Multi-dimensional sequential pattern mining

Pinto, Helen; Pei, Jian; Wang, Ke; Chen, Qiming; Dayal, Umeshwar

doi:10.1145/502598.502600

Cited by 53 publications

(74 citation statements)

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“…The focus on user control of the sequential pattern mining process in terms of applying constraints [8], and in a querying-type approach [7] has also been explored. The introduction of multi-dimensional databases has given rise to multi-dimensional pattern mining [20] which applies the same techniques to more dimensions than just one.…”

Section: Related Workmentioning

confidence: 99%

Discovering Sentinel Rules for Business Intelligence

Middelfart¹,

Pedersen

2009

Lecture Notes in Computer Science

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For additional information, see the DB Tech Reports homepage: dbtr.cs.aau.dk . Any software made available via DB Tech Reports is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose.The DB Tech Reports icon is made from two letters in an early version of the Rune alphabet, which was used by the Vikings, among others. Runes have angular shapes and lack horizontal lines because the primary storage medium was wood, although they may also be found on jewelry, tools, and weapons. Runes were perceived as having magic, hidden powers. The first letter in the logo is "Dagaz," the rune for day or daylight and the phonetic equivalent of "d." Its meanings include happiness, activity, and satisfaction. The second letter is "Berkano," which is associated with the birch tree. Its divinatory meanings include health, new beginnings, growth, plenty, and clearance. It is associated with Idun, goddess of Spring, and with fertility. It is the phonetic equivalent of "b." Abstract This paper proposes the concept of sentinel rules for multi-dimensional data that warns users when measure data concerning the external environment changes. For instance, a surge in negative blogging about a company could trigger a sentinel rule warning that revenue will decrease within two months, so a new course of action can be taken. Hereby, we expand the window of opportunity for organizations and facilitate successful navigation even though the world behaves chaotically. Since sentinel rules are at the schema level as opposed to the data level, and operate on data changes as opposed to absolute data values, we are able to discover strong and useful sentinel rules that would otherwise be hidden when using sequential pattern mining or correlation techniques. We present a method for sentinel rule discovery and an implementation of this method that scales linearly on large data volumes.

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Section: Related Workmentioning

confidence: 99%

Discovering Sentinel Rules for Business Intelligence

Middelfart¹,

Pedersen

2009

Lecture Notes in Computer Science

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“…For this work, we chose a sequential pattern mining algorithm that we have developed [8], as it combines several features from other algorithms such as accepting time constraints [11], processing databases with dimensional information [17], eliminating redundancy [20,18], and also because it offers some original features such as accepting symbols with numeric values [8]. We describe next some basic features of the algorithm.…”

Section: Mining Temporal Patterns From Sequences Of Eventsmentioning

confidence: 99%

“…Our solution to this issue is to take advantage of an extra feature of our algorithm (based on [17]), which is to add dimensional information to sequences. A database having a set of dimensions D = D1, D2, ...Dn is called an MD-Database.…”

Section: The Observing Phasementioning

confidence: 99%

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

Fournier-Viger

Nkambou

Faghihi

et al. 2009

Data Mining and Multi-Agent Integration

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Abstract. We propose two mechanisms for agent learning based on the idea of mining temporal patterns from agent behavior. The first one consists of extracting temporal patterns from the perceived behavior of other agents accomplishing a task, to learn the task. The second learning mechanism consists in extracting temporal patterns from an agent's own behavior. In this case, the agent then reuses patterns that brought self-satisfaction. In both cases, no assumption is made on how the observed agents' behavior is internally generated. A case study with a real application is presented to illustrate each learning mechanism.

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“…The design of efficient algorithms for mining sequential patterns has been extensively studied in the past decade ( [24,1,8,3,2]). Recent work on sequential pattern mining has focused on the problem of reducing the amount of discovered patterns by introducing user control mechanisms into the mining process rather than pruning uninteresting patterns only during a post-processing phase.…”

Section: Related Workmentioning

confidence: 99%

“…Different kinds of sequential patterns and more general temporal patterns ( [4,5,6,7,8]) have been proposed, as well as general formalisms and algorithms for expressing and mining these patterns have been developed ( [9,10]). Most of these patterns are specified by formalisms which are, in some extent, reducible to Propositional Temporal Logic.…”

Section: Introductionmentioning

confidence: 99%