Graph Database using Data Crawling

Graph Database using Data Crawling

연구 분야: Databases

학회: 2020 IEEE Pune Section International Conference (PuneCon)

Data on the internet is growing nonstop. Every platform has millions of active users, searching for something specific. LinkedIn is one such platform, known for career opportunities, company and employee information and more. In this paper, we make a graph database system, collecting data from LinkedIn, IBM new feed, DNB. Using data crawling the data is gathered followed by data cleaning and, building APIs for graph database. A graph database is made of nodes and relationships, Cypher query language is used to store and retrieve the data from graph database. Neo4j and cypher query language are used for visual representation, with Neovis library. The system shows company details, employee details such as skills, experience, education background, contact information, certifications, licenses and more. The system is resourceful for companies and employees, provides easy and quick relevant information about the company and a person, such as a company’s employees, it’s blogs and articles, and further about the employee’s details. The project holds great future scope, with bigger, multiple sources.