Discovery of Association Rules in Temporal Databases

Tansel, Abdullah Uz; Imberman, Susan P.

doi:10.1109/itng.2007.78

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…Different technologies have been used to deal with temporal sequences, including sequential pattern mining [4,16,18,27,31,34,43], association rules [19,35], and classification [25]. Sequential pattern mining was the research field that attracted most scholars' attention.…”

Section: Related Workmentioning

confidence: 99%

Discovering multi-label temporal patterns in sequence databases

Chen

Wang

2011

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Discovering multi-label temporal patterns in sequence databases

Chen

Wang

2011

Information Sciences

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“…Though the temporal database technology has made great progress in recent years (Snodgrass 2007;Tansel and Imberman 2007), the application of temporal information processing technology still has problems due to lack of temporal software development tools. Though the temporal database technology has made great progress in recent years (Snodgrass 2007;Tansel and Imberman 2007), the application of temporal information processing technology still has problems due to lack of temporal software development tools.…”

Section: Binding Semantics Of Now Variablementioning

confidence: 99%

Temporal Information Processing Technology and Its Application

Tang¹,

Ye²,

Tang³

2010

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The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: Frido Steinen-Broo, EStudio Calamar, SpainPrinted on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) i Preface Time is a natural attribute of everything. With the explosive growth of computer and network systems, temporal information has received extensive attention in both academia and industry. It plays an increasingly important role in the new generation information systems and also a key role in some applications. The use of temporal information modeling and processing technology in these applications can make them more useful and more convenient.Temporal database and application problems have been mentioned during the 1970s. The groundbreaking study in this area was conducted by J. Ben Zvi, who proposed the bitemporal concept and a temporal database model in his dissertation, submitted to the University of California, Los Angeles, in 1982. In subsequent years, the temporal database theory research has grown vigorously and hundreds of temporal models have been proposed. James Clifford, Christian S. Jensen, Richard T. Snodgrass and Andreas Steiner made important contributions to temporal database models, theory and technology. In the recent years, along with information technology that can meet the increasing requirement for new applications, the temporal database theory and application technologies have made remarkable progress. However, there are many problems in temporal information processing, e.g., weakness in temporal calculus theory, low efficiency of temporal storage and access, complex temporal information processing and lack of the software development tools. There are three main trends in temporal technologies: model standardization, middleware development and application diversification.We began to pay special attention to research on temporal database when we undertook the software application project: Intelligent Decision Support System of Salary (SIDSS), in 1998. The main concept behind SIDSS is that an employee's wage is paid according to information related to the employee and to the policies of the salary management department. SIDSS is a typical temporal system, in which the employee information that influences his or her salary is the typical temporal data and the salary policies that can be changed by the management department which also are time-varying knowledge. In the SIDSS, we used a ii temporal database model to design the employee database. We proposed a rule-based temporal knowledge model to represent the time-varying salary policies, and implemented a reasoning mechanism to realize the employee's salary determination and change based on the employee's temporal information and the salary policies based on temporal rule knowledge. The SIDSS has been used by more tha...

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“…We would like to find interesting association rules with time constraints in such a temporal database. This problem is referred to as temporal association rule mining [Ale and Rossi 2000;Bettini et al 1998;Chen and Petr 2000;Chen et al 1998;Harms and Deogun 2004;Jiang and Gruenwald 2006;Lee et al 2003Lee et al , 2001aTansel and Ayan 1998]. In contrast, sequential pattern mining algorithms focus on time relationship of itemsets in a sequence database.…”

Section: Twain: Two-end Association Miner With Precise Exhibition Permentioning

confidence: 99%

“…There are some prior works exploring the problem of finding temporal association rules, that is, discovering association rules from a given subset of database specified by time [Ale and Rossi 2000;Bettini et al 1998;Chen and Petr 2000;Chen et al 1998;Harms and Deogun 2004;Jiang and Gruenwald 2006;Lee et al 2001a;Tansel and Ayan 1998]. The temporal transaction database is divided into several partitions according to the time granularity imposed.…”

Section: Related Workmentioning

confidence: 99%

Twain

Huang

Dai

Chen

2007

ACM Trans. Knowl. Discov. Data

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We investigate the general model of mining associations in a temporal database, where the exhibition periods of items are allowed to be different from one to another. The database is divided into partitions according to the time granularity imposed. Such temporal association rules allow us to observe short-term but interesting patterns that are absent when the whole range of the database is evaluated altogether. Prior work may omit some temporal association rules and thus have limited practicability. To remedy this and to give more precise frequent exhibition periods of frequent temporal itemsets, we devise an efficient algorithm Twain (standing for TWo end AssocIation miNer.) Twain not only generates frequent patterns with more precise frequent exhibition periods, but also discovers more interesting frequent patterns. Twain employs Start time and End time of each item to provide precise frequent exhibition period while progressively handling itemsets from one partition to another. Along with one scan of the database, Twain can generate frequent 2-itemsets directly according to the cumulative filtering threshold. Then, Twain adopts the scan reduction technique to generate all frequent k-itemsets (k > 2) from the generated frequent 2-itemsets. Theoretical properties of Twain are derived as well in this article. The experimental results show that Twain outperforms the prior works in the quality of frequent patterns, execution time, I/O cost, CPU overhead and scalability.

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Discovery of Association Rules in Temporal Databases

Cited by 16 publications

References 11 publications

Discovering multi-label temporal patterns in sequence databases

Discovering multi-label temporal patterns in sequence databases

Temporal Information Processing Technology and Its Application

Twain

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