Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Abstract

Finding periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find periodic-frequent patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because the real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world databases demonstrate that PF-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of PF-ECLAT with a case study on air pollution analytics.