Pub Date : 2004-03-30DOI: 10.1109/ICDE.2004.1320088
Yong Ye, Xintao Wu, K. Subramanian, Liying Zhang
DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. We propose to combine graphical model based interaction analysis with other data mining techniques (e.g., association rule, hierarchical clustering) for this purpose. For interaction analysis, we propose the use of graphical Gaussian model to discover pairwise gene interactions and loglinear model to discover multigene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships.
{"title":"GenExplore: interactive exploration of gene interactions from microarray data","authors":"Yong Ye, Xintao Wu, K. Subramanian, Liying Zhang","doi":"10.1109/ICDE.2004.1320088","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320088","url":null,"abstract":"DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. We propose to combine graphical model based interaction analysis with other data mining techniques (e.g., association rule, hierarchical clustering) for this purpose. For interaction analysis, we propose the use of graphical Gaussian model to discover pairwise gene interactions and loglinear model to discover multigene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"12 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":"128015724","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.2004.1320007
Rui Zhang, B. Ooi, K. Tan
The effectiveness of many existing high-dimensional indexing structures is limited to specific types of queries and workloads. For example, while the Pyramid technique and the iMinMax are efficient for window queries, the iDistance is superior for kNN queries. We present a new structure, called the P/sup +/-tree, that supports both window queries and kNN queries under different workloads efficiently. In the P/sup +/-tree, a B/sup +/-tree is employed to index the data points as follows. The data space is partitioned into subspaces based on clustering, and points in each subspace are mapped onto a single dimensional space using the Pyramid technique, and stored in the B/sup +/ -tree. The crux of the scheme lies in the transformation of the data which has two crucial properties. First, it maps each subspace into a hypercube so that the Pyramid technique can be applied. Second, it shifts the cluster center to the top of the pyramid, which is the case that the Pyramid technique works very efficiently. We present window and kNN query processing algorithms for the P/sup +/-tree. Through an extensive performance study, we show that the P/sup +/-tree has considerable speedup over the Pyramid technique and the iMinMax for window queries and outperforms the iDistance for kNN queries.
{"title":"Making the pyramid technique robust to query types and workloads","authors":"Rui Zhang, B. Ooi, K. Tan","doi":"10.1109/ICDE.2004.1320007","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320007","url":null,"abstract":"The effectiveness of many existing high-dimensional indexing structures is limited to specific types of queries and workloads. For example, while the Pyramid technique and the iMinMax are efficient for window queries, the iDistance is superior for kNN queries. We present a new structure, called the P/sup +/-tree, that supports both window queries and kNN queries under different workloads efficiently. In the P/sup +/-tree, a B/sup +/-tree is employed to index the data points as follows. The data space is partitioned into subspaces based on clustering, and points in each subspace are mapped onto a single dimensional space using the Pyramid technique, and stored in the B/sup +/ -tree. The crux of the scheme lies in the transformation of the data which has two crucial properties. First, it maps each subspace into a hypercube so that the Pyramid technique can be applied. Second, it shifts the cluster center to the top of the pyramid, which is the case that the Pyramid technique works very efficiently. We present window and kNN query processing algorithms for the P/sup +/-tree. Through an extensive performance study, we show that the P/sup +/-tree has considerable speedup over the Pyramid technique and the iMinMax for window queries and outperforms the iDistance for kNN queries.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"13 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":"120917431","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.2004.1320068
Lei Chen, M. Tamer Özsu
Similarity-based time series data retrieval has been used in many real world applications, such as stock data or weather data analysis. Two types of queries on time series data are generally studied: pattern existence queries and exact match queries. Here, we describe a technique to answer both pattern existence queries and exact match queries. A typical application that needs answers to both queries is an interactive analysis of time series data. We propose a histogram-based representation to approximate time series data.
{"title":"Multi-scale histograms for answering queries over time series data","authors":"Lei Chen, M. Tamer Özsu","doi":"10.1109/ICDE.2004.1320068","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320068","url":null,"abstract":"Similarity-based time series data retrieval has been used in many real world applications, such as stock data or weather data analysis. Two types of queries on time series data are generally studied: pattern existence queries and exact match queries. Here, we describe a technique to answer both pattern existence queries and exact match queries. A typical application that needs answers to both queries is an interactive analysis of time series data. We propose a histogram-based representation to approximate time series data.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"2 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":"121121704","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.2004.1319987
N. Vanetik, E. Gudes
Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data the emphasis is on frequent labels and common topologies. Here, the structure of the data is just as important as its content. When data contains large amount of different labels, both fully labeled and partially labeled data may be useful. More informative patterns can be found in the database if some of the pattern nodes can be regarded as 'unlabeled'. We study the problem of discovering typical fully and partially labeled patterns of graph data. Discovered patterns are useful in many applications, including: compact representation of source information and a road-map for browsing and querying information sources.
