Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767896
Xuemin Lin, Ying Zhang, W. Zhang, M. A. Cheema
In many applications involving the multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions. As a key feature, the skyline in a multi-dimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no mater how their preferences vary. Driven by many applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this paper we propose a novel skyline operator, namely stochastic skyline. In the light of the expected utility principle, stochastic skyline guarantees to provide the minimum set of candidates for the optimal solutions over all possible monotonic multiplicative utility functions. In contrast to the conventional skyline or the probabilistic skyline computation, we show that the problem of stochastic skyline is NP-complete with respect to the dimensionality. Novel and efficient algorithms are developed to efficiently compute stochastic skyline over multi-dimensional uncertain data, which run in polynomial time if the dimensionality is fixed. We also show, by theoretical analysis and experiments, that the size of stochastic skyline is quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs.
{"title":"Stochastic skyline operator","authors":"Xuemin Lin, Ying Zhang, W. Zhang, M. A. Cheema","doi":"10.1109/ICDE.2011.5767896","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767896","url":null,"abstract":"In many applications involving the multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions. As a key feature, the skyline in a multi-dimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no mater how their preferences vary. Driven by many applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this paper we propose a novel skyline operator, namely stochastic skyline. In the light of the expected utility principle, stochastic skyline guarantees to provide the minimum set of candidates for the optimal solutions over all possible monotonic multiplicative utility functions. In contrast to the conventional skyline or the probabilistic skyline computation, we show that the problem of stochastic skyline is NP-complete with respect to the dimensionality. Novel and efficient algorithms are developed to efficiently compute stochastic skyline over multi-dimensional uncertain data, which run in polynomial time if the dimensionality is fixed. We also show, by theoretical analysis and experiments, that the size of stochastic skyline is quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889020","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767901
Pit Fender, G. Moerkotte
Finding an optimal execution order of join operations is a crucial task in every cost-based query optimizer. Since there are many possible join trees for a given query, the overhead of the join (tree) enumeration algorithm per valid join tree should be minimal. In the case of a clique-shaped query graph, the best known top-down algorithm has a complexity of Θ(n2) per join tree, where n is the number of relations. In this paper, we present an algorithm that has an according O(1) complexity in this case. We show experimentally that this more theoretical result has indeed a high impact on the performance in other non-clique settings. This is especially true for cyclic query graphs. Further, we evaluate the performance of our new algorithm and compare it with the best top-down and bottom-up algorithms described in the literature.
{"title":"A new, highly efficient, and easy to implement top-down join enumeration algorithm","authors":"Pit Fender, G. Moerkotte","doi":"10.1109/ICDE.2011.5767901","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767901","url":null,"abstract":"Finding an optimal execution order of join operations is a crucial task in every cost-based query optimizer. Since there are many possible join trees for a given query, the overhead of the join (tree) enumeration algorithm per valid join tree should be minimal. In the case of a clique-shaped query graph, the best known top-down algorithm has a complexity of Θ(n2) per join tree, where n is the number of relations. In this paper, we present an algorithm that has an according O(1) complexity in this case. We show experimentally that this more theoretical result has indeed a high impact on the performance in other non-clique settings. This is especially true for cyclic query graphs. Further, we evaluate the performance of our new algorithm and compare it with the best top-down and bottom-up algorithms described in the literature.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128919544","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767877
Jorge-Arnulfo Quiané-Ruiz, C. Pinkel, Jörg Schad, J. Dittrich
MapReduce is a computing paradigm that has gained a lot of popularity as it allows non-expert users to easily run complex analytical tasks at very large-scale. At such scale, task and node failures are no longer an exception but rather a characteristic of large-scale systems. This makes fault-tolerance a critical issue for the efficient operation of any application. MapReduce automatically reschedules failed tasks to available nodes, which in turn recompute such tasks from scratch. However, this policy can significantly decrease performance of applications. In this paper, we propose a family of Recovery Algorithms for Fast-Tracking (RAFT) MapReduce. As ease-of-use is a major feature of MapReduce, RAFT focuses on simplicity and also non-intrusiveness, in order to be implementation-independent. To efficiently recover from task failures, RAFT exploits the fact that MapReduce produces and persists intermediate results at several points in time. RAFT piggy-backs checkpoints on the task progress computation. To deal with multiple node failures, we propose query metadata checkpointing. We keep track of the mapping between input key-value pairs and intermediate data for all reduce tasks. Thereby, RAFT does not need to re-execute completed map tasks entirely. Instead RAFT only recomputes intermediate data that were processed for local reduce tasks and hence not shipped to another node for processing. We also introduce a scheduling strategy taking full advantage of these recovery algorithms. We implemented RAFT on top of Hadoop and evaluated it on a 45-node cluster using three common analytical tasks. Overall, our experimental results demonstrate that RAFT outperforms Hadoop runtimes by 23% on average under task and node failures. The results also show that RAFT has negligible runtime overhead.
