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.
The Relational Algebra is composed of several operators to assist queries and data manipulation on Relational Databases. The Relational Division operator, particularly, allows simple representations of several queries involving the concept of “for all”, however, the SQL does not have an explicit implementation for it. In this paper, we compare the performance of the best implementation known for the division operator in SQL, considering different cases of use. We also present a new algorithm for the division, which we implemented through stored procedures. We performed a case study using the relational division to select genetic data. The results showed that our implementation for the relational division is potentially faster than the best implementation in SQL.
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