{"title":"Mining frequent labeled and partially labeled graph patterns","authors":"N. Vanetik, E. Gudes","doi":"10.1109/ICDE.2004.1319987","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1319987","url":null,"abstract":"Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data the emphasis is on frequent labels and common topologies. Here, the structure of the data is just as important as its content. When data contains large amount of different labels, both fully labeled and partially labeled data may be useful. More informative patterns can be found in the database if some of the pattern nodes can be regarded as 'unlabeled'. We study the problem of discovering typical fully and partially labeled patterns of graph data. Discovered patterns are useful in many applications, including: compact representation of source information and a road-map for browsing and querying information sources.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"94 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":"123286524","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.2004.1319984
Ning Zhang, V. Kacholia, M. Tamer Özsu
Path expressions are ubiquitous in XML processing languages. Existing approaches evaluate a path expression by selecting nodes that satisfies the tag-name and value constraints and then joining them according to the structural constraints. We propose a novel approach, next-of-kin (NoK) pattern matching, to speed up the node-selection step, and to reduce the join size significantly in the second step. To efficiently perform NoK pattern matching, we also propose a succinct XML physical storage scheme that is adaptive to updates and streaming XML as well. Our performance results demonstrate that the proposed storage scheme and path evaluation algorithm is highly efficient and outperforms the other tested systems in most cases.
{"title":"A succinct physical storage scheme for efficient evaluation of path queries in XML","authors":"Ning Zhang, V. Kacholia, M. Tamer Özsu","doi":"10.1109/ICDE.2004.1319984","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1319984","url":null,"abstract":"Path expressions are ubiquitous in XML processing languages. Existing approaches evaluate a path expression by selecting nodes that satisfies the tag-name and value constraints and then joining them according to the structural constraints. We propose a novel approach, next-of-kin (NoK) pattern matching, to speed up the node-selection step, and to reduce the join size significantly in the second step. To efficiently perform NoK pattern matching, we also propose a succinct XML physical storage scheme that is adaptive to updates and streaming XML as well. Our performance results demonstrate that the proposed storage scheme and path evaluation algorithm is highly efficient and outperforms the other tested systems in most cases.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"7 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":"116584510","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.2004.1320037
Hao He, Jun Yang
XML and other types of semistructured data are typically represented by a labeled directed graph. To speed up path expression queries over the graph, a variety of structural indexes have been proposed. They usually work by partitioning nodes in the data graph into equivalence classes and storing equivalence classes as index nodes. A(k)-index introduces the concept of local bisimilarity for partitioning, allowing the trade-off between index size and query answering power. However, all index nodes in A(k)-index have the same local similarity k, which cannot take advantage of the fact that a workload may contain path expressions of different lengths, or that different parts of the data graph may have different local similarity requirements. To overcome these limitations, we propose M(k)- and M*(k)-indexes. The basic M(k)-index is workload-aware: Like the previously proposed D(k)-index, it allows different index nodes to have different local similarity requirements, providing finer partitioning only for parts of the data graph targeted by longer path expressions. Unlike D(k)-index, M(k)-index is never over-refined for irrelevant index or data nodes. However, the workload-aware feature still incurs overrefinement due to over-qualified parent index nodes. Moreover, fine partitions penalize the performance of short path expressions. To solve these problems, we further propose the M*(k)-index. An M*(k)-index consists of a collection of indexes whose nodes are organized in a partition hierarchy, allowing successively coarser partitioning information to co-exist with the finest partitioning information required. Experiments show that our indexes are superior to previously proposed indexes in terms of index size and query performance.