{"title":"RAFTing MapReduce: Fast recovery on the RAFT","authors":"Jorge-Arnulfo Quiané-Ruiz, C. Pinkel, Jörg Schad, J. Dittrich","doi":"10.1109/ICDE.2011.5767877","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767877","url":null,"abstract":"MapReduce is a computing paradigm that has gained a lot of popularity as it allows non-expert users to easily run complex analytical tasks at very large-scale. At such scale, task and node failures are no longer an exception but rather a characteristic of large-scale systems. This makes fault-tolerance a critical issue for the efficient operation of any application. MapReduce automatically reschedules failed tasks to available nodes, which in turn recompute such tasks from scratch. However, this policy can significantly decrease performance of applications. In this paper, we propose a family of Recovery Algorithms for Fast-Tracking (RAFT) MapReduce. As ease-of-use is a major feature of MapReduce, RAFT focuses on simplicity and also non-intrusiveness, in order to be implementation-independent. To efficiently recover from task failures, RAFT exploits the fact that MapReduce produces and persists intermediate results at several points in time. RAFT piggy-backs checkpoints on the task progress computation. To deal with multiple node failures, we propose query metadata checkpointing. We keep track of the mapping between input key-value pairs and intermediate data for all reduce tasks. Thereby, RAFT does not need to re-execute completed map tasks entirely. Instead RAFT only recomputes intermediate data that were processed for local reduce tasks and hence not shipped to another node for processing. We also introduce a scheduling strategy taking full advantage of these recovery algorithms. We implemented RAFT on top of Hadoop and evaluated it on a 45-node cluster using three common analytical tasks. Overall, our experimental results demonstrate that RAFT outperforms Hadoop runtimes by 23% on average under task and node failures. The results also show that RAFT has negligible runtime overhead.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025255","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767887
Tingjian Ge, David J. Grabiner, S. Zdonik
Array database systems are architected for scientific and engineering applications. In these applications, the value of a cell is often imprecise and uncertain. There are at least two reasons that a Monte Carlo query processing algorithm is usually required for such uncertain data. Firstly, a probabilistic graphical model must often be used to model correlation, which requires a Monte Carlo inference algorithm for the operations in our database. Secondly, mathematical operators required by science and engineering domains are much more complex than those of SQL. State-of-the-art query processing uses Monte Carlo approximation. We give an example of using Markov Random Fields combined with an array's chunking or tiling mechanism to model correlated data. We then propose solutions for two of the most challenging problems in this framework, namely the expensive array join operation, and the determination and optimization of stopping conditions of Monte Carlo query processing. Finally, we perform an extensive empirical study on a real world application.
