Challenge Paper: The Vision for Time Profiled Temporal Association Mining

V. Radhakrishna, G. Reddy, Puligadda Veereswara Kumar, V. Janaki
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引用次数: 5

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
挑战论文:时序时序关联挖掘的愿景
电子商务一直是现代世界的市场破坏者。组织一直专注于挖掘大量数据,以确定趋势并从大量数据中提取关键信息。数据收集在数据库中,它们本质上是事务性的。在一个临时上下文中收集了数百万个事务。此外,组织正在向NOSQL数据库过渡。事务被分配到时间段中。这样的事务数据称为带有时间戳的时态数据。除了关注挖掘时态数据、提取模式、趋势和信息外,隐含的重点还在于构建一种高效的算法,该算法可以准确地减少扫描数据库所花费的时间、内存消耗和计算工作量。从时间戳时间数据集[1,2]中发现的时间关联被称为时间剖面时间模式。从应用程序的角度来看,时间轮廓时态
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