Pattern mining for query answering in marine sensor data

2011 ◽  
Author(s):  
Md. Sumon Shahriar ◽  
Paulo de Souza ◽  
Greg Timms
Keyword(s):  
Pattern Mining ◽  
Query Answering ◽  
Sensor Data

scholarly journals Smart Query Answering for Marine Sensor Data

Sensors ◽  
2011 ◽  
Vol 11 (3) ◽  
pp. 2885-2897 ◽  
Author(s):  
Md. Sumon Shahriar ◽  
Paulo De Souza ◽  
Greg Timms
Keyword(s):  
Query Answering ◽  
Sensor Data

scholarly journals A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets

2019 ◽  
Vol 9 (4) ◽  
pp. 2993
Author(s):  
Nehal A. Sakr ◽  
Mervat Abu-ElKheir ◽  
A. Atwan ◽  
H. H. Soliman
Keyword(s):  
Fuzzy Sets ◽  
Activity Recognition ◽  
Pattern Mining ◽  
Scoring Function ◽  
Sensor Data ◽  
Training Dataset ◽  
Complex Activity ◽  
Multilabel Classification ◽  
Classification Approach ◽  
Emerging Patterns

In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches.

Scalable regular pattern mining in evolving body sensor data

2017 ◽  
Vol 75 ◽  
pp. 172-186 ◽  
Author(s):  
Syed Khairuzzaman Tanbeer ◽  
Mohammad Mehedi Hassan ◽  
Ahmad Almogren ◽  
Mansour Zuair ◽  
Byeong-Soo Jeong
Keyword(s):  
Pattern Mining ◽  
Sensor Data ◽  
Regular Pattern

scholarly journals MISCELA: discovering simultaneous and time-delayed correlated attribute patterns

2020 ◽  
Author(s):  
Kei Harada ◽  
Yuya Sasaki ◽  
Makoto Onizuka
Keyword(s):  
Time Delays ◽  
Pattern Mining ◽  
State Of The Art ◽  
Temporal Correlation ◽  
Sensor Data ◽  
Mining Method ◽  
Correlated Patterns ◽  
Mining Methods ◽  
Spatio Temporal ◽  
Temporally Correlated

Abstract This article addresses a new pattern mining problem in time series sensor data, which we call correlated attribute pattern mining. The correlated attribute patterns (CAPs for short) are the sets of attributes (e.g., temperature and traffic volume) on sensors that are spatially close to each other and temporally correlated in their measurements. Although the CAPs are useful to accurately analyze and understand spatio-temporal correlation between attributes, the existing mining methods are inefficient to discover CAPs because they extract unnecessary patterns. Therefore, we propose a mining method Miscela to efficiently discover CAPs. Miscela can discover not only simultaneous correlated patterns but also time delayed correlated patterns. Furthermore, we extend Miscela to automatically search for correlated patterns with any time delays. Through our experiments using three real sensor datasets, we show that the response time of Miscela is up to 20.84 times faster compared with the state-of-the-art method. We show that Miscela discovers meaningful patterns for urban managements and environmental studies.

scholarly journals Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

2018 ◽  
Vol 8 (5) ◽  
pp. 3374
Author(s):  
Imam Mukhlash ◽  
Desna Yuanda ◽  
Mohammad Iqbal
Keyword(s):  
Data Mining ◽  
Smart Home ◽  
Pattern Mining ◽  
Technology Development ◽  
Sensor Data ◽  
Mining Machine ◽  
Periodic Pattern ◽  
Time Interval ◽  
Periodic Patterns ◽  
Using Data

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.

scholarly journals The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms

2016 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Edith Belise Kenmogne
Keyword(s):  
Dna Analysis ◽  
Pattern Mining ◽  
User Behavior ◽  
Search Space ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sensor Data ◽  
Large Field ◽  
Pattern Growth ◽  
The Impact

Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large datasetwidely addressed by the data mining community, with a very large field of applications, such as cross-marketing, DNA analysis, web log analysis,user behavior, sensor data, etc. The sequence pattern mining aims at extractinga set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan and PrefixSpan.In this paper, we are interested in the study of the impact of the pattern-growthordering on the performances of pattern growth-based sequential pattern mining algorithms.To this end, we introduce a class of pattern-growth orderings, called linear orderings, for which patterns are grown by making grow either the currentpattern prefix or the current pattern suffix from the same position at eachgrowth-step.We study the problem of pruning and partitioning the search space followinglinear orderings. Experimentations show that the order in which patternsgrow has a significant influence on the performances. 

scholarly journals A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33318-33341 ◽  
Author(s):  
Md. Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Sakib Shahriar Shafin ◽  
Md. Zakirul Alam Bhuiyan
Keyword(s):  
Internet Of Things ◽  
Pattern Mining ◽  
Behavioral Pattern ◽  
Sensor Data

Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques

2015 ◽  
Vol 115 (6) ◽  
pp. 1151-1178 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Chengliang Wang ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Pattern Mining ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Sequential Data ◽  
Research Issues ◽  
Content Type ◽  
Performance Optimizations ◽  
And Performance

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Validating the INTERPRETOR Software Architecture for the Interpretation of Large and Noisy Data Sets

2013 ◽  
pp. 135-148
Author(s):  
Apkar Salatian
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Software Architecture ◽  
Case Studies ◽  
Pattern Mining ◽  
Empirical Studies ◽  
Noisy Data ◽  
Sensor Data ◽  
Data Sets ◽  
Dataflow Model

In this chapter, the authors validate INTERPRETOR software architecture as a dataflow model of computation for filtering, abstracting, and interpreting large and noisy datasets with two detailed empirical studies from the authors’ former research endeavours. Also discussed are five further recent and distinct systems that can be tailored or adapted to use the software architecture. The detailed case studies presented are from two disparate domains that include intensive care unit data and building sensor data. By performing pattern mining on five further systems in the way the authors have suggested herein, they argue that INTERPRETOR software architecture has been validated.

scholarly journals An Efficient Incremental Mining Algorithm for Discovering Sequential Pattern in Wireless Sensor Network Environments

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 29 ◽  
Author(s):  
Xin Lyu ◽  
Hongxu Ma
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern ◽  
Sensor Data ◽  
Incremental Algorithm ◽  
Wireless Sensor ◽  
Incremental Mining ◽  
The Real ◽  
Mining Algorithm ◽  
Intelligent Decision ◽  
Mining Algorithms

Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data are produced from WSNs all the time, and it is significant to process and analyze data effectively to support intelligent decision and management. However, the new characteristics of sensor data, such as rapid growth and frequent updates, bring new challenges to the mining algorithms, especially given the time constraints for intelligent decision-making. In this work, an efficient incremental mining algorithm for discovering sequential pattern (novel incremental algorithm, NIA) is proposed, in order to enhance the efficiency of the whole mining process. First, a reasoned proof is given to demonstrate how to update the frequent sequences incrementally, and the mining space is greatly narrowed based on the proof. Second, an improvement is made on PrefixSpan, which is a classic sequential pattern mining algorithm with a high-complexity recursive process. The improved algorithm, named PrefixSpan+, utilizes a mapping structure to extend the prefixes to sequential patterns, making the mining step more efficient. Third, a fast support number-counting algorithm is presented to choose frequent sequences from the potential frequent sequences. A reticular tree is constructed to store all the potential frequent sequences according to subordinate relations between them, and then the support degree can be efficiently calculated without scanning the original database repeatedly. NIA is compared with various kinds of mining algorithms via intensive experiments on the real monitoring datasets, benchmarking datasets and synthetic datasets from aspects including time cost, sensitivity of factors, and space cost. The results show that NIA performs better than the existed methods.

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