Large Graph Analysis in the GMine System

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally

BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.Comment: 8 pages, Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, IEEE Pres

3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging

Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.Comment: This is a pre-print of an article published in Computer-Based Medical Systems. The final authenticated version is available online at: https://doi.org/10.1109/CBMS.2019.0009

TDANetVis: Suggesting temporal resolutions for graph visualization using zigzag persistent homology

Temporal graphs are commonly used to represent complex systems and track the evolution of their constituents over time. Visualizing these graphs is crucial as it allows one to quickly identify anomalies, trends, patterns, and other properties leading to better decision-making. In this context, the to-be-adopted temporal resolution is crucial in constructing and analyzing the layout visually. The choice of a resolution is critical, e.g., when dealing with temporally sparse graphs. In such cases, changing the temporal resolution by grouping events (i.e., edges) from consecutive timestamps, a technique known as timeslicing, can aid in the analysis and reveal patterns that might not be discernible otherwise. However, choosing a suitable temporal resolution is not trivial. In this paper, we propose TDANetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing a given graph, i.e., resolutions that lead to substantial topological changes in the graph structure. To achieve this goal, TDANetVis leverages zigzag persistent homology, a well-established technique from Topological Data Analysis (TDA). To enhance visual graph analysis, TDANetVis also incorporates the colored barcode, a novel timeline-based visualization built on the persistence barcodes commonly used in TDA. We demonstrate the usefulness and effectiveness of TDANetVis through a usage scenario and a user study involving 27 participants.Comment: This document contains the main article and supplementary material. For associated code and software, see https://github.com/raphaeltinarrage/TDANetVi

SemIndex: Semantic-Aware Inverted Index

[email protected] paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.This study is partly funded by: Bourgogne Region program, CNRS, and STIC AmSud project Geo-Climate XMine, and LAU grant SOERC-1314T012.Revisión por pare

Combinando análise fractal e mineração de séries temporais para identificação de extremos climáticos regionais

Nas últimas décadas, grandes quantidades de dados climáticos provenientes de estações meteorológicas e de outros tipos de sensores têm sido coletadas e armazenadas por diversas instituições. A análise desses dados tornou-se uma tarefa importante devido às mudanças climáticas e seus efeitos sociais e econômicos. Este trabalho propõe um processo de análise de múltiplas séries temporais climáticas para identificar padrões temporais intrínsecos aos dados. Considerando múltiplas séries como uma data stream, é possível integrar diferentes variáveis climáticas e detectar mudanças de comportamento ao longo do tempo. Estudos em séries climáticas reais coletadas em diferentes regiões do Brasil mostram o potencial de aplicação dessa abordagem