Abstract: The present invention provides a data searching method of database, apparatus and computer program for the same. The data searching apparatus of a database includes: a storage unit configured to store graph data; and a controller configured to store physical location information in which vertex and edge information included in the graph data is stored in the storage unit, and perform a search for the vertex and the edge based on the physical location information. Thereby, it is possible to efficiently search the data by improving a search speed of a graph, minimize the update of the information even when a storage location of the data is changed, and facilitate query by the graph data regardless of types of the database and a storage structure, and efficiently use a storage space.

Abstract: Provided are data processing apparatus and methods for predicting a pattern frequency thereof. The data processing apparatus includes: a storage unit adapted to store a preset graph pattern, an occurrence frequency of the preset graph pattern, and information on actual data nodes corresponding to each node which belongs to the graph pattern; and a control unit adapted to compare the preset graph pattern with a graph pattern to be searched to perform a graph search, and when the preset graph pattern is not included in the graph to be searched, search for a node shared by two different preset graph patterns which are stored in the storage unit to perform the graph search. By this configuration, it is possible to predict a level of an intermediate result to optimize a graph query and effectively calculate sizes of various graphs patterns while making a capacity of a memory small.

Abstract: The present invention provides a data searching method of database, apparatus and computer program for the same. The data searching apparatus of a database includes: a storage unit configured to store graph data; and a controller configured to store physical location information in which vertex and edge information included in the graph data is stored in the storage unit, and perform a search for the vertex and the edge based on the physical location information. Thereby, it is possible to efficiently search the data by improving a search speed of a graph, minimize the update of the information even when a storage location of the data is changed, and facilitate query by the graph data regardless of types of the database and a storage structure, and efficiently use a storage space.

Abstract: Provided are a data processing apparatus and a data mapping method thereof. The data processing apparatus according to an present invention includes: a storage unit adapted to store relational data; a mapping unit adapted to store mapping information for mapping a schema of a relational database having the relational data stored therein to a node or an edge of a graph database; and a control unit adapted to convert the relational data into graph data by the mapping unit. By this configuration, it is possible to easily transfer the data of the relational database into the graph database, query the relational database using a query language used in a graph data model, and visualize the relational database in a graph represented by a node and an edge.

Abstract: Provided are data processing apparatus and methods for predicting a pattern frequency thereof. The data processing apparatus includes: a storage unit adapted to store a preset graph pattern, an occurrence frequency of the preset graph pattern, and information on actual data nodes corresponding to each node which belongs to the graph pattern; and a control unit adapted to compare the preset graph pattern with a graph pattern to be searched to perform a graph search, and when the preset graph pattern is not included in the graph to be searched, search for a node shared by two different preset graph patterns which are stored in the storage unit to perform the graph search. By this configuration, it is possible to predict a level of an intermediate result to optimize a graph query and effectively calculate sizes of various graphs patterns while making a capacity of a memory small.