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Journal ArticleDOI

T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data

10 Aug 2010-Sensors (Multidisciplinary Digital Publishing Institute (MDPI))-Vol. 10, Iss: 8, pp 7496-7513
TL;DR: This work extends the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences, to find patterns in temporal event data, with a statistical model to obtain a fast and robust algorithm.
Abstract: The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.

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Citations
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Journal ArticleDOI
TL;DR: The vulnerabilities and threats facing modern critical infrastructures with special emphasis on industrial control systems are explored, and a number of protection measures are described.

232 citations


Cites background from "T-Patterns Revisited: Mining for Te..."

  • ...In fact, data-mining [82] techniques can be leveraged to predict and discover new behavior patterns through specific techniques, such as sequential patterns [76] or time series and statistical analysis [15]....

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Journal ArticleDOI
TL;DR: In this work, topic models are employed to learn the latent structure and dynamics of sensor network data and have shown the ability to find routines of activity over sensor networkData in office environments.
Abstract: Recent advances on sensor network technology provide the infrastructure to create intelligent environments on physical places. One of the main issues of sensor networks is the large amount of data they generate. Therefore, it is necessary to have good data analysis techniques with the aim of learning and discovering what is happening on the monitored environment. The problem becomes even more challenging if this process is performed following an unsupervised way without having any a priori information and applied over a long-term timeline with many sensors. In this work, topic models are employed to learn the latent structure and dynamics of sensor network data. Experimental results using two realistic datasets, having over 50weeks of data, have shown the ability to find routines of activity over sensor network data in office environments.

17 citations


Cites methods from "T-Patterns Revisited: Mining for Te..."

  • ...Salah et al., 2010 reviewed the existing techniques for the discovery of temporal patterns in sensor data and proposed a modified T-Pattern algorithm (Magnusson, 2000)....

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Journal ArticleDOI
TL;DR: A novel approach for introducing long-range interactions based on sequential pattern mining and enriching sensor-based representations with the mined patterns allows improving results over sequential and segmental labeling algorithms in most of the cases.
Abstract: Activity recognition is a key task for the development of advanced and effective ubiquitous applications in fields like ambient assisted living. A major problem in designing effective recognition algorithms is the difficulty of incorporating long-range dependencies between distant time instants without incurring substantial increase in computational complexity of inference. In this paper we present a novel approach for introducing long-range interactions based on sequential pattern mining. The algorithm searches for patterns characterizing time segments during which the same activity is performed. A probabilistic model is learned to represent the distribution of pattern matches along sequences, trying to maximize the coverage of an activity segment by a pattern match. The model is integrated in a segmental labeling algorithm and applied to novel sequences, tagged according to matches of the extracted patterns. The rationale of the approach is that restricting dependencies to span the same activity segment (i.e., sharing the same label), allows keeping inference tractable. An experimental evaluation shows that enriching sensor-based representations with the mined patterns allows improving results over sequential and segmental labeling algorithms in most of the cases. An analysis of the discovered patterns highlights non-trivial interactions spanning over a significant time horizon.

16 citations

Proceedings ArticleDOI
13 Oct 2011
TL;DR: The work presented here aims to answer the question: Using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data and shows the power of the LDA model in extracting relevant patterns from sensor network data.
Abstract: The work presented here aims to answer this question: Using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data? Sensor measurements are grouped to form artificial words (activities) and documents (set of activities). The goal is to infer the latent topics which are assumed to be the common routines from the observed data. An unsupervised probabilistic model, namely the Latent Dirichlet Allocation (LDA), is applied to automatically discover the latent topics (routines) in the data. Experimental results using real logged data of 24 weeks from an office building, with different number of topics, are shown. The results show the power of the LDA model in extracting relevant patterns from sensor network data.

15 citations


Cites methods from "T-Patterns Revisited: Mining for Te..."

  • ...A review of temporal pattern mining algorithms used to discover patterns in sensor networks is provided in [12]....

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Journal ArticleDOI
Zhaoyuan Yu1, Linwang Yuan1, Wen Luo1, Linyao Feng1, Guonian Lv1 
30 Dec 2015-Sensors
TL;DR: A geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data and can effectively extract all possible trajectories of the human motion, which makes it more accurate.
Abstract: Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

13 citations


Cites background or methods from "T-Patterns Revisited: Mining for Te..."

  • ...Due to such reasons, the statistical analysis of the human motion pattern from the PIR motion sensors also has considerable uncertainties [14]....

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  • ...The temporal patterns of the human motion in the MERL dataset were analyzed by T-Pattern algorithm [13,14]....

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References
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Journal ArticleDOI
TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.

10,264 citations


"T-Patterns Revisited: Mining for Te..." refers methods in this paper

  • ...For instance in recurrent neural networks, the temporal dimension is modelled with the help of context units [20]....

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Journal ArticleDOI
TL;DR: A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.
Abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.

2,567 citations

Proceedings ArticleDOI
02 Apr 2001
TL;DR: This work proposes a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern Mining, and shows that Pre fixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.
Abstract: Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of A priori which may substantially reduce the number of combinations to be examined. Howeve6 Apriori still encounters problems when a sequence database is large andor when sequential patterns to be mined are numerous ano we propose a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern mining. Prefixspan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover; prefi-projection substantially reduces the size of projected databases and leads to efJicient processing. Our performance study shows that Prefixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.

1,975 citations

Journal ArticleDOI
TL;DR: This work gives efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and presents detailed experimental results that are in use in telecommunication alarm management.
Abstract: Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present detailed experimental results. The methods are in use in telecommunication alarm management.

1,593 citations


"T-Patterns Revisited: Mining for Te..." refers methods in this paper

  • ...In particular, the WINEPI algorithm [27] and its extensions have a similar formulation with the T-pattern algorithm, and the two approaches can be contrasted for their merits and drawbacks in real and simulated data....

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  • ...In [27] the WINEPI algorithm is proposed, where a fixed-length temporal window is used to filter out cohesive episodes, followed by a threshold-based selection....

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Proceedings ArticleDOI
21 Sep 2008
TL;DR: This paper presents an easy to install sensor network and an accurate but inexpensive annotation method and shows how the hidden Markov model and conditional random fields perform in recognizing activities.
Abstract: A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.

873 citations


"T-Patterns Revisited: Mining for Te..." refers background in this paper

  • ..., in the home of an elderly person [15]), the real challenge of the problem is the existence of multiple causes, triggering unrelated events one after the other....

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