5,857 research outputs found
Contextual Outlier Interpretation
Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers does not receive much attention. Interpretation is becoming
increasingly important to help people trust and evaluate the developed models
through providing intrinsic reasons why the certain outliers are chosen. It is
difficult, if not impossible, to simply apply feature selection for explaining
outliers due to the distinct characteristics of various detection models,
complicated structures of data in certain applications, and imbalanced
distribution of outliers and normal instances. In addition, the role of
contrastive contexts where outliers locate, as well as the relation between
outliers and contexts, are usually overlooked in interpretation. To tackle the
issues above, in this paper, we propose a novel Contextual Outlier
INterpretation (COIN) method to explain the abnormality of existing outliers
spotted by detectors. The interpretability for an outlier is achieved from
three aspects: outlierness score, attributes that contribute to the
abnormality, and contextual description of its neighborhoods. Experimental
results on various types of datasets demonstrate the flexibility and
effectiveness of the proposed framework compared with existing interpretation
approaches
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
Detecting outlier patterns with query-based artificially generated searching conditions
In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions
In the age of social computing, finding interesting network patterns or
motifs is significant and critical for various areas such as decision
intelligence, intrusion detection, medical diagnosis, social network analysis,
fake news identification, national security, etc. However, sub-graph matching
remains a computationally challenging problem, let alone identifying special
motifs among them. This is especially the case in large heterogeneous
real-world networks. In this work, we propose an efficient solution for
discovering and ranking human behavior patterns based on network motifs by
exploring a user's query in an intelligent way. Our method takes advantage of
the semantics provided by a user's query, which in turn provides the
mathematical constraint that is crucial for faster detection. We propose an
approach to generate query conditions based on the user's query. In particular,
we use meta paths between nodes to define target patterns as well as their
similarities, leading to efficient motif discovery and ranking at the same
time. The proposed method is examined on a real-world academic network, using
different similarity measures between the nodes. The experiment result
demonstrates that our method can identify interesting motifs, and is robust to
the choice of similarity measures
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
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