation of XML Data: DOM, SAX Events, TokenStream, Skeleton, Vertical Partitioning; (3) XQuery Algebras: XQuery Core vs. Relational Algebra, XQuery Algebras from Research Projects; (4) XPath Query Processing: Transducers, Automata, etc.; (5) XQuery Optimization: XML query equivalence, Rewrite Rules, Cost Models; (6) XQuery Runtime Systems: Iterator Models, Algorithms for XQuery Operators; (7) XML Indexes: Value and path indexes, others; (8) XQuery Products and Prototypes: XQRL/BEA, Galax, Saxon, etc. (as available); (9) Advanced Query Processing Techniques, Related Topics: Querying compressed XML data, Multi-Query Optimization, Publish/Subscribe and XML Information Filter, XML Data Integration, XML Updates, XML integrity constraints; (10) Summary.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614556","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320089
S. Murthy, D. Maier, L. Delcambre, S. Bowers
People often impose new interpretations onto existing information. In the process, they work with information in two layers: a base layer, where the original information resides, and a superimposed layer, where only the new interpretations reside. Abstractions defined in the Superimposed Pluggable Architecture for Contexts and Excerpts (SPARCE) ease communication between the two layers. SPARCE provides three key abstractions for superimposed information management: mark, context, and excerpt. We demonstrate two applications, RIDPad and Schematics Browser, for use in the appeal process of the US Forest Service (USFS).
{"title":"Superimposed applications using SPARCE","authors":"S. Murthy, D. Maier, L. Delcambre, S. Bowers","doi":"10.1109/ICDE.2004.1320089","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320089","url":null,"abstract":"People often impose new interpretations onto existing information. In the process, they work with information in two layers: a base layer, where the original information resides, and a superimposed layer, where only the new interpretations reside. Abstractions defined in the Superimposed Pluggable Architecture for Contexts and Excerpts (SPARCE) ease communication between the two layers. SPARCE provides three key abstractions for superimposed information management: mark, context, and excerpt. We demonstrate two applications, RIDPad and Schematics Browser, for use in the appeal process of the US Forest Service (USFS).","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130113380","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320075
A. Kumaran, J. Haritsa
Current database systems offer support for storing multilingual data, but are not capable of querying across languages, an important consideration in today's global economy. We therefore propose a new multilexical operator called LexEQUAL that extends the standard lexicographic matching in database systems to matching of text data across languages, specifically for names, which form close to twenty percent of text corpora. The implementation of the LexEQUAL operator is based on transforming matches in language space into parameterized approximate matches in the equivalent phoneme space. A detailed evaluation of our approach on a real data set shows that there exist settings of the algorithm parameters with which it is possible to achieve both good recall and precision.
{"title":"LexEQUAL: supporting multilexical queries in SQL","authors":"A. Kumaran, J. Haritsa","doi":"10.1109/ICDE.2004.1320075","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320075","url":null,"abstract":"Current database systems offer support for storing multilingual data, but are not capable of querying across languages, an important consideration in today's global economy. We therefore propose a new multilexical operator called LexEQUAL that extends the standard lexicographic matching in database systems to matching of text data across languages, specifically for names, which form close to twenty percent of text corpora. The implementation of the LexEQUAL operator is based on transforming matches in language space into parameterized approximate matches in the equivalent phoneme space. A detailed evaluation of our approach on a real data set shows that there exist settings of the algorithm parameters with which it is possible to achieve both good recall and precision.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134148625","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1319990
G. Cong, B. Ooi, K. Tan, A. Tung
In constrained data mining, users can specify constraints to prune the search space to avoid mining uninteresting knowledge. This is typically done by specifying some initial values of the constraints that are subsequently refined iteratively until satisfactory results are obtained. Existing mining schemes treat each iteration as a distinct mining process, and fail to exploit the information generated between iterations. We propose to salvage knowledge that is discovered from an earlier iteration of mining to enhance subsequent rounds of mining. In particular, we look at how frequent patterns can be recycled. Our proposed strategy operates in two phases. In the first phase, frequent patterns obtained from an early iteration are used to compress a database. In the second phase, subsequent mining processes operate on the compressed database. We propose two compression strategies and adapt three existing frequent pattern mining techniques to exploit the compressed database. Results from our extensive experimental study show that our proposed recycling algorithms outperform their nonrecycling counterpart by an order of magnitude.
