Analytic Methods for Spatio-Temporal Data in a Nature-Inspired Data Model

A. Madraky(1*), Z. A. Othman(2), A. R. Hamdan(3)

(1) Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia (UKM), Malaysia., Malaysia
(2) Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia (UKM), Malaysia., Malaysia
(3) Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia (UKM), Malaysia., Malaysia
(*) Corresponding author

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We are surrounded by information and much of it needs to be stored and analysed. Data analysis would be easier if the data storage structure were closer to that of a natural data structure. Many storage structures and related methods have been proposed in recent years due to the importance of understanding spatio-temporal information associated with a particular place and time. In this paper, some of the most important analytic methods for spatio-temporal data are considered and categorized in terms of their algorithms. We also describe the difficulties of knowledge representation when dealing with spatio-temporal data. In addition, three of the analytic functions of theHair-oriented Data Model are defined, which is a nature-inspired solution. These analytic functions are implemented in Oracle and tested on climate change data as a case study. The main objectives of this research are to propose a model to achieve better knowledge representation, provide the capability to expand queries through additional analytical attributes and reduce redundancy, and thereby obtain better integrity and consistency in spatio-temporal databases
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Data Engineering; Data Mining; Spatio-Temporal Data Models; Analytic Methods; Hair-Oriented Data Model

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P. Compieta, S. Di Martino, M. Bertolotto, F. Ferrucci, T. Kechadi, Exploratory spatio-temporal data mining and visualization, Journal of Visual Languages & Computing, 18 (2007) 255-279.

Ma, C., Gao, X.-D., Zeng, Z.-W., A spatial data mining method based on the concept lattice of compact dependencies, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 341-34.

F. Petitjean, C. Kurtz, N. Passat, P. Gançarski, Spatio-temporal reasoning for the classification of satellite image time series, Pattern Recognition Letters, 33 (2012) 1805-1815.

D.H. Kim, K.H. Ryu, H.S. Kim, A spatiotemporal database model and query language, Journal of Systems and Software, 55 (2000) 129-149.

D.H. Kim, K.H. Ryu, C.H. Park, Design and implementation of spatiotemporal database query processing system, Journal of Systems and Software, 60 (2002) 37-49.

O. Ahlqvist, H. Ban, N. Cressie, N.Z. Shaw, Statistical counterpoint: Knowledge discovery of choreographic information using spatio-temporal analysis and visualization, Applied Geography, 30 (2010) 548-560.

J. Mennis, D. Guo, Spatial data mining and geographic knowledge discovery—An introduction, Computers, Environment and Urban Systems, 33 (2009) 403-408.

W. Hsu, M.L. Lee, J. Wang, Temporal and spatio-temporal data mining, IGI Pub., 2008.

X.-D. Zhu, Z.-Q. Huang, Conceptual modeling rules extracting for data streams, Knowledge-Based Systems, 21 (2008) 934-940.

A.R. Post, J.H. Harrison Jr, Temporal data mining, Clinics in Laboratory Medicine, 28 (2008) 83-100;

Feng, J., Liu, Y., Research and application on data stream management system of expressway real-time monitoring system, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 282-28.

K. Kaneiwa, A rough set approach to multiple dataset analysis, Applied Soft Computing, 11 (2011) 2538-2547.

G. Del Mondo, M. Rodríguez, C. Claramunt, L. Bravo, R. Thibaud, Modeling consistency of spatio-temporal graphs, Data & Knowledge Engineering, 84 (2013) 59-80.

J. Poelmans, S.O. Kuznetsov, D.I. Ignatov, G. Dedene, Formal Concept Analysis in Knowledge Processing: A Survey on Models and Techniques, Expert Systems with Applications, (2013).

Z. Neji Ben Salem, L. Boougrain, F. Alexandre, Spatio-temporal biologically inspired models for clean and noisy speech recognition, Neurocomputing, 71 (2007) 131-136.

H.-L. Wei, S.A. Billings, Y. Zhao, L. Guo, An adaptive wavelet neural network for spatio-temporal system identification, Neural Networks, 23 (2010) 1286-1299.

N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition, Neural Networks, (2012).

J. Zahradnik, M. Skrbek, Spatio-temporal data classification using CVNNs, Simulation Modelling Practice and Theory, (2013).

L.-F. Lin, Y.-K. Huang, Scalable processing of continuous< i> K-nearest neighbor queries with uncertain velocity, Expert Systems with Applications, 38 (2011) 9256-9265.

