Finding temporal features of event-oriented patterns

A major task of traditional temporal event sequence mining is to predict the occurrences of a special type of event (called target event) in a long temporal sequence. Our previous work has defined a new type of pattern, called event-oriented pattern, which can potentially predict the target event within a certain period of time. However, in the event-oriented pattern discovery, because the size of interval for prediction is pre-defined, the mining results could be inaccurate and carry misleading information. In this paper, we introduce a new concept, called temporal feature, to rectify this shortcoming. Generally, for any event-oriented pattern discovered under the pre-given size of interval, the temporal feature is the minimal size of interval that makes the pattern interesting. Thus, by further investigating the temporal features of discovered event-oriented patterns, we can refine the knowledge for the target event prediction.

Finding event-oriented patterns in long temporal sequences

A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.

Spatio-temporal evaluation of neck muscle activation during postural perturbations in healthy subjects

The purpose of this study was to examine the spatio-temporal activation of the sternocleidomastoid (SCM) and cervical extensor (CE) muscles with respect to the deltoid muscle onset during rapid voluntary upper limb movement in healthy volunteers. The repeatability and reliability of the spatio-temporal aspects of the myoelectric signals were also examined. Ten subjects performed bilateral and unilateral rapid upper limb flexion, abduction and extension in response to a visual stimulus. EMG onsets and normalised root mean square (nRMS) values were calculated for the SCM and CE muscles. Subjects attended three testing sessions over non-consecutive days allowing the repeatability and reliability of these measures to be assessed. The SCM and CE muscles demonstrated feed-forward activation (activation within 50 ms of deltoid onset) during rapid arm movements in all directions. The sequence and magnitude of neck muscle activation displayed directional specificity, however, the neck flexor and extensor muscles displayed co-activation during all perturbations. EMG onsets demonstrated high repeatability in terms of repeated measure precision (nSEM in the range 1.9-5.7%). This was less evident for the repeatability of nRMS values. The results of this study provide a greater understanding of cervical neuromotor control strategies. During bilateral and unilateral upper limb perturbations, the SCM and CE muscles demonstrate feed-forward co-activation. It seems apparent that feed-forward activation of neck muscles is a mechanism necessary to achieve stability for the visual and vestibular systems, whilst ensuring stabilisation and protection of the cervical spine. (C) 2004 Elsevier Ltd. All rights reserved.

The role of Temporal Context in the Tritone Paradox

The Tritone Paradox refers to a sequence of two specially synthesised "Shepard" tones which may sound ascending to one listener, and descending to another. The present study examines a recent suggestion that people's responses on this task may be determined by neural processes which are sensitive to temporal variations in pitch - so-called spectral motion detectors. Twelve listeners with normal hearing were presented with pairs of Shepard tones in each of two conditions - first in the traditional sequential manner, and then simultaneously, with one tone presented to each ear. Results indicated that respondents were able to judge consistent relationships between the tones even when presented simultaneously, and a high degree of similarity was observed between responses in each condition. The implications of these results for current theories of the Tritone Paradox are discussed.

Finding negative event oriented patterns in long temporal sequences

Pattern discovery in a long temporal event sequence is of great importance in many application domains. Most of the previous work focuses on identifying positive associations among time stamped event types. In this paper, we introduce the problem of defining and discovering negative associations that, as positive rules, may also serve as a source of knowledge discovery. In general, an event-oriented pattern is a pattern that associates with a selected type of event, called a target event. As a counter-part of previous research, we identify patterns that have a negative relationship with the target events. A set of criteria is defined to evaluate the interestingness of patterns associated with such negative relationships. In the process of counting the frequency of a pattern, we propose a new approach, called unique minimal occurrence, which guarantees that the Apriori property holds for all patterns in a long sequence. Based on the interestingness measures, algorithms are proposed to discover potentially interesting patterns for this negative rule problem. Finally, the experiment is made for a real application.

Introducing uncertainty into pattern discovery in temporal event sequences

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.