Challenge Paper: The Vision for Time Profiled Temporal Association Mining

Abstract

Ecommerce has been the market disruptor in the modern world. Organizations have been focusing on mining enormous amounts of data to identify trends and extract crucial information from the voluminous data. Data is collected in databases and they are transactional in nature. Millions of transactions are collected in a temporal context. Also, organizations are transitioning towards NOSQL databases. The transactions are distributed into timeslots. Such a transaction data is called as timestamped temporal data. Along with the focus on mining the temporal data, extracting patterns, trends, and information, the implicit focus is also on building an efficient algorithm that is accurate with a reduction in the time taken, memory consumed, and computational efforts to scan the database. Temporal associations discovered from timestamped temporal datasets [1, 2] are known as time profiled temporal patterns. From application perspective, time profiled temporal