Ontology Matching for Spatial Data Retrieval from Internet Portals

2005 ◽  
pp. 166-182 ◽  
Author(s):  
Hartwig H. Hochmair
Keyword(s):  
Spatial Data ◽  
Data Retrieval ◽  
Ontology Matching ◽  
Internet Portals

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

Distributed Intelligence for Constructing Economic Models

2012 ◽  
pp. 1538-1550
Author(s):  
Ting Yu
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Intelligent System ◽  
Economic Models ◽  
Data Retrieval ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Distributed Intelligence ◽  
Large Size ◽  
The Matrix

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

Distributed Intelligence for Constructing Economic Models

2010 ◽  
Vol 1 (3) ◽  
pp. 20-33
Author(s):  
Ting Yu
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Intelligent System ◽  
Economic Models ◽  
Data Retrieval ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Distributed Intelligence ◽  
Large Size ◽  
The Matrix

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

A spatial data retrieval and image processing expert system for the world wide web

1997 ◽  
Vol 33 (1-2) ◽  
pp. 433-436 ◽  
Author(s):  
Wendolin Bosques ◽  
Ricardo Rodríguez ◽  
Angélica Rondón ◽  
Ramón Vásquez
Keyword(s):  
Image Processing ◽  
Expert System ◽  
World Wide Web ◽  
Spatial Data ◽  
World Wide ◽  
Data Retrieval ◽  
The World

Application of the Agent-Based Ontology Matching Technology in the Relational Database

2013 ◽  
Vol 846-847 ◽  
pp. 1701-1706
Author(s):  
Jian Long Ding
Keyword(s):  
Data Storage ◽  
Relational Database ◽  
Data Retrieval ◽  
Domain Ontology ◽  
Ontology Matching ◽  
Semantic Matching ◽  
Semantic Retrieval ◽  
Matching Problem ◽  
Specific Level ◽  
Quantitative Calculation

As the relational database be the main data storage mode, but the traditional keyword based syntactic matching defects in precision and recall. This paper provides a ontology matching mechanisms for relational database, which realizes the semantic level of the data retrieval by using the intelligent search technology from Agent and Ontology. The mechanism using DCL domain ontology, SQC services query policy, SearchPolicyMap mapping to solve the problems as data object description, retrieval conditions description, and the mapping between those two description. And provided the preconditions for the semantic matching under the relational database. Solve the semantic matching problem of interaction between human and Agent by the WI interface and CM mechanism . Solve the problem of interaction between Agent and relational database by the service customization Interface SBI. Finally, solve the problem of semantic retrieval and quantitative calculation by querying adapter QA and core algorithm CMA. The mechanism has a strong practicality and application domains independent. Can implement a specific level semantics of relational database retrieval through the application domain ontology creation and mapping configuration.

scholarly journals DESIGN OF THE MAPPING OF ORANG UTAN WITH SPATIAL DATA APPROACH TO WEB-BASED GEOGRAPHIC INFORMATION SYSTEMS

2018 ◽  
Vol 12 (2) ◽  
pp. 38-46
Author(s):  
Jadiaman Parhusip ◽  
Rony Teguh ◽  
Liyando Hermawan Hasibuan
Keyword(s):  
Spatial Data ◽  
Interface Design ◽  
Data Retrieval ◽  
Structure Design ◽  
Nest Density ◽  
Web Based ◽  
Pongo Abelii ◽  
Library Studies ◽  
Meet Data ◽  
Map Service

Orangutans are the only great apes that live in Asia and have the most threatenedstatus in the world. Currently there are three types of orangutans in Indonesia; the Sumatranorangutan (Pongo abelii), the Borneo orangutan (Pongo pygmaeus) and the Tapanuliorangutan (Pongo tapanuliensi). The types of colors that enter critical status are endangered(critical) based on International Unity for Nature Conservation in 2017. Approximately 75%of the distribution of orangutans will be outside the sexuality area need data distributionand orangutan population to meet data needs for the benefit of orangutans.This study used a method to collect data which were of library studies, data retrieval,consultation and discussion studies. After the data was collected, it was conducted needanalysis and system design using the W2000 Software Development Method, which includedprocess design, database design, menu structure design and interface design. Then thesystem implementation utilized Google Map Service and the PHP programming languagebased on the website with the MySQL database.The results of the testing are several locations of orangutan nests where every pointowns information in the form of the number of orangutans and their population. The resultsof this study are the mapping of orangutan’s nests with a GIS web-based spatial dataapproach that will provide information on nest position, nest density, orangutan populationdensity which is at the position of the nest radius.

SOLD: A node-Splitting algorithm for R-tree based on Objects’ Locations Distribution

2018 ◽  
Vol 45 (2) ◽  
pp. 169-195 ◽  
Author(s):  
Esam Al-Nsour ◽  
Azzam Sleit ◽  
Mohammad Alshraideh
Keyword(s):  
Spatial Data ◽  
Real Data ◽  
Data Retrieval ◽  
Quality Factors ◽  
Splitting Algorithm ◽  
Data Indexing ◽  
Disk Space ◽  
Data Files ◽  
Balanced Tree ◽  
Tree Data Structure

