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

Abstract

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.

Effective and Unsupervised Fractal-Based Feature Selection for Very Large Datasets: Removing Linear and Non-linear Attribute Correlations

“…This appendix presents one novel architecture to support decision-making during crisis situations that was developed in parallel with the main proposal of the MSc program, in collaboration with other researchers from the Database and Image Group -GBdI at ICMC/USP. The work generated a full paper (OLIVEIRA et al, 2016) presented at the International Conference on Enterprise Information Systems -ICEIS 2016 (Qualis B2).…”

Conquering knowledge from images: improving image mining with region-based analysis and associated information

“…However, most of the processing performed by image mining techniques consider the entire image. This hurts most tasks since regions that are not of interest are considered in the analysis step, without proper distinction (OLIVEIRA et al, 2016;CHINO et al, 2015). Region-based analysis of images can improve the detection and content-based retrieval results.…”

“…This scenario brings out the need for processing the available data effectively and efficiently. The appropriate analysis of the available information can help authorities in emergency situations (CHINO et al, 2015;OLIVEIRA et al, 2016), supporting education and medical decision-making (SANTOS et al, 2018;FERREIRA et al, 2018), as well as speeding-up pipelines known to be time-consuming (STEGMAIER et al, 2014;HE et al, 2017;ULMAN et al, 2017).…”

On the support of the similarity-aware division operator in a relational database management system

“…Similarity queries have been widely employed to manipulate complex data such as audio, video, images, genetic sequences, long texts, etc (TRAINA et al, 2003;IQBAL;ODETAYO;JAMES, 2012;TROJACANEC;DIMITROVSKI;LOSKOVSKA, 2009;OLIVEIRA et al, 2016;VASCONCELOS et al, 2018;. Such data do not present total order relationship so operators like <, ≤, >, ≥ cannot be used and the identity comparison (=) is usually meaningless as these types of data will hardly ever be equal to one another (CHáVEZ et al, 2001;SAMET, 2005;ZEZULA et al, 2010).…”

“…Nowadays, modern applications demand efficient manipulation of complex data such as audio, video, images, genetic sequences, long texts, etc (TRAINA et al, 2003;IQBAL;ODETAYO;JAMES, 2012;TROJACANEC;DIMITROVSKI;LOSKOVSKA, 2009;OLIVEIRA et al, 2016;VASCONCELOS et al, 2018;.…”