MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries

Baioco, Gisele Busichia; Traina, Agma J. M.; Traina, Caetano

doi:10.1109/ssdbm.2007.17

Cited by 7 publications

(15 citation statements)

References 15 publications

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“…Baioco el al. [3] present a cost model to process similarity queries over data indexed by metric access methods called MAMCost. This cost is estimated by a histogram of the density of elements in the metric space.…”

Section: Previous Workmentioning

confidence: 99%

“…The proposed framework were incorporated into SIREN to allow its to optimize similarity queries using the similarity algebra proposed by Traina et al [6] and Barioni et al [4] and the cost model proposed by Baioco et al [3].…”

Section: Previous Workmentioning

confidence: 99%

“…The query optimizer processes the canonical operator tree feeding the operations, the execution order of each condition and their cost into a parse tree, which stores the following parameters, as shown in Figure 3a: Operation indicates what kind of operation will be executed; Right edge (i) and left edge (i) are pointers to other operators in the parse tree; ACT reference is a pointer to the Attributes and Conditions Table (ACT) and cost is the estimated cost assigned of each operator. The cost-estimation technique used in this work is the MAMCost [3].…”

Section: The Framework Architecturementioning

confidence: 99%

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An efficient framework for similarity query optimization

Ferreira

Traina

2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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The increasing volume of multimedia data stored in relational database management systems (RDBMS) demands efficient ways to process similarity queries. Therefore, the query processor should provide mechanisms to express similarity queries, to interpret and translate them into equivalent expression in relational algebra, to evaluate alternative query plans and finally to execute the queries using the best plan found. In this paper, we present an effective framework to interpret, translate, select the best plan and efficiently execute similarity queries over data indexed by metric access methods. Experimental evaluation of the framework shows a reduction of up to 20% in the total time required to answer similarity queries.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: The Framework Architecturementioning

confidence: 99%

See 1 more Smart Citation

An efficient framework for similarity query optimization

Ferreira

Traina

2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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“…Accordingly, existing k-NN models estimate a threshold as the query radius by using the pairwise distance distribution within S. Such estimates follow a biased assumption: query elements are more likely posed in high-density areas of the search space. For instance, the model for multidimensional spaces in [Aly et al 2015] assumes the k-NN radii follow a uniform distribution regarding fixed intervals of k, while the models in [Ciaccia et al 1998] and [Baioco et al 2007] assume k-NN radii follow a binomial and an exponential distribution, respectively. The main drawback of such models is they disregard the 'locality' of each query, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Spline-based Cost Model for Metric Trees

Bedo

Traina

2017

Anais Do XXXII Simpósio Brasileiro De Banco De Dados (SBBD 2017)

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Whenever two (or more) access methods are alternatives for the execution of a query, how to choose which one is the best for the task? Such a decision is made by the DBMS optimizer module, which models the query costs according to the distribution of the data space. Cost modeling of similarity searches, however, requires the representation of distances’ rather than data distribution. In this paper, we propose the Stockpile model for cost estimation of similarity queries on metric trees by using pivot-based distance histograms that represent the local densities around the query elements. By combining the local densities to the probability of traversing the tree nodes, Stockpile provides a fair estimation of both disk accesses (I/O costs) and distance calculations (CPU costs). We compared Stockpile and two literature models regarding similarity queries in real-world data sources and our model was up to 85% more precise than the competitors.

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“…Also, ranking query plans do not need to materialize a query, making the query plan ranking much more efficient than the traditional ones, which can be prohibitively expensive. The work of Baioco et al [2007] presented a selectivity and cost model for similarity queries in the Slim-tree. A cost model to integrate multiple similarity-based image joins in a multimedia database using the R-tree index family was presented in Kosch [2010].…”

Section: Cost and Condition Selectivity Modelmentioning

confidence: 99%

Optimizing similarity queries in metric spaces meeting user's expectation

Ferreira¹

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I would like to thank my advisor and friend Prof. Dr. Caetano Traina Jr., who believed in me from the beginning when invited me to do undergraduate research. He afforded me opportunities and challenges, support and encouragement at all time, and mainly trust. My thanks to my French advisor, Prof. Dr. Richard Chbeir, who also contributed to guide my work. My special thanks to my dear husband Leandro, for always being there, for sharing happy moments and encouraging me to overcome difficult times, for his comprehension during my absences in countless weekends and holidays that I had to work, for his sweet words that make everything seems so much easier. I would like to thank my parents, José Maria and Mayra, my grandmother Ladice and all my family that is very large and I can not mention one by one, for their unconditional love, support and encouragement dedicated throughout my life, for their comprehension during my absences in holidays and family gatherings, for their hug and cheer all the time. I also thank my brothers José Maria Jr. and José Guilherme, my sister-in-law Josélia, my brother-in-law Rudinei, my nephews João Pedro and Diego, and my parents-in-law Maria Helena and Celso for their attention, encouragement words and for always being around when I needed. My sincere thanks to my sister-in-law Elaine for helping and teaching me many things throughout my academic life. My special thanks to my grandmother and heroine Carminda (in memoriam) and to my aunt and godmother Dama (in memoriam) for their love and support, for their life examples, for their unconditionally encouragement dedicated throughout my life, for hugging and cheering me all the time. My gratitude to Profa. Agma Traina at ICMC-USP for her time and effort on my thesis, for her collaborative work, ideas, words of encouragement and for her affection demonstrated always so kind. I also thanks Profa. Ires Dias for the collaborative work, time and effort invested with the algebra. My grateful to Profa. Sandra de Amo and Prof. Renato Fileto for their contribution to my work. I dedicate my sincere thanks to my friends and colleagues of the GBdI-USP in São Carlos-SP-Brazil, especially I am grateful to Prof. Junior, Robson, Carolina, Letrícia, Jaqueline, Willian, Lucio, Sérgio and Daniel C. for their important collaboration in the lab and meetings. I also thanks my colleagues of the Le2i-uB in Dijon-France. I am grateful to my friends Marcela Ribeiro, Luciana Romani, Fekade Getahum and Elie Raad for their contributions to my work and for incentive words in moments of despair and anguish. My thanks to Laura, Glaucia, Ana Paula, Lhais and Carolina for helping me with bureaucracies. I also thank the Instituto de Ciências Matemáticas e de Computação of USP in São Carlos-SP-Brazil and Université de Bourgogne in Dijon-France for their academic structures that became possible the development of this cotutlle work. Finally, I acknowledge the funding agencies FAPESP and CAPES for the financial support during this doctorate. Additionally, I thanks the funding ag...

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MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries

Cited by 7 publications

References 15 publications

An efficient framework for similarity query optimization

An efficient framework for similarity query optimization

A Spline-based Cost Model for Metric Trees

Optimizing similarity queries in metric spaces meeting user's expectation

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