Querying Multimedia Data by Similarity in Relational DBMS

Barioni, Maria Camila Nardini; Kaster, Daniel S.; Razente, Humberto Luiz; Traina, Agma J. M.; Júnior, Caetano Traina

doi:10.4018/978-1-60960-475-2.ch014

Cited by 13 publications

(20 citation statements)

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“…SIREN (Barioni et al, 2011) is a middleware between a client application and the RDBMS. The client sends SQL commands extended with similarity keywords, which are checked by SIREN to identify similarity predicates.…”

Section: Similarity Support On Rdbmsmentioning

confidence: 99%

On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations

Oliveira

Fraideinberze

Laverde

et al. 2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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Abstract:Crowdsourcing solutions can be helpful to extract information from disaster-related data during crisis management. However, certain information can only be obtained through similarity operations. Some of them also depend on additional data stored in a Relational Database Management System (RDBMS). In this context, several works focus on crisis management supported by data. Nevertheless, none of them provide a methodology for employing a similarity-enabled RDBMS in disaster-relief tasks. To fill this gap, we introduce a methodology together with the Data-Centric Crisis Management (DCCM) architecture, which employs our methods over a similarity-enabled RDBMS. We evaluate our proposal through three tasks: classification of incoming data regarding current events, identifying relevant information to guide rescue teams; filtering of incoming data, enhancing the decision support by removing near-duplicate data; and similarity retrieval of historical data, supporting analytical comprehension of the crisis context. To make it possible, similarity-based operations were implemented within one popular, open-source RDBMS. Results using real data from Flickr show that our proposal is feasible for real-time applications. In addition to high performance, accurate results were obtained with a proper combination of techniques for each task. Hence, we expect our work to provide a framework for further developments on crisis management solutions.

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Section: Similarity Support On Rdbmsmentioning

confidence: 99%

On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations

Oliveira

Fraideinberze

Laverde

et al. 2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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“…Our implementation, named as Kiara, supports an SQL extension for building similarity queries over complex data (Barioni et al, 2011). Also through the SQL extension, Kiara allows managing feature extractors and evaluation functions, which are dynamically (no recompilation) inserted and updated by user-defined functions written in C++.…”

Section: Implementation Of Dccmmentioning

confidence: 99%

“…Two of the most common queries used in contentbased retrieval are the Range Query and the k Nearest Neighbor (kNN) Query (Barioni et al, 2011). Range Query is defined by the function Rq(s q , ξ), where s q represents an object from data domain S and ξ is the radius used as distance constraint.…”

Section: Content-based Retrievalmentioning

confidence: 99%

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An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Dediź

Stanier

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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ICEIS 2016 received 257 paper submissions from 42 countries in all continents, which makes it one of the largest conferences in the World in the area of Information Systems, thus demonstrating the success and global dimension of this conference. From these, 42 papers were selected for publication and presentation at the Conference as full papers. These numbers, leading to a full-paper acceptance ratio of 16%, show the intention of preserving a high quality forum for this conference, a quality that we intend to maintain in the future, for the next editions of this conference.The high number and high quality of the received papers imposed difficult choices in the selection process. To evaluate each submission, a double blind paper review was performed by the Program Committee, whose members are highly qualified researchers in ICEIS topic areas.All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Index) and Scopus.Additionally, a short list of presented papers will be selected to be expanded into a forthcoming book of ICEIS 2016 Selected Papers to be published by Springer in the LNBIP Series.The technical program of the conference included a panel and 4 invited talks delivered by internationally distinguished speakers, namely: Claudia Loebbecke (University of Cologne, Germany), Sergio Gusmeroli (TXT e-solutions SPA, Italy), Wil Van Der Aalst (Technische Universiteit Eindhoven, Netherlands) and Jan Vom Brocke (University of Liechtenstein, Liechtenstein). Their participation positively contributes to reinforce the overall quality of the Conference and to provide a deeper understanding of the fields addressed by the conference.Moreover, ICEIS 2016 had a Doctoral Consortium on Enterprise Information Systems and 1 tutorial. We are thankful to the Conference Co-chairs (Olivier Camp and José Cordeiro) and Program Co-chairs (Slimane Hammoudi, Leszek Maciaszek and Michele M. Missikoff) for their dedication and hard work in organizing these events.We sincerely thank all the authors for their submissions and participation in ICEIS 2016. Furthermore, we would like to thank all the members of the program committee and reviewers, who helped us with their expertise, dedication and time. We would also like to thank the invited speakers for their excellent contribution in sharing their knowledge and vision and the workshop/special session chairs whose collaboration with ICEIS 2015 was much appreciated. Finally, we gratefully acknowledge the professional support of the ICEIS 2016 team for all organizational processes. XIIIWe hope that all colleagues find this a fruitful and inspiring conference. We hope to contribute to the development of the Enterprise Information Systems community and look forward to having additional research results presented at the next edition of ICEIS, details of which are available at http://www.iceis.org. Slimane Hammoudi University of Cologne, GermanyAbstract:...

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“…Considering the multimedia data types, for example, the traditional querying approach based on attribute matching is not suitable. This fact has motivated the development of querying approaches based on the concept of similarity among complex data elements [5]. There are several examples of querying approaches that dealt with this issue in the scientific literature, such as the similarity selection algorithms (e.g., k-nearest neighbor selection and range selection) [6], the similarity join algorithms (e.g., k-nearest neighbor join, k-closest neighbor join, join around, and range join) [7], and the diversification of similarity selections [8].…”

Section: Introductionmentioning

confidence: 99%

Optimizing metric access methods for querying and mining complex data types

2014

Self Cite

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Background: There are several application scenarios that can take advantage from the efficient processing of similarity operations in complex data types, such as multimedia data. Among them, it is possible to mention the execution of more complex query types (e.g., similarity queries) and several well-known data mining algorithms (e.g., data clustering) that are directly based on similarity computations. In order to speed up the similarity-based comparisons performed by these approaches, it is possible to store the dataset in specialized data structures known as metric access methods (MAM). Methods: In this article we present four node split policies that can be employed in the construction of M-tree, the pioneer dynamic MAM, and of Slim-tree, the M-tree successor. Results: These policies allow faster tree construction, as they result in better distribution of elements on the tree nodes and require less distance calculations when compared with the previously proposed ones. Furthermore, trees built with these policies have shown to be more efficient for techniques that require similarity computations, such as nearest neighbors queries and data clustering algorithms. Conclusion:The experimental results show that trees built with the proposed policies outperform those built with the original ones with regard to the number of disk accesses, the amount of distance calculations, and the time required to run the queries.

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Querying Multimedia Data by Similarity in Relational DBMS

Cited by 13 publications

References 50 publications

On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations

On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Optimizing metric access methods for querying and mining complex data types

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