C.-C. Tseng, J.-C. Chen, C.-H. Fang, J.-J. James Lien, Human action recognition based on graph-embedded spatio-temporal subspace, Pattern Recognition, 45 (2012) 3611-3624.

B. Haworth, E. Bruce, K. Iveson, Spatio-temporal analysis of graffiti occurrence in an inner-city urban environment, Applied Geography, 38 (2013) 53-63.

T. Beaubouef, F.E. Petry, R. Ladner, Spatial data methods and vague regions: A rough set approach, Applied Soft Computing, 7 (2007) 425-440.

Yao, S., Li, L., Ma, M., Upper and lower approximations of rough sets induced by matrix transformation, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2696-270.

C.-H. Lee, Mining spatio-temporal information on microblogging streams using a density-based online clustering method, Expert Systems with Applications, 39 (2012) 9623-9641.

C. Strauss, M.B. Rosa, S. Stephany, Spatio-temporal clustering and density estimation of lightning data for the tracking of convective events, Atmospheric Research, (2013).

G. Georgoulas, A. Konstantaras, E. Katsifarakis, C.D. Stylios, E. Maravelakis, G.J. Vachtsevanos, “Seismic-Mass” Density-based Algorithm for Spatio-Temporal Clustering, Expert Systems with Applications, (2013).

D. Birant, A. Kut, ST-DBSCAN: An algorithm for clustering spatial–temporal data, Data & Knowledge Engineering, 60 (2007) 208-221.

B. Chakraborty, M.B. Holte, T.B. Moeslund, J. Gonzàlez, Selective spatio-temporal interest points, Computer Vision and Image Understanding, 116 (2012) 396-410.

E. Salazara, D.B. Dunsonb, L. Carina, Analysis of Space-Time Relational Data with Application to Legislative Voting.

E. Winarko, J.F. Roddick, ARMADA–an algorithm for discovering richer relative temporal association rules from interval-based data, Data & Knowledge Engineering, 63 (2007) 76-90.

M. Shaheen, M. Shahbaz, A. Guergachi, Context based positive and negative spatio-temporal association rule mining, Knowledge-Based Systems, (2012).

B. Petelin, I. Kononenko, V. Malačič, M. Kukar, Multi-level association rules and directed graphs for spatial data analysis, Expert Systems with Applications, (2013).

G. Serpen, Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization, Mathematics and computers in simulation, 64 (2004) 279-293.

M. De Lozzo, P. Klotz, B. Laurent, Multilayer perceptron for the learning of spatio-temporal dynamics—application in thermal engineering, Engineering Applications of Artificial Intelligence, (2013).

S. Shivshankar, A. Jamalipour, Spatio-temporal multicast grouping for content-based routing in vehicular networks: A distributed approach, Journal of Network and Computer Applications, (2013).

Noumer, S.K., Dowaji, S., Knowledge representation approach to measure distributed systems performance, (2013) International Review on Computers and Software (IRECOS), 8 (7), pp. 1727-173.

M. Genero, G. Poels, M. Piattini, Defining and validating metrics for assessing the understandability of entity–relationship diagrams, Data & Knowledge Engineering, 64 (2008) 534-557.

C. Ramirez, B. Valdes, A General Knowledge Representation Model of Concepts.

Y.-H. Wang, Image indexing and similarity retrieval based on spatial relationship model, Information Sciences, 154 (2003) 39-58.

A. Buccella, A. Cechich, D. Gendarmi, F. Lanubile, G. Semeraro, A. Colagrossi, Building a global normalized ontology for integrating geographic data sources, Computers & Geosciences, 37 (2011) 893-916.

E. Clementini, P.D. Felice, D. Hernández, Qualitative representation of positional information, Artificial Intelligence, 95 (1997) 317-356.

Huang, W., Li, Y., A probabilistic fuzzy set for uncertainties-based integration inference, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1392-139.

G. Tsafnat, The Field Representation Language, Journal of Biomedical Informatics, 41 (2008) 46-57.

A. Madraky, Z.A. Othman, A.R. Hamdan, Hair data model: A new data model for Spatio-Temporal data mining, in: Data Mining and Optimization (DMO), 2012 4th Conference on, IEEE, 2012, pp. 18-22.

UCI, El Nino Data in: D.o. Statistics, I.S. University (Eds.), Iowa 1999.


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