Spatial data indexing methods are of extreme importance as they massively build up as a result of the explosive growth in capturing data with spatial features. No matter how much the data size is, eventually it will reside on disk pages. Disk pages have to be properly indexed to preserve spatial properties of objects, optimise disk space usage and improve objects’ retrieval performance. One of the most popular spatial data indexes is the R-tree which is a height balanced tree data structure, where leaf nodes resemble disk pages and contain pointers to objects’ locations. A single tree node can host up to a maximum number of objects, where any more insertion makes it an overflown node and it has to be split. Better splits lead to better index performance and more utilisation of disk space. In this work, we introduce a new way of finding the most proper split for an overflown node in the R-tree index. The proposed work scans – in a linear cost – the overflown node’s objects once to identify the distribution of objects’ locations (minimum bounding rectangles (MBRs)) in relative to its node’s bounding rectangle (node’s MBR). It uses objects’ locations to calculate – for each main axis – the split quality factors: expected overlap between resulting nodes, objects distribution evenness among resulting nodes and the perimeter of resulting nodes. The axis with better combined quality factors values is selected as the split axis. The Splitting based on Objects’ Locations Distribution (SOLD) algorithm was implemented and tested against two other splitting algorithms, experiments using synthetic and real data files showed good results and it outperformed both algorithms in index creation tests and data retrieval tests.

scholarly journals The use of CBERS (China-Brazil Earth Resources Satellite) to trace the dynamics of total suspended matter at an urbanized coastal area

2017 ◽  
Vol 65 (2) ◽  
pp. 309-323
Author(s):  
Maria Fernanda Colo Giannini ◽  
Joseph Harari ◽  
Aurea Maria Ciotti
Keyword(s):  
Satellite Data ◽  
Spatial Data ◽  
Suspended Matter ◽  
Data Retrieval ◽  
Coastal Areas ◽  
Total Suspended Matter ◽  
Southeastern Brazil ◽  
Inorganic Particles ◽  
Quality Of Water

ABSTRACT The distribution of organic and inorganic particles in the water column, or the total suspended matter (TSM), responds to local and remote oceanographic and meteorological processes, potentially impacting biogeochemical cycles. In shallow coastal areas, where particles have distinct origins and compositions and vary in different time scales, the use of remote sensing tools for monitoring and tracing this material is highly encouraged due to the high temporal and spatial data resolution. The objective of this work was to understand the variability of in situ TSM at Santos Bay (Southeastern Brazil) and its response to oceanographic and meteorological conditions. We also aimed to verify the applicability of the satellite data from CBERS-2 sensor in order to map the dynamics of TSM in this region. Our results have shown that the distribution of TSM in Santos Bay varied consistently with winds, currents and tidal cycles, with significant relationships emphasizing the role of south-western winds and spring tides. Neap tides and eastern winds, along with rainfall, play an important role in the input of organic matter into the bay. In conclusion, our analyses showed that the main patterns observed in situ regarding the responses of TSM to the ocean-meteorological processes could be reproduced in the CBERS-2 satellite data, after simple and standard methods of images processing. TSM data retrieval from CBERS-2 or other satellite sensors were shown to be feasible, becoming an essential tool for synoptic observations of the composition and quality of water, especially at urbanized and impacted coastal areas.

scholarly journals Analisis Lokasi Penjualan Berbasis Data Geografis Menggunakan Perangkat Lunak Google Map API Studi Kasus PT. Johnson Home Hygiene Product

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Erick Harlest Budi R
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Data Storage ◽  
Spatial Data ◽  
Data Exchange ◽  
Geographic Location ◽  
Data Retrieval ◽  
Geographic Information ◽  
Geographic Data ◽  
Google Map Api

Abstrak: Sistem Informasi Geografis sebagai sistem komputer yang digunakan untuk memanipulasi data spasial. Sistem ini dapat diimplementasikan dengan perangkat keras dan perangkat lunak yang berfungsi untuk akuisisi dan verifikasi data, kompilasi data, penyimpanan data, perubahan dan pembaruan manajemen data dan pertukaran data, manipulasi data, pemanggilan dan penyajian data serta analisis data. Definisi Sistem Informasi Geografis adalah dasar penelitian ini dalam menerapkan analisis berbasis geografis menggunakan perangkat lunak Google Map API. Sampel dipilih di dalam dan sekitar wilayah Cibubur dengan populasi yang terjangkau. Proses pengambilan data dari 2.755 toko dalam format gambar diambil menggunakan Garmin 550. Pemrosesan data ini menghasilkan pemetaan lokasi geografis yang divisualisasikan melalui koneksi Internet dan diunggah ke Google Map sehingga koordinat toko atau outlet dapat divisualisasikan dengan bantuan Browser Web. Dengan memvisualisasikan lokasi koordinat akan membantu manajemen dalam menganalisis penjualan berdasarkan data geografis.Kata kunci: Analisis Lokasi Penjualan, Data Geografis dan Google Map APIAbstract: Geographic Information Systems as a computer system that is used to manipulate spatial data. This system is implementable with hardware and software that works for acquisition and verification of data, data compilation, data storage, changes and updates to data management and data exchange, data manipulation, calling and data presentation and analysis of data. Definition of Geographic Information Systems is the basis of this research in applying geographic-based analysis using the Google Map API software. Samples were selected in and around the area Cibubur, an affordable population. The data retrieval process of 2755 stores in image format was taken using a Garmin 550.The processing of this data produced geographic location mapping visualized through an Internet connection and uploaded to Google Maps so that the coordinates of the stores or outlets can be visualized with the help of a Web Browser. By visualizing the location of the coordinates will assist management in analyzing geographic data based on salesKeywords: Sales Location Analysis, Geographic Data, and Google Map API

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