Non-conventional Data Research Articles

Ground-Penetrating Radar and Photogrammetric Investigation on Prehistoric Tumuli at Parabita (Lecce, Italy) Performed with an Unconventional Use of the Position Markers

In this contribution, we propose ground-penetrating radar (GPR) investigation performed close and on some prehistoric tumuli, locally called “piccole specchie”, in the countryside around the town of Parabita (Lecce), within the Salento peninsula (southern Italy). In order to perform the GPR investigation on the tumuli, an unconventional method of data acquisition was exploited, involving, consequently, some non-conventional data processing steps. Photogrammetric survey was also performed, and 3D digital models of the prehistoric tumuli were created. The investigations have revealed some anomalies under two out of three investigated tumuli, which were interpreted as prehistoric tombs.

Assessing Numerical Model Skill at Simulating Coastal Flooding Using Field Observations of Deposited Debris and Photographic Evidence

Despite the growing range and availability of resources to support coastal flood hazard model development, there is often a scarcity of data to support critical assessment of the performance of community-scale coastal inundation models. Even where long-term tide gauge measurements are available in close proximity to the study area, the records provide little insight into the spatial distribution and limits of overland flooding, or the influence of topographic features and structures on flooding pathways. We present methods to support the assessment of model performance using field observations in lieu of, or supplementary to, conventional water-level records. A high-resolution, numerical coastal flood hazard model was developed to simulate storm surge-driven flooding in the Acadian Peninsula region of New Brunswick, Canada. Owing to the remoteness of the study area from tide gauge stations, model performance was assessed based on a comparison with field measurements of deposited wrack and debris, as well as photographic and video evidence of coastal flooding, for two significant storm surge events in recent history. Our research findings illustrate the value of observational and qualitative data for characterizing coastal flood hazards, lending gravity to the importance of non-conventional data sources, particularly in data-scarce regions.

A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

This research investigates the applicability of combining spatial filter’s algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll-α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC’s centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll-α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio’s centerline extracted from heat flux, chlorophyll-α, and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan.

Investigating the Impacts of Assimilating Surface Observations on High-Resolution Forecasts of the 15 May 2013 Tornado Event

In this study, the Advanced Regional Prediction System (ARPS), and its associated three-dimensional variational analysis (3DVAR) package are used to simulate a tornadic supercell at 400-m grid spacing. This storm produced an EF3 tornado in Johnson County, TX during the evening of 15 March 2013. Data from Doppler radar, satellite, aircraft, radiosondes, profilers, and surface observations are assimilated in this work. We show that the assimilation of non-conventional surface observations from three networks: the Citizen Weather Observer Program (CWOP), Global Science and Technology (GST) and Automated Weather Stations (AWS) operated by EarthNetworks, in and around the storm inflow, were fundamentally important to the development of an intense low-level mesocyclone. Simulations that did not incorporate this non-conventional data either developed a weak mesocyclone that was displaced to the east of the actual tornado track, or were unable to develop a defined mesocyclone in the first place. In particular, the assimilation of thermodynamic variables (temperature, pressure and moisture) from a subset of AWS observations near the storm’s inflow environment leads to the most accurate simulation of the low-level mesocyclone.

Going Beyond the Blue: The Utility of Emergency Medical Services Data in Understanding Violent Crime

National victimization data suggest less than 50% of violent crime incidents are reported to the police. Official reports of crime to police, however, are often the only type of data used for the analysis of violence problems, the identification of geographic concentrations of violent crime, and the selection of targets for police and prevention resources. Yet, the question remains, are estimates of violent crime prevalence and location distorted from a unilateral reliance on police data? Here, we examine whether emergency medical service (EMS) data collected by the fire department are spatially concentrated in the same way as police data and whether these data can help identify instances of violence unreported to police in the city of Seattle between 2009 and 2011. We find high levels of concentration in both police and EMS data and evidence that new information is learned about the location of violence problems from utilizing multiple data sources. Overall, these findings contribute to a small but growing body of work that demonstrates the utility of nonconventional data in the identification of crime and harm concentrations of interest.

