scholarly journals Temporal Evolution of Complex Data

2020 ◽  
Author(s):  
Isis Caroline Oliveira de Sousa Fogaça ◽  
Renato Bueno

Monitoring the temporal evolution of data is essential in many areas of application of databases, such as medicine, agriculture and meteorology. Complex data are usually represented in metric spaces, where only the elements and the distances between them are available, which makes it impossible to represent trajectories considering a temporal dimension. In this paper we propose to map the metric data to multidimensional spaces so that we can estimate the element's status at a given time, based on known states of the same element. As it is not possible to create the complex data equivalent to its estimated position, we propose to apply similarity queries using this position as query center. We evaluated three types of similarity queries: k-NN, kAndRange and kAndRev.

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Isis C. O. S. Fogaça ◽  
Renato Bueno

Regardless of the data domain, there are applications that must track the temporal evolution of data elements. Based on the instances present in the database, the goal is to estimate the state of a given element at a different time instant from those available in the database. This kind of task is common in many database application domains, such as medicine, meteorology, agriculture, financial, and others. In content-based retrieval with complex data (such as images, sounds and videos), data are usually represented in metric spaces, where only the distances between elements are available. Without dimensional coordinates, it is not possible simply to add a time dimension for trajectory estimation in these spaces, as is the case in multidimensional spaces. In this article we propose to map the metric data to a multidimensional space so that we can estimate the element’s status at a given time instant, based on known states of the same element. As it is not possible to create the complex data equivalent to its estimated position in mapped space, we propose to apply similarity queries using this position as query center. Then, we estimate how this element would be, retrieving the real data elements present in the database that are close to the estimate. In this article, in addition to the nearest neighbor query (k-NN), we propose to use two other queries: kAndRange and kAndRev. With both methods, we aim to prune non-relevant elements from the query results, retrieving only the elements that are really close to the estimates. We present experiments with different query scenarios, evaluating the effects of varying input parameters of the proposed queries.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.


2020 ◽  
pp. 25-37
Author(s):  
Pere Freixa

Over the last two decades, digital journalism and interactive documentaries have produced works in which interactivity, multimedia, and participation articulate the access and consumption of information. These are basically multimedia and dynamic texts that delve into two-way communication and hypertext, and motivate active reading. These are informational pieces typical of the digital ecosystem that often mutate via social networks and present significant transformations in their temporal evolution. Reading, analyzing, and understanding these texts requires specific tools and methodologies that consider: (a) the dynamism of such pieces, as well as their temporal modification, (b) their multimodal dimension, and (c) their transmedia development. This article proposes a methodological reflection on the ways of reading interactive documentary audiovisual texts and proposes strategies and tools for their understanding and analysis based on detailed reading (close reading), and decoupage. This research focuses on an analysis of the temporal evolution of these journalistic pieces. The need to observe and analyze the temporal dimension of journalistic texts in the digital ecosystem has allowed the development of specific methodologies (Widholm, 2016; Karlsson; Sjøvaag, 2016; Buhl; Günther; Quandt, 2018) focused on the immediacy and mutability of journalistic news, its permanence in networks, and its temporal evolution. However, these tools do not consider the study of large-scale journalistic stories, typical of interactive documentaries, which require a specific multimodal approach (Hiippala, 2017; Van-Krieken, 2018, Freixa et al., 2014; Freixa, 2015). A detailed reading reveals how the interactive documentary considers the dimension, both temporal and of content and form, of the traditional documentary text, by becoming part of a transmedia framework as part of a dialogue with the public. Resumen Desde hace dos décadas, el periodismo digital y el documental interactivo produce obras en las que la interactividad, la multimedialidad y la participación articulan el acceso y consumo de la información. Básicamente se trata de textos multimediales y dinámicos, que ahondan en la comunicación bidireccional y el hipertexto, y que proponen lecturas activas. Se trata de piezas informacionales propias del ecosistema digital que, a menudo, mutan en las redes sociales y presentan significativas transformaciones en su evolución temporal. La lectura, el análisis y la comprensión de estos textos precisa de herramientas y metodologías específicas que contemplen: a) el dinamismo de las piezas, así como su modificación temporal; b) su dimensión multimodal y c) su desarrollo transmedia. En este artículo se propone una reflexión metodológica sobre las formas de lectura de los textos audiovisuales interactivos documentales, y se proponen estrategias y herramientas para su comprensión y análisis basadas en la lectura detallada (close reading), y el découpage. La investigación focaliza su interés en el análisis de la evolución temporal de estas piezas periodísticas. La necesidad de observar y analizar la dimensión temporal de los textos periodísticos en el ecosistema digital ha permitido el desarrollo de metodologías específicas (Widholm, 2016; Karlsson; Sjøvaag, 2016; Buhl; Günther; Quandt, 2018) focalizadas en la inmediatez y mutabilidad de la noticia periodística, su permanencia en red y evolución temporal. Estas herramientas, sin embargo, no contemplan el estudio de los relatos periodísticos de gran dimensión, propios del documental interactivo, que precisan de una aproximación multimodal específica (Hiippala, 2017; Van-Krieken, 2018, Freixa et al., 2014; Freixa, 2015). La lectura detallada permite observar cómo el documental interactivo cuestiona la dimensión, tanto temporal como de contenido y forma, del texto documental tradicional, al pasar a formar parte de un entramado transmedia en diálogo con el público.


2021 ◽  
Author(s):  
Gabriel P. Oliveira ◽  
Gabriel R. G. Barbosa ◽  
Bruna C. Melo ◽  
Mariana O. Silva ◽  
Danilo B. Seufitelli ◽  
...  

Music is an alive industry with an increasing volume of complex data that creates new challenges and opportunities for extracting knowledge, benefiting not only the different music segments but also the Music Information Retrieval (MIR) community. In this paper, we present MUHSIC, a novel dataset with enhanced information on musical success. We focus on artists and genres by combining chart-related data with acoustic metadata to describe the temporal evolution of musical careers. The enriched and curated data allow building success-based time series to investigate high-impact periods (hot streaks) in such careers, transforming complex data into knowledge. Overall, MUHSIC is a relevant tool in music-related tasks due to its easy use and replicability.


Temporal data clustering examines the time series data to determine the basic structure and other characteristics of the data. Many methodologies simply process the temporal dimension of data but it still faces the many challenges for extracting useful patterns due to complex data types. In order to analyze the complex temporal data, Hybridized Gradient Descent Spectral Graph and Local-Global Louvain Clustering (HGDSG-LGLC) technique are designed. The number of temporal data is gathered from input dataset. Then the HGDSG-LGLC technique performs graph-based clustering to partitions the vertices i.e. data into different clusters depending on similarity matrix spectrum. The distance similarity is measured between the data and cluster mean. The Gradient Descent function find minimum distance between data and cluster mean. Followed by, the Local-Global Louvain method performs the merging and filtering of temporal data to connect the local and global edges of the graph with similar data. Then for each data, the change in modularity is calculated for filtering the unwanted data from its own cluster and merging it into the neighboring cluster. As a result, optimal ‘k’ numbers of clusters are obtained with higher accuracy with minimum error rate. Experimental analysis is performed with various parameters like clustering accuracy ( ), error rate ( ), computation time ( ) and space complexity ( ) with respect to number of temporal data. The proposed HGDSG-LGLC technique achieves higher and lesser , minimum as well as than conventional methods.


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