Software systems for social data mining provide algorithms and tools for extracting useful knowledge from user-generated social media data. ParSoDA (Parallel Social Data Analytics) is a high-level library for developing parallel data mining applications based on the extraction of useful knowledge from large data set gathered from social media. The library aims at reducing the programming skills needed for implementing scalable social data analysis applications. To reach this goal, ParSoDA defines a general structure for a social data analysis application that includes a number of configurable steps and provides a predefined (but extensible) set of functions that can be used for each step. User applications based on the ParSoDA library can be run on both Apache Hadoop and Spark clusters. The paper describes the ParSoDA library and presents two social data analysis applications to assess its usability and scalability. Concerning usability, we compare the programming effort required for coding a social media application using versus not using the ParSoDA library. The comparison shows that ParSoDA leads to a drastic reduction (i.e., about 65%) of lines of code, since the programmer only has to implement the application logic without worrying about configuring the environment and related classes. About scalability, using a cluster with 300 cores and 1.2 TB of RAM, ParSoDA is able to reduce the execution time of such applications up to 85%, compared to a cluster with 25 cores and 100 GB of RAM.
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This work has been partially supported by the SMART Project, CUP J28C17000150006, funded by Regione Calabria (POR FESR-FSE 2014-2020), and by the ASPIDE Project funded by the European Unions Horizon 2020 research and innovation program under grant agreement No. 801091.
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Belcastro, L., Marozzo, F., Talia, D. et al. ParSoDA: high-level parallel programming for social data mining. Soc. Netw. Anal. Min. 9, 4 (2019). https://doi.org/10.1007/s13278-018-0547-5
- Social data analysis
- Parallel library
- Big data
- Social media
- Social networks