Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A case study in weather pattern searching using a spatial data warehouse model
Download
index.pdf
Date
2008
Author
Köylü, Çağlar
Metadata
Show full item record
Item Usage Stats
268
views
163
downloads
Cite This
Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be included in the analysis phases of a decision support system (DSS). In this regard, this study aims to search for answers to the problem how to design a spatially enabled data warehouse architecture in order to support spatio-temporal data analysis and exploration of multidimensional data. Consequently, in this study, the concepts of OLAP and GISs are synthesized in an integrated fashion to maximize the benefits generated from the strengths of both systems by building a spatial data warehouse model. In this context, a multidimensional spatio-temporal data model is proposed as a result of this synthesis. This model addresses the integration problem of spatial, non-spatial and temporal data and facilitates spatial data exploration and analysis. The model is evaluated by implementing a case study in weather pattern searching.
Subject Keywords
Computer science.
,
Geodesy.
URI
http://etd.lib.metu.edu.tr/upload/2/12609573/index.pdf
https://hdl.handle.net/11511/18258
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
An application of the minimal spanning tree approach to the cluster stability problem
Volkovich, Z.; Barzily, Z.; Weber, Gerhard Wilhelm; Toledano-Kitai, D.; Avros, R. (Springer Science and Business Media LLC, 2012-03-01)
Among the areas of data and text mining which are employed today in OR, science, economy and technology, clustering theory serves as a preprocessing step in the data analyzing. An important component of clustering theory is determination of the true number of clusters. This problem has not been satisfactorily solved. In our paper, this problem is addressed by the cluster stability approach. For several possible numbers of clusters, we estimate the stability of the partitions obtained from clustering of samp...
An approach for landslide risk assesment by using geographic information systems (gis) and remote sensing (rs)
Erener, Arzu; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2009)
This study aims to develop a Geographic Information Systems (GIS) and Remote Sensing (RS) Based systematic quantitative landslide risk assessment methodology for regional and local scales. Each component of risk, i.e., hazard assessment, vulnerability, and consequence analysis, is quantitatively assessed for both scales. The developed landslide risk assessment methodology is tested at Kumluca watershed, which covers an area of 330 km2, in Bartın province of the Western Black Sea Region, Turkey. GIS and RS t...
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
An approach to the mean shift outlier model by Tikhonov regularization and conic programming
TAYLAN, PAKİZE; Yerlikaya-Oezkurt, Fatma; Weber, Gerhard Wilhelm (IOS Press, 2014-01-01)
In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically c...
A pattern classification approach for boosting with genetic algorithms
Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09)
Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Ç. Köylü, “A case study in weather pattern searching using a spatial data warehouse model,” M.S. - Master of Science, Middle East Technical University, 2008.