Spatio-Temporal OLAP Queries Similarity Measure and Algorithm

Spatio-temporal data warehouses store large volumes of consolidated and historized multidimensional data, to be explored and analyzed by various users in order to make the best decision. A spatio-temporal OLAP user interactively navigates a spatio-temporal data cube (Geo-cube) by launching a sequence of spatio-temporal OLAP queries (GeoMDX queries) in order to analyze the data. One important class of spatio-temporal analysis is computing spatio-temporal queries similarity. In this article, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. The problem of measuring spatio-temporal OLAP queries similarities has not been studied so far. Therefore, this article aims at filling this gap by proposing a new similarity measure and its corresponding algorithm. The proposed measure and algorithm can be used either in developing query recommendation, personalization systems or speeding-up query evolution. It takes into account the temporal similarity and the basic components of spatial similarity assessment relationships.

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Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v3i1.34 ◽

2016 ◽

Vol 3 (1) ◽

Author(s):

Mohammad Nur Shodiq ◽

Ali Ridho Barakbah ◽

Tri Harsono

Keyword(s):

Cluster Analysis ◽

Dimensional Space ◽

Temporal Analysis ◽

Optimal Number ◽

Multidimensional Data ◽

Temporal Data ◽

Seismic Parameters ◽

Visualization System ◽

Indonesian Archipelago ◽

Spatio Temporal

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters.

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Personalized Spatio-Temporal OLAP Queries Suggestion Based on User Behavior and a New Similarity Measure

Emerging Perspectives in Big Data Warehousing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-5516-2.ch005 ◽

2019 ◽

pp. 105-128

Author(s):

Olfa Layouni ◽

Jalel Akaichi

Keyword(s):

Probabilistic Model ◽

User Behavior ◽

Search Behavior ◽

Relevant Information ◽

Current User ◽

Data Cube ◽

Temporal Data ◽

Query Suggestion ◽

Spatio Temporal ◽

User Search Behavior

Spatio-temporal data warehouses store enormous amount of data. They are usually exploited by spatio-temporal OLAP systems to extract relevant information. For extracting interesting information, the current user launches spatio-temporal OLAP (ST-OLAP) queries to navigate within a geographic data cube (Geo-cube). Very often choosing which part of the Geo-cube to navigate further, and thus designing the forthcoming ST-OLAP query, is a difficult task. So, to help the current user refine his queries after launching in the geo-cube his current query, we need a ST-OLAP queries suggestion by exploiting a Geo-cube. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, first, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. Then, they propose a personalized query suggestion model based on users' search behavior, where they inject relevance between queries in the current session and current user' search behavior into a basic probabilistic model.

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A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining

Transportation Systems and Engineering ◽

10.4018/978-1-4666-8473-7.ch053 ◽

2015 ◽

pp. 1044-1063

Author(s):

Chengcui Zhang

Keyword(s):

Data Mining ◽

Temporal Analysis ◽

Volume Estimation ◽

Temporal Data Mining ◽

Temporal Data ◽

Traffic Surveillance ◽

Surveillance Videos ◽

Data Mining Algorithms ◽

Spatio Temporal ◽

Surveillance Applications

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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A Spatio-Temporal Analysis of the Health Situation in Poland Based on Functional Discriminant Coordinates

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031109 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1109

Author(s):

Mirosław Krzyśko ◽

Waldemar Wołyńki ◽

Marcin Szymkowiak ◽

Andrzej Wojtyła

Keyword(s):

Multivariate Time Series ◽

Complete Information ◽

Temporal Analysis ◽

Temporal Data ◽

Second Stage ◽

Provinces Of Poland ◽

Spatio Temporal ◽

Health Situation ◽

Two Stages ◽

Ward Method

The aim of this study was to investigate if the provinces of Poland are homogeneous in terms of the observed spatio-temporal data characterizing the health situation of their inhabitants. The health situation is understood as a set of selected factors influencing inhabitants’ health and the healthcare system in their area of residence. So far, studies concerning the health situation of selected territorial units have been based on data relating to a specific year rather than longer periods. The task of assessing province homogeneity was carried out in two stages. In stage one, the original spatio-temporal data space (space of multivariate time series) was transformed into a functional discriminant coordinates space. The resulting functional discriminant coordinates are synthetic measures of the health situation of inhabitants of particular provinces. These measures contain complete information regarding 8 diagnostic variables examined over a period of 6 years. In the second stage, the Ward method, commonly used in cluster analysis, was applied in order to identify groups of homogeneous provinces in the space of functional discriminant coordinates. Sixteen provinces were divided into four clusters. The homogeneity of the clusters was confirmed by the multivariate functional coefficient of variation.

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CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1387-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1387-1391 ◽

Cited By ~ 1

Author(s):

X. Wu ◽

R. Zurita-Milla ◽

M.-J. Kraak ◽

E. Izquierdo-Verdiguier

Keyword(s):

Clustering Algorithm ◽

Clustering Algorithms ◽

Temporal Patterns ◽

Data Cube ◽

Data Matrix ◽

Temporal Data Mining ◽

Temporal Data ◽

Complex Patterns ◽

Spatio Temporal ◽

Temporal Dimensions

As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.

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Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Data ◽

10.3390/data4030093 ◽

2019 ◽

Vol 4 (3) ◽

pp. 93 ◽

Cited By ~ 12

Author(s):

John Truckenbrodt ◽

Terri Freemantle ◽

Chris Williams ◽

Tom Jones ◽

David Small ◽

...

Keyword(s):

Regional Differences ◽

Open Data ◽

Data Cube ◽

Temporal Data ◽

Test Bed ◽

Digital Elevation ◽

The Alps ◽

Data Environment ◽

Spatio Temporal ◽

Sar Analysis

This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

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A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v4i1c.3114 ◽

2013 ◽

Vol 4 (1) ◽

pp. 146-150

Author(s):

Lax Maiah ◽

DR.A.GOVARDHAN DR.A.GOVARDHAN ◽

DR. C.SUNIL KUMAR

Keyword(s):

Data Mining ◽

Data Processing ◽

Data Warehouse ◽

Data Storage ◽

Multidimensional Data ◽

Metadata Management ◽

Temporal Data ◽

Application Layer ◽

Data Process ◽

Spatio Temporal

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

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