Numerical Analysis of Storm Surges on Canada’s Western Arctic Coastline

A numerical study was conducted to characterize the probability and intensity of storm surge hazards in Canada’s western Arctic. The utility of the European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5) dataset to force numerical simulations of storm surges was explored. Fifty historical storm surge events that were captured on a tide gauge near Tuktoyaktuk, Northwest Territories, were simulated using a two-dimensional (depth-averaged) hydrodynamic model accounting for the influence of sea ice on air-sea momentum transfer. The extent of sea ice and the duration of the ice season has been reducing in the Arctic region, which may contribute to increasing risk from storm surge-driven hazards. Comparisons between winter storm events under present-day ice concentrations and future open-water scenarios revealed that the decline in ice cover has potential to result in storm surges that are up to three times higher. The numerical model was also used to hindcast a significant surge event that was not recorded by the tide gauge, but for which driftwood lines along the coast provided insights to the high-water marks. Compared to measurements at proximate meteorological stations, the ERA5 reanalysis dataset provided reasonable estimates of atmospheric pressure but did not accurately capture peak wind speeds during storm surge events. By adjusting the wind drag coefficients to compensate, reasonably accurate predictions of storm surges were attained for most of the simulated events. The extreme value probability distributions (i.e., return periods and values) of the storm surges were significantly altered when events absent from the tide gauge record were included in the frequency analysis, demonstrating the value of non-conventional data sources, such as driftwood line surveys, in supporting coastal hazard assessments in remote regions.

The speaking/writing relation in non-conventional data of children’s writing

Tendo como principal preocupação a relação fala/escrita na escrita infantil, o objetivo do presente estudo foi investigar flutuações no registro ortográfico de palavras nesse tipo de escrita. Trata-se de flutuações que ocorreram no interior de um mesmo texto e que envolveram os constituintes prosódicos grupo clítico e palavra fonológica. Os dados foram extraídos de produções escritas de crianças brasileiras que cursavam as três séries iniciais do Ensino Fundamental I, que compõem o Ciclo de Alfabetização. Os resultados mostraram que as flutuações deixam indícios da ancoragem das crianças, simultaneamente, em características prosódicas de enunciados falados e em características das convenções ortográficas presentes em enunciados escritos. Assim, ao sinalizarem sua ancoragem em práticas de oralidade e de letramento, as flutuações mostram que as relações fala/escrita detectadas na escrita infantil são constitutivas dessa escrita. A ancoragem das flutuações em características dessas práticas mostrou, ainda, as não-coincidências das palavras consigo mesmas, sobretudo por apontarem para relações que uma mesma estrutura prosódica da língua mantém, na enunciação escrita, com as estruturas prosódicas mais amplas que as recobrem. Mostrou, por fim, traços da subjetividade e da historicidade dos escreventes.

Implikatur Non-Konvensional pada Novel 5 Cm Karya Dony Dirgantara

This research on non-conventional Implications is based on the 5 cm Novel by Donny Dirgantara. A literary work can be researched, enjoyed, and lived in, as well as studies related to real-life images used by the author. Readers can look for implicit meanings, or something different from what the speaker actually says. Therefore, the aim of this study is to look at the conversations of the characters in Donny Dhirgantoro's 5cm novel non-conventional. The method used in this research is descriptive method through a qualitative approach. Based on the results of the research, there are twelve non-conventional data that have the meaning of meaning, namely wanting to imply something with the truth.

