Name
Abstract | ||
|---|---|---|
Mobile phone service providers collect large volumes of data all over the globe. Taking into account that significant information is recorded in these datasets, there is a great potential for knowledge discovery. Since the processing pipeline contains several important steps, like data preparation, transformation, knowledge discovery, a holistic approach is required in order to avoid costly ETL operations across different heterogeneous systems. In this work, we present a design and implementation of knowledge discovery from CDR mobile phone data, using the Apache Spark distributed engine. We focus on the community detection problem which is extremely challenging and it has many practical applications. We have used Apache Spark with the Louvain community detection algorithm using a cluster of machines, to study the scalability and efficiency of the proposed methodology. The experimental evaluation is based on real-world mobile phone data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-98539-8_2 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Data mining,Big data analytics,Community detection | Data science,Spark (mathematics),Social network,Computer science,Service provider,Artificial intelligence,Knowledge extraction,Mobile phone,Data preparation,Big data,Machine learning,Scalability | Conference |
Volume | ISSN | Citations |
11031 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ciprianoctavian truica | 1 | 11 | 4.40 |
| Olivera Novovic | 2 | 1 | 0.34 |
| Sanja Brdar | 3 | 1 | 2.71 |
| Apostolos N. Papadopoulos | 4 | 752 | 101.60 |