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

Replacing missing values using trustworthy data values from web data sources

In practice, collected data usually are incomplete and contains missing value. Existing approaches in managing missing values overlook the importance of trustworthy data values in replacing missing values. In view that trusted completed data is very important in data analysis, we proposed a framework of missing value replacement using trustworthy data values from web data sources. The proposed framework adopted ontology to map data values from web data sources to the incomplete dataset. As data from web is conflicting with each other, we proposed a trust score measurement based on data accuracy and data reliability. Trust score is then used to select trustworthy data values from web data sources for missing values replacement. We successfully implemented the proposed framework using financial dataset and presented the findings in this paper. From our experiment, we manage to show that replacing missing values with trustworthy data values is important especially in a case of conflicting data to solve missing values problem

Personlighetens påvirkning for emosjonell endring etter natureksponering

I denne studien har vi undersøkt om variabler som personlighetstrekk og kjønn påvirker hvordan mennesker blir emosjonelt påvirket etter å eksponeres for bilder av ulike naturtyper. Personlighetsmodellen OCEAN ble brukt til å måle deltakernes personlighet. PANAS ble brukt til å måle positiv og negativ affekt. Det ble utført en pilotstudie der deltakerne måtte vurdere bilder av landskap for å kunne klassifisere landskapsbilder, hvor man ønsket å finne landskap til å være veldig åpne og lett å gå i, eller veldig lukket, vanskelig å gå i og som hadde mye vegetasjon. Årsaken til at disse to landskapstypene ble valgt i studien er på bakgrunn av prospect refuge hypotesen, som antyder at folk foretrekker landskap som gir både god utsikt, og at det er mulig å gjemme seg. Deltakerne i hovedstudien ble delt inn i to grupper, en for hver landskapstype. Etter at begge gruppene så bilder tilhørende sin kategori, ble forskjellene i affektiv endring sammenlignet. Hovedstudien ønsket å se nærmere på om biologiske faktorer som personlighet og kjønn kan forutsi om du vil ha en positiv eller negativ følelsesmessig respons på de to landskapstypene, eller om noen vil oppleve en større positiv endring enn andre. Resultatet fra denne studien tyder på at det er forskjeller i hvordan mennesker med ulike personlighetstrekk vil reagere på ulik natur. Personer som har høy grad av nevrotisisme vil ha en større økning i positiv affekt etter utsatt for begge landskapstypene. Det er også en forskjell mellom menn og kvinner i hvordan de reagerer på de to landskapene. Kvinner vil ha en fordel av å bli eksponert for begge typer landskap, men menn vil bare påvirkes positivt etter å ha blitt eksponert for åpent landskap.In this study, we have examined whether variables like personality traits and gender do have a role in how people are affected at an emotional level when they are exposed to photos of different types of nature. In terms of personality, the model that was measured in this study was OCEAN personality model. PANAS was used to measure positive and negative affect. There was conduced a pilot study where participants had to evaluate pictures of landscape, to identify landscape that were very open and easy to walk in, or very closed, difficult to walk in and had much vegetation. The reason why these two kinds of landscapes were selected in the study was due to the prospect refuge hypothesis, which suggests that people prefer landscapes that provides both a good view and a place to hide. Participants in the main study was divided into two groups, one for each landscape, and the differences in affective change after being exposed to pictures of the landscapes was compared. The main study wants to look closer into if biological factors like personality and gender can predict whether you will have a positive or negative emotional response to the two landscapes, or if someone has a stronger benefit from it. The result from this study suggests that there are differences in how people with different personality traits will respond to different nature. People who have very high level of neuroticism will have a greater increase in positive affect after being exposed to both landscapes. And there’s also a difference between men and women in how they respond to the two landscapes. Women will have a benefit from exposed to both types of landscape, but men will only benefit from being exposed to open landscape

Semantic-based detection of segment outliers and unusual events for wireless sensor networks

Environmental scientists have increasingly been deploying wireless sensor networks to capture valuable data that measures and records precise information about our environment. One of the major challenges associated with wireless sensor networks is the quality of the data – and more specifically the detection of segment outliers and unusual events. Most previous research has focused on detecting outliers that are errors that are caused by unreliable sensors and sensor nodes. However, there is an urgent need for the development of new tools capable of identifying, tagging and visualizing erroneous segment outliers and unusual events from sensor data streams. In this paper, we present a SOUE-Detector (Segment Outlier and Unusual Event-Detector) system for wireless sensor networks that combines statistical analyses using Dynamic Time Warping (DTW) with domain expert knowledge (captured via an ontology and semantic inferencing rules). The resulting Web portal enables scientist to efficiently search across a collection of wireless sensor data streams and identify, retrieve and display segment outliers (both erroneous and genuine) within the data streams. In this paper, we firstly describe the detection algorithms, the implementation details and the functionality of the SOUE-Detector system. Secondly we evaluate our approach using data that comprises sensor observations collected from a sensor network deployed in the Springbrook National Park in Queensland, Australia. The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall