• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.22-24
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• 2022

Recently, Graph Database (GDB) is being used in wide range of industrial fields. GDB is a database system which adopts graph structure for storing the information. GDB handles the information in the form of a graph which consists of vertices and edges. In contrast to the relational database system which requires pre-defined table schema, GDB doesn't need a pre-defined structure for storing data, allowing a very flexible way of thinking about and using the data. With GDB, we can handle a large volume of heavily interconnected data. A network service provider provides its services based on the heavily interconnected communication network facilities. In many cases, their information is hosted in relational database, where it is not easy to process a query that requires recursive graph traversal operation. In this study, we suggest a way to store an example set of interconnected network facilities in GDB, then show how to graph-query them efficiently.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.15-22
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• 2008

In this paper, we propose an efficient algorithm using only one-time database scan. The proposed algorithm creates the bipartite graph which indicates relationship of large items and transactions including the large items. And then we can find large itemsets using the bipartite graph. The bipartite graph is generated when database is scanned to find large items. We can't easily find transactions which include large items in the large database. In the bipartite graph, large items and transactions are linked each other. So, we can trace the transactions which include large items through the link information. Therefore the bipartite graph is a indexed database which indicates inclusion relationship of large items and transactions. We can fast find large itemsets because proposed method conducts only one-time database scan and scans indexed the bipartite graph. Also, it don't generate candidate itemsets.

• ETRI Journal
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• v.44 no.5
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.533-538
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• 2001

In this paper, we extend FARGs single mode attribute to multiple attributes for real image application and present a new CBIR using FMARG(Fuzzy Multiple Attribute Relational Graph), which can handle queries involving multiple attributes, not only object label, but also color, texture and spatial relation. In the experiment using the synthetic image database of 1,024 images and the natural image database of 1.026 images built from NETRA database and Corel Draw, the proposed approach shows 6~30% recall increase in the synthetic image database and a good performance, at the displacements and the retrieved number of similar images in the natural image database, compared with the single attribute approach.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.991-998
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• 2002

With the advent of XML and database languages armed with the object-oriented concept and deductive logic, the problem of efficient query processing for them has become a major issue. We describe a way of processing semi-structured XML data through an implementation of a Deductive and Object-oriented Database (DOODB) language with the explanation of query processing. We have shown how to convert an XML data model to a DOODB data model. We have then presented an efficient query processing method based on Connection Graph Resolution. We also present a knowledge-based query processing method that uses the homomorphism of objects in the database and the associative rule of substitutions.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• Journal of Platform Technology
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• v.9 no.4
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• pp.3-9
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• 2021

In this paper, a graph data model is introduced to effectively recognize the situation between each object on the road detected by vehicles or road infrastructure sensors. The proposed method builds a graph database by modeling each object on the road as a node of the graph and the relationship between objects as an edge of the graph, and updates object properties and edge properties in real time. In this case, the relationship between objects represented as edges is set when there is a possibility of approach between objects in consideration of the position, direction, and speed of each object. Finally, we propose a spatial indexing technique for graph nodes and edges to update the road object graph database represented through the proposed graph modeling method continuously in real time. To show the superiority of the proposed indexing technique, we compare the proposed indexing based database update method to the non-indexing update method through simulation. The results of the simulation show the proposed method outperforms more than 10 times to the non-indexing method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.411-419
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• 2013

Research on developing algorithms for building modeling such as extracting outlines of the buildings and segmenting patches of the roofs using aerial images or LiDAR data are active. However, utilizing information from the building model is not well implemented yet. This study aims to propose a scheme for search identical or similar shape of buildings by utilizing graph topology pattern matching under the assumptions: (1) Buildings were modeled beforehand using imagery or LiDAR data, or (2) 3D building data from digital maps are available. Side walls, segmented roofs and footprints were represented as nodes, and relationships among the nodes were defined using graph topology. Topology graph database was generated and pattern matching was performed with buildings of various shapes. The results show that efficiency of the proposed method in terms of reliability of matching and database structure. In addition, flexibility in the search was achieved by altering conditions for the pattern matching. Furthermore, topology graph representation could be used as scale and rotation invariant shape descriptor.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.12-36
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• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.