A new and efficient algorithm to look for periodic patterns on spatio-temporal databases

2021 ◽  
pp. 1-10
Author(s):  
Claudio Gutiérrez-Soto ◽  
Tatiana Gutiérrez-Bunster ◽  
Guillermo Fuentes
Keyword(s):  
Efficient Algorithm ◽  
Complex Problem ◽  
Temporal Database ◽  
Periodic Patterns ◽  
Processing Times ◽  
Generic Term ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
Spatio Temporal ◽  
Data Incorporate

Big Data is a generic term that involves the storing and processing of a large amount of data. This large amount of data has been promoted by technologies such as mobile applications, Internet of Things (IoT), and Geographic Information Systems (GIS). An example of GIS is a Spatio-Temporal Database (STDB). A complex problem to address in terms of processing time is pattern searching on STDB. Nowadays, high information processing capacity is available everywhere. Nevertheless, the pattern searching problem on STDB using traditional Data Mining techniques is complex because the data incorporate the temporal aspect. Traditional techniques of pattern searching, such as time series, do not incorporate the spatial aspect. For this reason, traditional algorithms based on association rules must be adapted to find these patterns. Most of the algorithms take exponential processing times. In this paper, a new efficient algorithm (named Minus-F1) to look for periodic patterns on STDB is presented. Our algorithm is compared with Apriori, Max-Subpattern, and PPA algorithms on synthetic and real STDB. Additionally, the computational complexities for each algorithm in the worst cases are presented. Empirical results show that Minus-F1 is not only more efficient than Apriori, Max-Subpattern, and PAA, but also it presents a polynomial behavior.

RST: A Connectionist Architecture to Deal with Spatiotemporal Relationships

1998 ◽  
Vol 10 (4) ◽  
pp. 883-902 ◽  
Author(s):  
J.-C. Chappelier ◽  
A. Grumbach
Keyword(s):  
Three Dimensional ◽  
Video Images ◽  
Temporal Relationships ◽  
Leaky Integrator ◽  
Spatial Aspect ◽  
Actual Space ◽  
Temporal Aspect ◽  
Static Pattern ◽  
Spatio Temporal ◽  
Detection And Localization

In the past decade, connectionism has proved its efficiency in the field of static pattern recognition. The next challenge is to deal with spatiotemporal problems. This article presents a new connectionist architecture, RST (ŕeseau spatio temporel [spatio temporal network]), with such spatiotemporal capacities. It aims at taking into account at the architecture level both spatial relationships (e.g., as between neighboring pixels in an image) and temporal relationships (e.g., as between consecutive images in a video sequence). Concerning the spatial aspect, the network is embedded in actual space (two-or three-dimensional), the metrics of which directly influence its structure through a connection distribution function. For the temporal aspect, we looked toward biology and used a leaky-integrator neuron model with a refractory period and postsynaptic potentials. The propagation of activity by spatiotemporal synchronized waves enables RST to perform motion detection and localization in sequences of video images.

Web-based GIS technologies for monitoring and analysis of spatio-temporal processes

2016 ◽  
Vol 12 (1) ◽  
pp. 102-124 ◽  
Author(s):  
Valery Gitis ◽  
Alexander Derendyaev ◽  
Arkady Weinstock
Keyword(s):  
Critical Incidents ◽  
Temporal Data ◽  
Web Based ◽  
Content Type ◽  
Spatial Properties ◽  
Environmental Processes ◽  
Gis Technologies ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
Spatio Temporal

Purpose This paper aims to describe two Web-based technologies of geographic information systems (GIS) to be used in monitoring and analysis of environmental processes, proposed by the authors. The technologies analyze the temporal aspect of the process together with the spatial aspect, which defers them from most other works on environmental processes, as these are usually limited either to spatial statistics or to temporal statistics. The approach is instrumental in dynamically finding the relationships between the processes and predicting critical incidents. Design/methodology/approach Often, the study of natural processes is limited to the analysis of their spatial properties presented by individual time series. The principal idea of this approach consists in supplementing this traditional analysis with the analysis of time fields. In this way, the authors are able to analyze temporal and spatial properties of environmental processes together. Findings The paper presents two technologies which provide the analysis of spatial and temporal data obtained in natural environment monitoring. The discussed spatio-temporal data mining methods are shown to enable the research into environmental processes, and the solution of practical issues of critical situation forecasts. Originality/value The paper discussed Web-based GIS technologies for the analysis of the temporal aspect of the environmental process together with the spatial aspect. Application examples demonstrate the ability of this approach to find the relationships in dynamics of the processes and to predict critical incidents.

