Search results for "Temporal database"
showing 10 items of 12 documents
Proceedings of the 16th International Symposium on Spatial and Temporal Databases
2019
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology
2008
Analyzing spatiotemporal dependencies between different types of events is highly relevant to many biological phenomena (e.g., signaling and trafficking), especially as advances in probes and microscopy have facilitated the imaging of dynamic processes in living cells. For many types of events, the segmented areas can overlap spatially and temporally, forming random clumps. In this paper, we model the binary image sequences of two different event types as a realization of a bivariate temporal random set and propose a nonparametric approach to quantify spatial and spatiotemporal interrelations using the pair correlation, cross-covariance, and the Ripley K functions. Based on these summary st…
Testing the effects of temporal data resolution on predictions of the effects of climate change on bivalves
2014
a b s t r a c t The spatial-temporal scales on which environmental observations are made can significantly affect our perceptions of ecological patterns in nature. Understanding potential mismatches between environmen- tal data used as inputs to predictive models, and the forecasts of ecological responses that these models generate are particularly difficult when predicting responses to climate change since the assumption of model stationarity in time cannot be tested. In the last four decades, increases in computational capacity (by a factor of a million), and the evolution of new modeling tools, have permitted a corresponding increase in model complexity, in the length of the simulations,…
Temporal drivers of liking
2013
Abstract Generally liking is measured overall but is likely to vary over the food intake, like sensory perception. Replacing the attributes in Temporal Dominance of Sensations (TDS) by the categories of a usual ordinal liking scale makes it possible to monitor liking changes while eating a product (Sudre et al., 2012). This methodology allows for a better understanding of the influence of temporal dominance of sensations on liking and liking evolution over the time of product intake. Thus, it is possible to associate hedonic temporal data and descriptive temporal data (TDS profiles), which would identify drivers of liking, that is attributes which, when cited as dominant, would lead to a de…
A Semantic Model to Query Spatial–Temporal Data
2013
There is a growing need for the study of spatial–temporal objects and their relationships. A common approach for this task is the use of relational databases, which unfortunately do not allow inference. In this research, we introduce a new approach that uses the concept of a “continuum” together with ontologies and semantic Web technologies. The continuum allows us to define parent–child relationships between representations of objects. It also allows us to compare the evolution of two different objects and establish the relationships between them along time. Our approach is based on the four-dimensional fluent, which is extended to obtain spatial–temporal qualitative information from the a…
SILKNOWViz: Spatio-Temporal Data Ontology Viewer
2019
Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The grap…
Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Ka…
2013
Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investiga…
Healthcare trajectory mining by combining multidimensional component and itemsets
2012
Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $ \lbrace $ Radiography, Appendectomy $\rbrace$) and…