{"title":"Monte Carlo query processing of uncertain multidimensional array data","authors":"Tingjian Ge, David J. Grabiner, S. Zdonik","doi":"10.1109/ICDE.2011.5767887","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767887","url":null,"abstract":"Array database systems are architected for scientific and engineering applications. In these applications, the value of a cell is often imprecise and uncertain. There are at least two reasons that a Monte Carlo query processing algorithm is usually required for such uncertain data. Firstly, a probabilistic graphical model must often be used to model correlation, which requires a Monte Carlo inference algorithm for the operations in our database. Secondly, mathematical operators required by science and engineering domains are much more complex than those of SQL. State-of-the-art query processing uses Monte Carlo approximation. We give an example of using Markov Random Fields combined with an array's chunking or tiling mechanism to model correlated data. We then propose solutions for two of the most challenging problems in this framework, namely the expensive array join operation, and the determination and optimization of stopping conditions of Monte Carlo query processing. Finally, we perform an extensive empirical study on a real world application.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425343","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767922
Ning Jin, Wei Wang
Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine discriminative subgraphs efficiently. We discover that search history of discriminative subgraph mining is very useful in computing empirical upper-bounds of discrimination scores of subgraphs. We propose a novel discriminative subgraph mining method, LTS (Learning To Search), which begins with a greedy algorithm that first samples the search space through subgraph probing and then explores the search space in a branch and bound fashion leveraging the search history of these samples. Extensive experiments have been performed to analyze the gain in performance by taking into account search history and to demonstrate that LTS can significantly improve performance compared with the state-of-the-art discriminative subgraph mining algorithms.
判别子图可以用来描述复杂图，构造图分类器和生成图索引。判别子图的搜索空间通常非常大。大多数判别子图的兴趣度测量对于子图频率既不是单调的也不是反单调的。因此，分支定界算法无法有效地挖掘判别子图。我们发现判别子图挖掘的搜索历史对于计算子图判别分数的经验上界是非常有用的。我们提出了一种新的判别子图挖掘方法LTS (Learning To Search)，它从贪婪算法开始，首先通过子图探测对搜索空间进行采样，然后利用这些样本的搜索历史以分支和界的方式探索搜索空间。已经进行了大量的实验，通过考虑搜索历史来分析性能的增益，并证明与最先进的判别子图挖掘算法相比，LTS可以显着提高性能。
{"title":"LTS: Discriminative subgraph mining by learning from search history","authors":"Ning Jin, Wei Wang","doi":"10.1109/ICDE.2011.5767922","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767922","url":null,"abstract":"Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine discriminative subgraphs efficiently. We discover that search history of discriminative subgraph mining is very useful in computing empirical upper-bounds of discrimination scores of subgraphs. We propose a novel discriminative subgraph mining method, LTS (Learning To Search), which begins with a greedy algorithm that first samples the search space through subgraph probing and then explores the search space in a branch and bound fashion leveraging the search history of these samples. Extensive experiments have been performed to analyze the gain in performance by taking into account search history and to demonstrate that LTS can significantly improve performance compared with the state-of-the-art discriminative subgraph mining algorithms.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131448195","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767915
I. Manolescu, Konstantinos Karanasos, V. Vassalos, Spyros Zoupanos
We consider the problem of rewriting XQuery queries using multiple materialized XQuery views. The XQuery dialect we use to express views and queries corresponds to tree patterns (returning data from several nodes, at different granularities, ranging from node identifiers to full XML subtrees) with value joins. We provide correct and complete algorithms for finding minimal rewritings, in which no view is redundant. Our work extends the state of the art by considering more flexible views than the mostly XPath 1.0 dialects previously considered, and more powerful rewritings. We implemented our algorithms and assess their performance through a set of experiments.
{"title":"Efficient XQuery rewriting using multiple views","authors":"I. Manolescu, Konstantinos Karanasos, V. Vassalos, Spyros Zoupanos","doi":"10.1109/ICDE.2011.5767915","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767915","url":null,"abstract":"We consider the problem of rewriting XQuery queries using multiple materialized XQuery views. The XQuery dialect we use to express views and queries corresponds to tree patterns (returning data from several nodes, at different granularities, ranging from node identifiers to full XML subtrees) with value joins. We provide correct and complete algorithms for finding minimal rewritings, in which no view is redundant. Our work extends the state of the art by considering more flexible views than the mostly XPath 1.0 dialects previously considered, and more powerful rewritings. We implemented our algorithms and assess their performance through a set of experiments.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126725230","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767941
Daniel Deutch, Ohad Greenshpan, Boris Kostenko, T. Milo
We introduce in this Demonstration a system called Trivia Masster that generates a very large Database of facts in a variety of topics, and uses it for question answering. The facts are collected from human users (the “crowd”); the system motivates users to contribute to the Database by using a Trivia Game, where users gain points based on their contribution. A key challenge here is to provide a suitable Data Cleaning mechanism that allows to identify which of the facts (answers to Trivia questions) submitted by users are indeed correct / reliable, and consequently how many points to grant users, how to answer questions based on the collected data, and which questions to present to the Trivia players, in order to improve the data quality. As no existing single Data Cleaning technique provides a satisfactory solution to this challenge, we propose here a novel approach, based on a declarative framework for defining recursive and probabilistic Data Cleaning rules. Our solution employs an algorithm that is based on Markov Chain Monte Carlo Algorithms.