{"title":"Multiresolution indexing of XML for frequent queries","authors":"Hao He, Jun Yang","doi":"10.1109/ICDE.2004.1320037","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320037","url":null,"abstract":"XML and other types of semistructured data are typically represented by a labeled directed graph. To speed up path expression queries over the graph, a variety of structural indexes have been proposed. They usually work by partitioning nodes in the data graph into equivalence classes and storing equivalence classes as index nodes. A(k)-index introduces the concept of local bisimilarity for partitioning, allowing the trade-off between index size and query answering power. However, all index nodes in A(k)-index have the same local similarity k, which cannot take advantage of the fact that a workload may contain path expressions of different lengths, or that different parts of the data graph may have different local similarity requirements. To overcome these limitations, we propose M(k)- and M*(k)-indexes. The basic M(k)-index is workload-aware: Like the previously proposed D(k)-index, it allows different index nodes to have different local similarity requirements, providing finer partitioning only for parts of the data graph targeted by longer path expressions. Unlike D(k)-index, M(k)-index is never over-refined for irrelevant index or data nodes. However, the workload-aware feature still incurs overrefinement due to over-qualified parent index nodes. Moreover, fine partitions penalize the performance of short path expressions. To solve these problems, we further propose the M*(k)-index. An M*(k)-index consists of a collection of indexes whose nodes are organized in a partition hierarchy, allowing successively coarser partitioning information to co-exist with the finest partitioning information required. Experiments show that our indexes are superior to previously proposed indexes in terms of index size and query performance.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"3 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":"115510562","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.2004.1320034
H. Jagadish, R. Ng, B. Ooi, A. Tung
Real datasets are often large enough to necessitate data compression. Traditional 'syntactic' data compression methods treat the table as a large byte string and operate at the byte level. The tradeoff in such cases is usually between the ease of retrieval (the ease with which one can retrieve a single tuple or attribute value without decompressing a much larger unit) and the effectiveness of the compression. In this regard, the use of semantic compression has generated considerable interest and motivated certain recent works. We propose a semantic compression algorithm called ItCompress ITerative Compression, which achieves good compression while permitting access even at attribute level without requiring the decompression of a larger unit. ItCompress iteratively improves the compression ratio of the compressed output during each scan of the table. The amount of compression can be tuned based on the number of iterations. Moreover, the initial iterations provide significant compression, thereby making it a cost-effective compression technique. Extensive experiments were conducted and the results indicate the superiority of ItCompress with respect to previously known techniques, such as 'SPARTAN' and 'fascicles'.
{"title":"ItCompress: an iterative semantic compression algorithm","authors":"H. Jagadish, R. Ng, B. Ooi, A. Tung","doi":"10.1109/ICDE.2004.1320034","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320034","url":null,"abstract":"Real datasets are often large enough to necessitate data compression. Traditional 'syntactic' data compression methods treat the table as a large byte string and operate at the byte level. The tradeoff in such cases is usually between the ease of retrieval (the ease with which one can retrieve a single tuple or attribute value without decompressing a much larger unit) and the effectiveness of the compression. In this regard, the use of semantic compression has generated considerable interest and motivated certain recent works. We propose a semantic compression algorithm called ItCompress ITerative Compression, which achieves good compression while permitting access even at attribute level without requiring the decompression of a larger unit. ItCompress iteratively improves the compression ratio of the compressed output during each scan of the table. The amount of compression can be tuned based on the number of iterations. Moreover, the initial iterations provide significant compression, thereby making it a cost-effective compression technique. Extensive experiments were conducted and the results indicate the superiority of ItCompress with respect to previously known techniques, such as 'SPARTAN' and 'fascicles'.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"88 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":"115641156","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.2004.1320042
K. Kanth, S. Ravada, N. An
Much research has been devoted to scalable storage and retrieval techniques for domain databases such as spatial, text, XML and gene sequence data. Many efficient indexing techniques have been developed in this context. Given the improvement in the underlying technology, database applications are increasingly using domain data in transactional semantics. We examine the issue of when during the lifetime of a transaction is it better to incorporate updates in domain indexes. We present our experiences with R-tree indexes in Oracle. We examine two approaches for incorporating updates in spatial R-tree indexes: the first at update time, and the second at commit time. The first approach immediately incorporates changes in the index right away using system transactions and at commit time makes them visible to other transactions. The second approach, referred to as the deferred-incorporate approach, defers the updates in a secondary table and incorporates the changes in the index only at commit time. In experiments on real data sets, we compare the performance of the two approaches. For most transactions with reasonable number of update operations, we observe that the deferred approach outperforms the immediate-incorporate approach significantly for update operations and with appropriate optimizations achieves comparable query performance.
