The motif tracking algorithm

Wilson, William; Birkin, Phil; Aickelin, Uwe

doi:10.1007/s11633-008-0032-0

Cited by 23 publications

(34 citation statements)

References 27 publications

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“…We direct the readers attention to [22] for a more in depth description of this algorithm if further information is required. The parameters required in the MTA include the length of a symbol s, the match threshold r, and the alphabet size a.…”

Section: Long and Short Term Memorymentioning

confidence: 99%

“…This process is repeated for all sliding windows across T to generate m-s+1 subsequences, each consisting of symbol strings comprising one character. Additional information on this process can be found in [22].…”

Section: Mtamentioning

confidence: 99%

“…The start locations of X and Y are also saved. For further information on the derivation and selection of this matching mechanism please refer to [22].…”

Section: Mtamentioning

confidence: 99%

“…No subsequent comparison of the sequences underlying those symbol strings is performed to confirm the existence of the motif. To incorporate this confirmation stage the same Euclidean distance function used in the MTA, as described in [22], is incorporated into Keogh's code. This ensures only genuine motifs are reported.…”

Section: Steamgen Datamentioning

confidence: 99%

“…From an initial scan by eye it is unclear whether any significant motifs exist, representing an ideal challenge for the two algorithms. The parameters for the MTA for this data set have been selected based on the results of previous testing [22,21]. An alphabet size a=6 was set in accordance with that selected by Keogh.…”

Section: Steamgen Datamentioning

confidence: 99%

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Motif Detection Inspired by Immune Memory

2011

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Abstract. The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of meaningful motifs in both cases, and the value of these motifs is discussed.

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Section: Long and Short Term Memorymentioning

confidence: 99%

Section: Mtamentioning

confidence: 99%

“…The start locations of X and Y are also saved. For further information on the derivation and selection of this matching mechanism please refer to [22].…”

Section: Mtamentioning

confidence: 99%

Section: Steamgen Datamentioning

confidence: 99%

Section: Steamgen Datamentioning

confidence: 99%

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Motif Detection Inspired by Immune Memory

2011

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Online behavior classification for anomaly detection in self‐x real‐time systems

Rammig

Stahl

2015

Concurrency and Computation

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SUMMARYAutonomous adaptation in self-adapting embedded real-time systems introduces novel risks as it may lead to unforeseen system behavior. An anomaly detection framework integrated in a real-time operating system can ease the identification of such suspicious novel behavior and, thereby, offers the potential to enhance the reliability of the considered self-x system. However, anomaly detection is based on knowledge about normal behavior. When dealing with self-reconfiguring applications, normal behavior changes. Hence, knowledge base requires adaptation or even re-construction at runtime. The stringent restrictions of real-time systems considering runtime and memory consumption make this task to a really challenging problem. We present our idea for online construction of application behavior knowledge that does not rely on training phase. The applications' behavior is defined by the application's system call invocations. For the knowledge base, we exploit suffix trees as they offer potentials to represent application behavior patterns and associated information in a compact manner. The online algorithm provided by suffix trees is a basis to construct the knowledge base with low computational effort. Anomaly detection and classification is integrated into the online construction method. New behavioral patterns do not unconditionally update the behavior knowledge base. They are evaluated in a context-related manner inspired by Danger Theory, a special discipline of artificial immune systems.

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Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data

McGarry

2022

Artificial Intelligence XXXIX

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Detecting unusual or interesting patterns in discrete symbol sequences is of great importance. Many domains consist of discrete sequential time-series such as internet traffic, online transactions, cyber-attacks, financial transactions, biological transcription, intensive care data and social sciences data such as career trajectories or residential history. The sequences usually consist of discrete symbols that may form regular patterns or motifs. We use regular expressions to construct the longest repeating sequences and subsequences that compose them, we then define these as motifs (which may or may not represent novel patterns). The sequences are now composed of simpler motifs which are used to build Hidden Markov Models models which can capture complex relationships based on location, frequency of occurrence and position. New data that deviates from established motifs either in location of appearance, frequency of appearance, or motif composition may represent patterns that may be different in some way and hence interesting to the user.

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The motif tracking algorithm

Cited by 23 publications

References 27 publications

Motif Detection Inspired by Immune Memory

Motif Detection Inspired by Immune Memory

Online behavior classification for anomaly detection in self‐x real‐time systems

Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data

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