Name
Abstract | ||
|---|---|---|
Last decade has witnessed the dramatic expansion of online user-generated content. Making full use of this data to discover behaviour patterns has become an increasingly appealing research topic. In this pilot study, a big data analytic framework is proposed, particularly taking streaming data from students' activity on their laptop usage as an illustrative example. Three modules are implemented to harvest raw streaming records, storage heterogeneous data, and apply the fuzzy representation and rule-mining algorithm for a modelling purpose. The efficiency of the proposed framework is then evaluated using a nationwide streaming dataset. The exploratory simulation of results demonstrates the flexibility and applicability of the proposed framework for processing complex streaming data, and revealing patterns from digital engagement which be used to inform decision makers. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3014812.3014869 | ACSW |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Yang | 1 | 28 | 10.66 |
| Jun Ma | 2 | 12 | 10.74 |
| Sarah K. Howard | 3 | 0 | 1.01 |
| Matthew Ciao | 4 | 0 | 0.34 |
| Rangan Srikhanta | 5 | 0 | 0.34 |