Abstract
Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precisely or approximately. Precise Matching is also known as Exact Matching such as (sub)Graph Isomorphism and Approximate Matching is called Inexact Matching in which matching activity concerns with conceptual/semantic matching rather than focusing on structural details of graphs. In this article, a review of matching problem is presented i.e. Semantic Matching (conceptual), Syntactic Match-ing (structural) and Schematic Matching (Schema based). The aim is to present the current state of the art in Large Scale Graph Matching (LSGM), a systematic review of algorithms, tools and techniques along with the existing challenges of LSGM. Moreover, the potential application domains and related research activities are provided.
Highlights
In this era of big data, graphs are considered as data representation tool that is capable for holding large scale attributed data and the relationships among data entities
The outline followed in this paper is as: in section 2 it is discussed, how a data model can be represented as a graph model
We present a general mapping tree of height 3, considering the fact that tree is a specialized form of graph and it is possible to map data from one model to another
Summary
In this era of big data, graphs are considered as data representation tool that is capable for holding large scale attributed data and the relationships among data entities. It has been proven that graphs can represent structural information in the form of attributed objects (vertices) and their relationships (edges) in an efficient manner. Isomorphism belongs to the NP-complete class of problems and is used for strictest matching of graphs which is conceptually applicable but could not scale well for large graphs[8]. The outline followed in this paper is as: in section 2 it is discussed, how a data model can be represented as a graph model.
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