{"title":"Using Markov Chain Monte Carlo to play Trivia","authors":"Daniel Deutch, Ohad Greenshpan, Boris Kostenko, T. Milo","doi":"10.1109/ICDE.2011.5767941","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767941","url":null,"abstract":"We introduce in this Demonstration a system called Trivia Masster that generates a very large Database of facts in a variety of topics, and uses it for question answering. The facts are collected from human users (the “crowd”); the system motivates users to contribute to the Database by using a Trivia Game, where users gain points based on their contribution. A key challenge here is to provide a suitable Data Cleaning mechanism that allows to identify which of the facts (answers to Trivia questions) submitted by users are indeed correct / reliable, and consequently how many points to grant users, how to answer questions based on the collected data, and which questions to present to the Trivia players, in order to improve the data quality. As no existing single Data Cleaning technique provides a satisfactory solution to this challenge, we propose here a novel approach, based on a declarative framework for defining recursive and probabilistic Data Cleaning rules. Our solution employs an algorithm that is based on Markov Chain Monte Carlo Algorithms.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128127267","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767868
Thomas Neumann, G. Moerkotte
Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are not well-prepared to perform cardinality estimation in this context. Further, there are hardly any special cardinality estimation methods for RDF databases. To overcome this lack of appropriate cardinality estimation methods, we introduce characteristic sets together with new cardinality estimation methods based upon them. We then show experimentally that the new methods are-in the RDF context-highly superior to the estimation methods employed by commercial DBMSs and by the open-source RDF store RDF-3X.
{"title":"Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins","authors":"Thomas Neumann, G. Moerkotte","doi":"10.1109/ICDE.2011.5767868","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767868","url":null,"abstract":"Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are not well-prepared to perform cardinality estimation in this context. Further, there are hardly any special cardinality estimation methods for RDF databases. To overcome this lack of appropriate cardinality estimation methods, we introduce characteristic sets together with new cardinality estimation methods based upon them. We then show experimentally that the new methods are-in the RDF context-highly superior to the estimation methods employed by commercial DBMSs and by the open-source RDF store RDF-3X.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114615115","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767912
Robert Fink, Dan Olteanu, Swaroop Rath
Extensive work has recently been done on the evaluation of positive queries on probabilistic databases. The case of queries with negation has notoriously been left out, since it raises serious additional challenges to efficient query evaluation.
{"title":"Providing support for full relational algebra in probabilistic databases","authors":"Robert Fink, Dan Olteanu, Swaroop Rath","doi":"10.1109/ICDE.2011.5767912","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767912","url":null,"abstract":"Extensive work has recently been done on the evaluation of positive queries on probabilistic databases. The case of queries with negation has notoriously been left out, since it raises serious additional challenges to efficient query evaluation.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047275","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 : 2011-04-11DOI: 10.1109/ICDE.2011.5767836
S. Narayanan, F. Waas
Enterprise database systems handle a variety of diverse query workloads that are of different importance to the business. For example, periodic reporting queries are usually mission critical whereas ad-hoc queries by analysts tend to be less crucial. It is desirable to enable database administrators to express (and modify) the importance of queries at a simple and intuitive level. The mechanism used to enforce these priorities must be robust, adaptive and efficient.
{"title":"Dynamic prioritization of database queries","authors":"S. Narayanan, F. Waas","doi":"10.1109/ICDE.2011.5767836","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767836","url":null,"abstract":"Enterprise database systems handle a variety of diverse query workloads that are of different importance to the business. For example, periodic reporting queries are usually mission critical whereas ad-hoc queries by analysts tend to be less crucial. It is desirable to enable database administrators to express (and modify) the importance of queries at a simple and intuitive level. The mechanism used to enforce these priorities must be robust, adaptive and efficient.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988506","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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