{"title":"Go green: recycle and reuse frequent patterns","authors":"G. Cong, B. Ooi, K. Tan, A. Tung","doi":"10.1109/ICDE.2004.1319990","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1319990","url":null,"abstract":"In constrained data mining, users can specify constraints to prune the search space to avoid mining uninteresting knowledge. This is typically done by specifying some initial values of the constraints that are subsequently refined iteratively until satisfactory results are obtained. Existing mining schemes treat each iteration as a distinct mining process, and fail to exploit the information generated between iterations. We propose to salvage knowledge that is discovered from an earlier iteration of mining to enhance subsequent rounds of mining. In particular, we look at how frequent patterns can be recycled. Our proposed strategy operates in two phases. In the first phase, frequent patterns obtained from an early iteration are used to compress a database. In the second phase, subsequent mining processes operate on the compressed database. We propose two compression strategies and adapt three existing frequent pattern mining techniques to exploit the compressed database. Results from our extensive experimental study show that our proposed recycling algorithms outperform their nonrecycling counterpart by an order of magnitude.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129409546","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320002
M. Mokbel, Ming Lu, Walid G. Aref
We introduce the hash-merge join algorithm (HMJ, for short); a new nonblocking join algorithm that deals with data items from remote sources via unpredictable, slow, or bursty network traffic. The HMJ algorithm is designed with two goals in mind: (1) minimize the time to produce the first few results, and (2) produce join results even if the two sources of the join operator occasionally get blocked. The HMJ algorithm has two phases: The hashing phase and the merging phase. The hashing phase employs an in-memory hash-based join algorithm that produces join results as quickly as data arrives. The merging phase is responsible for producing join results if the two sources are blocked. Both phases of the HMJ algorithm are connected via a flushing policy that flushes in-memory parts into disk storage once the memory is exhausted. Experimental results show that HMJ combines the advantages of two state-of-the-art nonblocking join algorithms (XJoin and Progressive Merge Join) while avoiding their shortcomings.
{"title":"Hash-merge join: a non-blocking join algorithm for producing fast and early join results","authors":"M. Mokbel, Ming Lu, Walid G. Aref","doi":"10.1109/ICDE.2004.1320002","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320002","url":null,"abstract":"We introduce the hash-merge join algorithm (HMJ, for short); a new nonblocking join algorithm that deals with data items from remote sources via unpredictable, slow, or bursty network traffic. The HMJ algorithm is designed with two goals in mind: (1) minimize the time to produce the first few results, and (2) produce join results even if the two sources of the join operator occasionally get blocked. The HMJ algorithm has two phases: The hashing phase and the merging phase. The hashing phase employs an in-memory hash-based join algorithm that produces join results as quickly as data arrives. The merging phase is responsible for producing join results if the two sources are blocked. Both phases of the HMJ algorithm are connected via a flushing policy that flushes in-memory parts into disk storage once the memory is exhausted. Experimental results show that HMJ combines the advantages of two state-of-the-art nonblocking join algorithms (XJoin and Progressive Merge Join) while avoiding their shortcomings.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133116434","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}
Conventional tools for automatic metadata creation mostly extract named entities or patterns from texts and annotate them with information about persons, locations, dates, and so on. However, this kind of entity type information is often too primitive for more advanced intelligent applications such as concept-based search. Here, we try to generate semantically-deep metadata with limited human intervention. The main idea behind our approach is to use Web mining and categorization techniques to create thematic metadata. The proposed approach, comprises of three computational modules: feature extraction, HCQF (hier-concept query formulation) and text instance categorization. The feature extraction module sends the name of text instances to Web search engines, and the returned highly-ranked search-result pages are used to describe them.
{"title":"Mining the Web for generating thematic metadata from textual data","authors":"Chien-Chung Huang, Shui-Lung Chuang, Lee-Feng Chien","doi":"10.1109/ICDE.2004.1320065","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320065","url":null,"abstract":"Conventional tools for automatic metadata creation mostly extract named entities or patterns from texts and annotate them with information about persons, locations, dates, and so on. However, this kind of entity type information is often too primitive for more advanced intelligent applications such as concept-based search. Here, we try to generate semantically-deep metadata with limited human intervention. The main idea behind our approach is to use Web mining and categorization techniques to create thematic metadata. The proposed approach, comprises of three computational modules: feature extraction, HCQF (hier-concept query formulation) and text instance categorization. The feature extraction module sends the name of text instances to Web search engines, and the returned highly-ranked search-result pages are used to describe them.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130575096","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320043
K. Lou, K. Ramani, Sunil Prabhakar
We discuss the design and implementation of a prototype 3D engineering shape search system. The system incorporates multiple feature vectors, relevance feedback, and query by example and browsing, flexible definition of shape similarity, and efficient execution through multidimensional indexing and clustering. In order to offer more information for a user to determine similarity of 3D engineering shape, a 3D interface that allows users to manipulate shapes is proposed and implemented to present the search results. The system allows users to specify which feature vectors should be used to perform the search. The system is used to conduct extensive experimentation real data to test the effectiveness of various feature vectors for shape - the first such comparison of this type. The test results show that the descending order of the average precision of feature vectors is: principal moments, moment invariants, geometric parameters, and eigenvalues. In addition, a multistep similarity search strategy is proposed and tested to improve the effectiveness of 3D engineering shape search. It is shown that the multistep approach is more effective than the one-shot search approach, when a fixed number of shapes are retrieved.