Editorial for Special Issue “Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms”

Microwave radar imaging plays a key role in several civilian and defense applications, such as security, surveillance, diagnostics and monitoring in civil engineering and cultural heritage, environment observation, with particular emphasis on disasters and crisis management, where it is required to remotely sense the area of interest in a timely, safe and effective way. To address these constraints, a technological opportunity is offered by radar systems mounted onboard smart and flexible platforms, such as ground-based ones, airplanes, helicopters, drones, unmanned aerial and ground vehicles (UAV and UGV). For this reason, radar imaging based on data collected by such platforms is gaining interest in the remote sensing community. However, a full exploitation of smart and flexible radar systems requires the development and use of image formation techniques and reconstruction approaches able to exploit and properly deal with non-conventional data acquisition configurations. The other main issue is related to the need to operate in challenging environments, and still deliver high target detection, localization and tracking. These environments include through the wall imaging, rugged terrain and rough surface/subsurface. In these cases, one seeks mitigation of the adverse effects of clutter and multipath via the implementation of effective signal processing strategies and electromagnetic modeling.

Assessing Impacts of the High-Frequency Assimilation of Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas–Fort Worth Test Bed

AbstractThe Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.

Urinary steroid profiling in women hints at a diagnostic signature of the polycystic ovary syndrome: A pilot study considering neglected steroid metabolites.

BackgroundAlthough the polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women with vast metabolic consequences, its etiology remains unknown and its diagnosis is still made by exclusion. This study aimed at characterizing a large number of urinary steroid hormone metabolites and enzyme activities in women with and without PCOS in order to test their value for diagnosing PCOS.MethodsComparative steroid profiling of 24h urine collections using an established in-house gas-chromatography mass spectrometry method. Data were collected mostly prospectively. Patients were recruited in university hospitals in Switzerland. Participants were 41 women diagnosed with PCOS according to the current criteria of the Androgen Excess and PCOS Society Task Force and 66 healthy controls. Steroid profiles of women with PCOS were compared to healthy controls for absolute metabolite excretion and for substrate to product conversion ratios. The AUC for over 1.5 million combinations of metabolites was calculated in order to maximize the diagnostic accuracy in patients with PCOS. Sensitivity, specificity, PPV, and NPV were indicated for the best combinations containing 2, 3 or 4 steroid metabolites.ResultsThe best single discriminating steroid was androstanediol. The best combination to diagnose PCOS contained four of the forty measured metabolites, namely androstanediol, estriol, cortisol and 20βDHcortisone with AUC 0.961 (95% CI 0.926 to 0.995), sensitivity 90.2% (95% CI 76.9 to 97.3), specificity 90.8% (95% CI 81.0 to 96.5), PPV 86.0% (95% CI 72.1 to 94.7), and NPV 93.7% (95% CI 84.5 to 98.2).ConclusionPCOS shows a specific 24h urinary steroid profile, if neglected metabolites are included in the analysis and non-conventional data analysis applied. PCOS does not share a profile with hyperandrogenic forms of congenital adrenal hyperplasias due to single steroid enzyme deficiencies. Thus PCOS diagnosis by exclusion may no longer be warranted. Whether these findings also apply to spot urine and serum, remains to be tested as a next step towards routine clinical applicability.

IT Governance in Higher Education Institutions

Information Technology (IT) is a very important aspect for higher education institutions (HEI) in both teaching, research and administration. The managers of those intuitions are more and more aware that IT is a strategic tool for their institutions. On the other hand, IT Governance is getting attention from the practitioner and research side, given the need to govern IT extending the organisation's strategy and objectives into IT. IT Governance helps to set clear expectations, to gain participation, open communications, establish accountability and provide executive management oversight. Thus, it is important to consider IT Governance and the alignment with business strategy for HEI. In this article, authors present a Systematic Literature Review (SLR) on IT Governance in HEIs using a collection of scientific and non-conventional data (grey literature). The motivation that drives this literature review is the further development of an IT Governance framework for Tunisian universities. This article aims to define the situation of the IT Governance in other countries. This article aims to provide a map of the state of the art of IT governance in HEI in various countries. Results show a mixed situation of IT Governance in HEIs. Some countries have the support of top level management to introduce IT Governance in HEIs by adopting regulatory frameworks and common laws. But other countries relay in their strong culture of IT Governance. The different case studies presented in this review show that there is no consensus on the IT Governance framework or standard to use in HEI. An important number of institutions are implementing COBIT or ISO best practices. In the other hand, some counties have developed their own frameworks. Results show there is no a single way to implement an IT Governance framework designed for HEI. However, it is also true that there are two mandatory aspects that are necessary to implement in IT Governance deployments: firstly, establishing a committee structure for IT assets and secondly, enable effective communication between the IT, the business and the stakeholders.

