Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

Time perception: the bad news and the good.

Time perception is fundamental and heavily researched, but the field faces a number of obstacles to theoretical progress. In this advanced review, we focus on three pieces of 'bad news' for time perception research: temporal perception is highly labile across changes in experimental context and task; there are pronounced individual differences not just in overall performance but in the use of different timing strategies and the effect of key variables; and laboratory studies typically bear little relation to timing in the 'real world'. We describe recent examples of these issues and in each case offer some 'good news' by showing how new research is addressing these challenges to provide rich insights into the neural and information-processing bases of timing and time perception. WIREs Cogn Sci 2014, 5:429-446. doi: 10.1002/wcs.1298 This article is categorized under: Psychology > Perception and Psychophysics Neuroscience > Cognition.This is the final published version. It originally appeared at http://onlinelibrary.wiley.com/doi/10.1002/wcs.1298/abstract, published by Wiley

A Partitioning Algorithm for Detecting Eventuality Coincidence in Temporal Double recurrence

A logical theory of regular double or multiple recurrence of eventualities, which are regular patterns of occurrences that are repeated, in time, has been developed within the context of temporal reasoning that enabled reasoning about the problem of coincidence. i.e. if two complex eventualities, or eventuality sequences consisting respectively of component eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k and all eventualities are of fixed durations, is there a subinterval of k over which the occurrence xp and yq for p between 1 and r and q between 1 and s coincide. We present the ideas behind a new algorithm for detecting the coincidence of eventualities xp and yq within a cycle of the double recurrence of x and y. The algorithm is based on the novel concept of gcd partitions that requires the partitioning of each of the incidences of both x and y into eventuality sequences each of which components have a duration that is equal to the greatest common divisor of the durations of x and y. The worst case running time of the partitioning algorithm is linear in the maximum of the duration of x and that of y, while the worst case running time of an algorithm exploring a complete cycle is quadratic in the durations of x and y. Hence the partitioning algorithm works faster than the cyclical exploration in the worst case

A general state-based temporal pattern recognition

Time-series and state-sequences are ubiquitous patterns in temporal logic and are widely used to present temporal data in data mining. Generally speaking, there are three known choices for the time primitive: points, intervals, points and intervals. In this thesis, a formal characterization of time-series and state-sequences is presented for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. In addition, subsequence matching is addressed to associate the state-sequences, where both non-temporal aspects as well as rich temporal aspects including temporal order, temporal duration and temporal gap should be taken into account. Firstly, based on the typed point based time-elements and time-series, a formal characterization of time-series and state-sequences is introduced for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. A time-series is formalized as a tetrad (T, R, Tdur, Tgap), which denotes: the temporal order of time- elements; the temporal relationship between time-elements; the temporal duration of each time-element and the temporal gap between each adjacent pair of time-elements respectively. Secondly, benefiting from the formal characterization of time-series and state-sequences, a general similarity measurement (GSM) that takes into account both non-temporal and rich temporal information, including temporal order as well as temporal duration and temporal gap, is introduced for subsequence matching. This measurement is general enough to subsume most of the popular existing measurements as special cases. In particular, a new conception of temporal common subsequence is proposed. Furthermore, a new LCS-based algorithm named Optimal Temporal Common Subsequence (OTCS), which takes into account rich temporal information, is designed. The experimental results on 6 benchmark datasets demonstrate the effectiveness and robustness of GSM and its new case OTCS. Compared with binary-value distance measurements, GSM can distinguish between the distance caused by different states in the same operation; compared with the real-penalty distance measurements, it can filter out the noise that may push the similarity into abnormal levels. Finally, two case studies are investigated for temporal pattern recognition: basketball zone-defence detection and video copy detection. In the case of basketball zone-defence detection, the computational technique and algorithm for detecting zone-defence patterns from basketball videos is introduced, where the Laplacian Matrix-based algorithm is extended to take into account the effects from zoom and single defender‘s translation in zone-defence graph matching and a set of character-angle based features was proposed to describe the zone-defence graph. The experimental results show that the approach explored is useful in helping the coach of the defensive side check whether the players are keeping to the correct zone-defence strategy, as well as detecting the strategy of the opponent side. It can describe the structure relationship between defender-lines for basketball zone-defence, and has a robust performance in both simulation and real-life applications, especially when disturbances exist. In the case of video copy detection, a framework for subsequence matching is introduced. A hybrid similarity framework addressing both non-temporal and temporal relationships between state-sequences, represented by bipartite graphs, is proposed. The experimental results using real-life video databases demonstrated that the proposed similarity framework is robust to states alignment with different numbers and different values, and various reordering including inversion and crossover

Repeated Web Page Visits and the Scanpath Theory: A Recurrent Pattern Detection Approach

This paper investigates the eye movement sequences of users visiting web pages repeatedly. We are interested in potential habituation due to repeated exposure. The scanpath theory posits that every person learns an idiosyncratic gaze sequence on first exposure to a stimulus and re-applies it on subsequent exposures. Josephson and Holmes (2002) tested the applicability of this hypothesis to web page revisitation but results were inconclusive. With a recurrent temporal pattern detection technique, we examine additional aspects and expose scanpaths. Results do not suggest direct applicability of the scanpath theory. While repetitive scan patterns occurred and were individually distinctive, their occurrence was variable, there were often several different patterns per person, and patterns were not primarily formed on the first exposure. However, extensive patterning occurred for some participants yet not for others which deserves further study into its determinants