Building data storage and analytic backend services for listen online

Data storage and post-processing are among the most important data-related tasks. These tasks aim at keeping the data available and reusable in the long term so people can look into the data, manipulate the data and find valuable information that they need. The tasks can be more complex and difficult to deal with when the domain of the problem expands to the Big-Social World. In this case, the data could be nonuniform, which means the source of data is not limited to only one social media and the structure of the data could be variant. Therefore, traditional relational database management systems (RDBMSs) cannot properly work here to handle the unstructured data. This thesis introduces a system which integrates both Neo4j, a graph database, and MySQL, a traditional relational database, together to solve the unstructured social media data management problem mentioned above. The system has been integrated in Listen Online, or Lion for short, to handle the real problems. For convenience, we call the system Lion Backend. Lastly, by building upon some existing libraries, Lion-Backend provides graphical interfaces to users to help them easily build their queries and apply analysis functions to their data.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Hu, Zhaoheng

Contributors dc:contributor

• Chang, Kevin

• Database
• Data processing
• Listen Online

Statement dc:rights

• Copyright 2018 Zhaoheng Hu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/102958

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/102958