{"title":"Content-based three-dimensional engineering shape search","authors":"K. Lou, K. Ramani, Sunil Prabhakar","doi":"10.1109/ICDE.2004.1320043","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320043","url":null,"abstract":"We discuss the design and implementation of a prototype 3D engineering shape search system. The system incorporates multiple feature vectors, relevance feedback, and query by example and browsing, flexible definition of shape similarity, and efficient execution through multidimensional indexing and clustering. In order to offer more information for a user to determine similarity of 3D engineering shape, a 3D interface that allows users to manipulate shapes is proposed and implemented to present the search results. The system allows users to specify which feature vectors should be used to perform the search. The system is used to conduct extensive experimentation real data to test the effectiveness of various feature vectors for shape - the first such comparison of this type. The test results show that the descending order of the average precision of feature vectors is: principal moments, moment invariants, geometric parameters, and eigenvalues. In addition, a multistep similarity search strategy is proposed and tested to improve the effectiveness of 3D engineering shape search. It is shown that the multistep approach is more effective than the one-shot search approach, when a fixed number of shapes are retrieved.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797602","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1319986
Jianyong Wang, Jiawei Han
Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and-test paradigm which is inherently costly in both runtime and space usage when the support threshold is low or the patterns become long. We present, BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. We adopt a novel sequence closure checking scheme called bidirectional extension, and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method and the Scan-Skip optimization technique. A thorough performance study with both sparse and dense real-life data sets has demonstrated that BIDE significantly outperforms the previous algorithms: it consumes order(s) of magnitude less memory and can be more than an order of magnitude faster. It is also linearly scalable in terms of database size.
{"title":"BIDE: efficient mining of frequent closed sequences","authors":"Jianyong Wang, Jiawei Han","doi":"10.1109/ICDE.2004.1319986","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1319986","url":null,"abstract":"Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and-test paradigm which is inherently costly in both runtime and space usage when the support threshold is low or the patterns become long. We present, BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. We adopt a novel sequence closure checking scheme called bidirectional extension, and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method and the Scan-Skip optimization technique. A thorough performance study with both sparse and dense real-life data sets has demonstrated that BIDE significantly outperforms the previous algorithms: it consumes order(s) of magnitude less memory and can be more than an order of magnitude faster. It is also linearly scalable in terms of database size.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126008620","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}
Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320044
K. Xu, Xiaofang Zhou, Xuemin Lin
Terrain can be approximated by a triangular mesh consisting millions of 3D points. Multiresolution triangular mesh (MTM) structures are designed to support applications that use terrain data at variable levels of detail (LOD). Typically, an MTM adopts a tree structure where a parent node represents a lower-resolution approximation of its descendants. Given a region of interest (ROI) and a LOD, the process of retrieving the required terrain data from the database is to traverse the MTM tree from the root to reach all the nodes satisfying the ROI and LOD conditions. This process, while being commonly used for multiresolution terrain visualization, is inefficient as either a large number of sequential I/O operations or fetching a large amount of extraneous data is incurred. Various spatial indexes have been proposed in the past to address this problem, however level-by-level tree traversal remains a common practice in order to obtain topological information among the retrieved terrain data. A new MTM data structure called direct mesh is proposed. We demonstrate that with direct mesh the amount of data retrieval can be substantially reduced. Comparing with existing MTM indexing methods, a significant performance improvement has been observed for real-life terrain data.
{"title":"Direct mesh: a multiresolution approach to terrain visualization","authors":"K. Xu, Xiaofang Zhou, Xuemin Lin","doi":"10.1109/ICDE.2004.1320044","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320044","url":null,"abstract":"Terrain can be approximated by a triangular mesh consisting millions of 3D points. Multiresolution triangular mesh (MTM) structures are designed to support applications that use terrain data at variable levels of detail (LOD). Typically, an MTM adopts a tree structure where a parent node represents a lower-resolution approximation of its descendants. Given a region of interest (ROI) and a LOD, the process of retrieving the required terrain data from the database is to traverse the MTM tree from the root to reach all the nodes satisfying the ROI and LOD conditions. This process, while being commonly used for multiresolution terrain visualization, is inefficient as either a large number of sequential I/O operations or fetching a large amount of extraneous data is incurred. Various spatial indexes have been proposed in the past to address this problem, however level-by-level tree traversal remains a common practice in order to obtain topological information among the retrieved terrain data. A new MTM data structure called direct mesh is proposed. We demonstrate that with direct mesh the amount of data retrieval can be substantially reduced. Comparing with existing MTM indexing methods, a significant performance improvement has been observed for real-life terrain data.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839455","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}
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