Aspect and space

2017 ◽  
Author(s):  
Lucas Champollion
Keyword(s):  
Spatial Domain ◽  
The Other ◽  
Alternative View ◽  
Temporal Domain ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
The Way

This chapter models the relation between temporal aspect (run for an hour vs. *run all the way to the store for an hour) and spatial aspect (meander for a mile vs. *end for a mile) previously discussed by Gawron (2009). The chapter shows that for-adverbials impose analogous conditions on the spatial domain and on the temporal domain, and that an event may satisfy stratified reference with respect to one of the domains without satisfying it with respect to the other one as well. This provides the means to extend the telic-atelic opposition to the spatial domain. The chapter argues in some detail that stratified reference is in this respect empirically superior to an alternative view of telicity based on divisive reference (Krifka 1998).

Analiza spaţio-temporară a duratei de strălucire a soarelui

2019 ◽  
Author(s):  
Rodica Cojocari ◽  
Keyword(s):  
Sunshine Duration ◽  
Meteorological Station ◽  
Upward Trend ◽  
Trend Line ◽  
Maximum Value ◽  
Increasing Trend ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
Seasonal Aspect ◽  
Annual Amount

In temporal aspect, the duration of sunshine shows a general upward trend. In seasonal aspect, spring season demonstrates an increase of about +0.2 hours, summer - about 0.1 hours, in autumn there is a trend of decreasing, according to the trend line, and the oscillation is equal to zero hours. Also we observe the increase in the number of hours in the spatial aspect. For Briceni meteorological station, the annual amount of sunshine duration varies within the limits of 1544 hours (1980) and 2326 hours (2015). The oscillation limits at the Cahul meteorological station are 1880 hours (1976) and 2604 hours (1963). At the Chisinau meteorological station, there's general increasing trend for the number of hours during which the sunshine duration is maintained, and it exhibits oscillations within 1783 hours (1989) and 2498 hours (1963). In the spatial aspect for winter, this increase +1 hour in Cahul, +0.38 hours in Briceni, and + 0.4 hours / season in Chisinau. In spring, the highest value of +2.2 hours is observed at Briceni and the lowest in Chisinau +1.9 hours / season. At Cahul meteorological station the increase was +1.4 hours. The increase in summer is +1.7 hours in Briceni and Chisinau, and only +0.9 hours in Cahul. Autumn trend line is a downward trend with a -0.3 hours decrease registered at Briceni and Chisinau meteorological stations (minimum values), and -0.6 hours in Cahul, maximum value.

Toward a Visual Query System for Spatio-Temporal Databases

2009 ◽  
pp. 987-1002
Author(s):  
Valéria M.B. Cavalcanti ◽  
Ulrich Schiel ◽  
Claudio de Souza Baptista
Keyword(s):  
Query Languages ◽  
Temporal Databases ◽  
Temporal Database ◽  
Database Queries ◽  
Integrated Environment ◽  
Visual Query ◽  
Query System ◽  
Spatio Temporal ◽  
Temporal Dimensions ◽  
Query Systems

Visual query systems (VQS) for spatio-temporal databases, which enable formulation of queries involving both spatial and temporal dimensions, are an important research subject. Existing results treat these dimensions separately and there are only a few integrated proposals. This chapter presents a VQS called spatio-temporal visual query environment (S-TVQE), which allows the formulation of conventional, spatial, temporal, and spatio-temporal database queries in an integrated environment. With S-TVQE, the user, instead of querying the database by textual query languages will interact with the system by visual operators for the statement of the query conditions. The tool provides a visualization of the results in different formats such as maps, graphics, and tables.

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