scholarly journals Analysing Temporal Evolution of Complex Data Using Similarity Queries

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Isis C. O. S. Fogaça ◽  
Renato Bueno
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Temporal Evolution ◽  
Real Data ◽  
Time Instant ◽  
Multidimensional Space ◽  
Complex Data ◽  
Time Dimension ◽  
Similarity Queries ◽  
Data Elements

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.

scholarly journals Temporal Evolution of Complex Data

2020 ◽  
Author(s):  
Isis Caroline Oliveira de Sousa Fogaça ◽  
Renato Bueno
Keyword(s):  
Metric Spaces ◽  
Temporal Evolution ◽  
Complex Data ◽  
Temporal Dimension ◽  
Similarity Queries

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.

scholarly journals Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence

Jurnal INFORM ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Evy Kamilah Ratnasari
Keyword(s):  
Feature Recognition ◽  
Nearest Neighbor ◽  
Fruit Size ◽  
Texture Features ◽  
Real Data ◽  
K Nearest Neighbor ◽  
Education Industry ◽  
Basic Features ◽  
Computer Recognition ◽  
Color And Texture Features

Abstract — Fruit recognition can be automatically applied to the field of education, industry, sales, as well as science. In the vision of computer recognition of fruit relies on four basic features that describe the characteristics of the fruit, i.e., size, color, shape, and texture. The fruit recognition through the RGB image results of cameras using the features of shape and size are not reliable and effective, because in a real data image can be composed of several different sizes of fruit on each type of fruit so it can't be identified morphologically the fruit size and uniformity that can affect the results of the classification. This journal based on the feature recognition method of building colors and textures for the classification of fruit.The classification is done by K-Nearest Neighbor based on color and texture features co-occurrence. Experimental results of 1882 dataset image of fruit for 12 different classes can recognize the fruit in both color and texture features based with the highest accuracy of 92%.

scholarly journals Practical Construction of k-Nearest Neighbor Graphs in Metric Spaces

2006 ◽  
pp. 85-97 ◽  
Author(s):  
Rodrigo Paredes ◽  
Edgar Chávez ◽  
Karina Figueroa ◽  
Gonzalo Navarro
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

scholarly journals WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 413 ◽  
Author(s):  
Chris Lytridis ◽  
Anna Lekova ◽  
Christos Bazinas ◽  
Michail Manios ◽  
Vassilis G. Kaburlasos
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Human Robot Interaction ◽  
Time Series Classification ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Knn Classifier ◽  
Benchmark Datasets

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

scholarly journals ON SPATIAL CONSENSUS FORMATION: IS THE SZNAJD MODEL DIFFERENT FROM A VOTER MODEL?

2003 ◽  
Vol 14 (10) ◽  
pp. 1331-1354 ◽  
Author(s):  
LAXMIDHAR BEHERA ◽  
FRANK SCHWEITZER
Keyword(s):  
Computer Simulations ◽  
Nearest Neighbor ◽  
Temporal Evolution ◽  
Relaxation Times ◽  
Nearest Neighbors ◽  
Voter Model ◽  
Consensus Formation ◽  
Spatio Temporal ◽  
Update Rules

In this paper, we investigate the so-called "Sznajd Model" (SM) in one dimension, which is a simple cellular automata approach to consensus formation among two opposite opinions (described by spin up or down). To elucidate the SM dynamics, we first provide results of computer simulations for the spatio-temporal evolution of the opinion distribution L(t), the evolution of magnetization m(t), the distribution of decision times P(τ) and relaxation times P(μ). In the main part of the paper, it is shown that the SM can be completely reformulated in terms of a linear voter model (VM), where the transition rates towards a given opinion are directly proportional to frequency of the respective opinion of the second-nearest neighbors (no matter what the nearest neighbors are). So, the SM dynamics can be reduced to one rule, "Just follow your second-nearest neighbor". The equivalence is demonstrated by extensive computer simulations that show the same behavior between SM and VM in terms of L(t), m(t), P(τ), P(μ), and the final attractor statistics. The reformulation of the SM in terms of a VM involves a new parameter σ, to bias between anti- and ferromagnetic decisions in the case of frustration. We show that σ plays a crucial role in explaining the phase transition observed in SM. We further explore the role of synchronous versus asynchronous update rules on the intermediate dynamics and the final attractors. As compared to the original SM, we find three additional attractors, two of them related to an asymmetric coexistence between the opposite opinions.

Kinetic paths of B2 and DO3 order parameters: Theory

1989 ◽  
Vol 4 (5) ◽  
pp. 1132-1139 ◽  
Author(s):  
L. Anthony ◽  
B. Fultz
Keyword(s):  
Time Dependence ◽  
Nearest Neighbor ◽  
Temporal Evolution ◽  
Initial Conditions ◽  
Interatomic Interaction ◽  
Order Parameters ◽  
Rate Theory ◽  
Bcc Lattice ◽  
Activated State ◽  
State Rate

It is shown that a binary alloy with an AB3 stoichiometry on a bcc lattice may develop various combinations of B2 and DO3 order along its kinetic path toward equilibrium. The temporal evolution of these two order parameters is analyzed with an activated-state rate theory. Individual vacancy jumps are treated in a master equation formalism that involves first-nearest-neighbor (1nn) and second-nearest-neighbor (2nn) interactions. In our formulation, a set of coupled differential equations is obtained describing the time-dependence of six order parameters. These equations were integrated numerically for a variety of interatomic interactions and initial conditions. It was found that the relative rates of B2 and DO3 ordering, and hence the path of the alloy through the space spanned by the B2 and DO3 order parameters, depend on the relative strengths of the interatomic interaction potentials and on the temperature. For very strong 2nn interactions, a transient B32 structure was observed to develop at early times, although this phase disappeared as equilibrium was approached.

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