{"title":"Incorporating updates in domain indexes: experiences with Oracle spatial R-trees","authors":"K. Kanth, S. Ravada, N. An","doi":"10.1109/ICDE.2004.1320042","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320042","url":null,"abstract":"Much research has been devoted to scalable storage and retrieval techniques for domain databases such as spatial, text, XML and gene sequence data. Many efficient indexing techniques have been developed in this context. Given the improvement in the underlying technology, database applications are increasingly using domain data in transactional semantics. We examine the issue of when during the lifetime of a transaction is it better to incorporate updates in domain indexes. We present our experiences with R-tree indexes in Oracle. We examine two approaches for incorporating updates in spatial R-tree indexes: the first at update time, and the second at commit time. The first approach immediately incorporates changes in the index right away using system transactions and at commit time makes them visible to other transactions. The second approach, referred to as the deferred-incorporate approach, defers the updates in a secondary table and incorporates the changes in the index only at commit time. In experiments on real data sets, we compare the performance of the two approaches. For most transactions with reasonable number of update operations, we observe that the deferred approach outperforms the immediate-incorporate approach significantly for update operations and with appropriate optimizations achieves comparable query performance.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"40 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":"130061907","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.2004.1320032
B. Yao, M. Tamer Özsu, Nitin Khandelwal
XML support is being added to existing database management systems (DBMSs) and native XML systems are being developed both in industry and in academia. The individual performance characteristics of these approaches as well as the relative performance of various systems is an ongoing concern. In this paper we discuss the XBench XML benchmark and report on the relative performance of various DBMSs. XBench is a family of XML benchmarks which recognizes that the XML data that DBMSs manage are quite varied and no one database schema and workload can properly capture this variety. Thus, the members of this benchmark family have been defined for capturing diverse application domains.
{"title":"XBench benchmark and performance testing of XML DBMSs","authors":"B. Yao, M. Tamer Özsu, Nitin Khandelwal","doi":"10.1109/ICDE.2004.1320032","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320032","url":null,"abstract":"XML support is being added to existing database management systems (DBMSs) and native XML systems are being developed both in industry and in academia. The individual performance characteristics of these approaches as well as the relative performance of various systems is an ongoing concern. In this paper we discuss the XBench XML benchmark and report on the relative performance of various DBMSs. XBench is a family of XML benchmarks which recognizes that the XML data that DBMSs manage are quite varied and no one database schema and workload can properly capture this variety. Thus, the members of this benchmark family have been defined for capturing diverse application domains.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"88 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":"126778340","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.2004.1320070
Bertram Ludäscher, Alan Nash
A Web service operation can be seen as a function op: X/sub 1/,..., X/sub n/ /spl rarr/ Y/sub 1/,..., Y/sub m/ having an input message (request) with n arguments (parts), and an output message (response) with m parts. We study the problem of deciding whether a query Q is feasible, i.e., whether there exists a logically equivalent query Q' that can be executed observing the limited access patterns given by the Web service (source) relations. Executability depends on the specific syntactic form of a query, while feasibility is a more "robust" semantic notion, involving all equivalent queries (i.e., reorderings, minimized queries, etc). We show that deciding query feasibility (called "stability") is NP-complete for conjunctive queries (CQ) and for conjunctive queries with union (UCQ).
{"title":"Web service composition through declarative queries: the case of conjunctive queries with union and negation","authors":"Bertram Ludäscher, Alan Nash","doi":"10.1109/ICDE.2004.1320070","DOIUrl":"https://doi.org/10.1109/ICDE.2004.1320070","url":null,"abstract":"A Web service operation can be seen as a function op: X/sub 1/,..., X/sub n/ /spl rarr/ Y/sub 1/,..., Y/sub m/ having an input message (request) with n arguments (parts), and an output message (response) with m parts. We study the problem of deciding whether a query Q is feasible, i.e., whether there exists a logically equivalent query Q' that can be executed observing the limited access patterns given by the Web service (source) relations. Executability depends on the specific syntactic form of a query, while feasibility is a more \"robust\" semantic notion, involving all equivalent queries (i.e., reorderings, minimized queries, etc). We show that deciding query feasibility (called \"stability\") is NP-complete for conjunctive queries (CQ) and for conjunctive queries with union (UCQ).","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"11 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":"126147273","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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