Efficient data distribution and results merging for parallel data clustering in mapreduce environment

Clustering data consists in partitioning it into clusters such that there is a strong similarity between data in the same cluster and a weak similarity between data in different clusters. With the significant increase in data volume, the clustering process becomes an expensive task in terms of computation. Therefore, several solutions have been proposed to overcome this issue using parallelism with the MapReduce paradigm. The proposed solutions in the literature aim to optimize the execution time while keeping the clustering quality close or identical to the sequential execution. One of the commonly used parallel clustering strategies when using the MapReduce framework consists in partitioning data and processing each partition separately. The results obtained from each partition are merged to obtain the final clusters configuration. Using a random data distribution strategy and an inappropriate merging technique will lead to an inaccurate final centroids and a rather average clustering quality. Hence, in this paper we propose a parallel scheme for partitional clustering algorithms based on MapReduce with a non-conventional data distribution and results merging strategies to improve the clustering quality. With this solution, in addition to optimizing the execution time, we exploit the parallel environment to enhance the clustering quality. The experimental results demonstrate the effectiveness and scalability of our solution in comparison with other recently proposed works. We also proposed an application of our approach to the community detection problem. The results demonstrate the ability of our approach to provide effective and relevant results.

Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

Abstract. With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

Radiogenomic Analysis of Oncological Data: A Technical Survey.

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

In-situ observational network for extreme weather events in India

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

A similarity-based data warehousing environment for medical images

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

Surface identification from multiband LADAR reflectance with varied incidence angle via database mapping.

Incident angle dependencies of LADAR reflection depend on bulk material reflectivity and surface texture properties that can be exploited for surface identification. In this paper, surface identification via multiband LADAR reflected radiance is assessed using the nonconventional exploitation factors data system database. A statistics-based dimension reduction algorithm, stochastic neighborhood embedding (t-SNE), is used to separate the data clouds resulting from the monostatic LADAR reflected radiance and corresponding band ratios. The application of t-SNE to multiband reflected radiance effectively separates the data clouds, making surface identification via multiband LADAR reflectance possible in the presence of unknown incident angle dependencies and uncertainties. It is demonstrated that, for both the multiband monostatic reflected radiance and band ratios, the application of t-SNE mapping yields a significant improvement in surface identification from measurements with unknown or varied incident angles.

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency.

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

A UML profile for the conceptual modelling of structurally complex data: Easing human effort in the KDD process

ContextDomains where data have a complex structure requiring new approaches for knowledge discovery from data are on the increase. In such domains, the information related to each object under analysis may be composed of a very broad set of interrelated data instead of being represented by a simple attribute table. This further complicates their analysis. ObjectiveIt is becoming more and more necessary to model data before analysis in order to assure that they are properly understood, stored and later processed. On this ground, we have proposed a UML extension that is able to represent any set of structurally complex hierarchically ordered data. Conceptually modelled data are human comprehensible and constitute the starting point for automating other data analysis tasks, such as comparing items or generating reference models. MethodThe proposed notation has been applied to structurally complex data from the stabilometry field. Stabilometry is a medical discipline concerned with human balance. We have organized the model data through an implementation based on XML syntax. ResultsWe have applied data mining techniques to the resulting structured data for knowledge discovery. The sound results of modelling a domain with such complex and wide-ranging data confirm the utility of the approach. ConclusionThe conceptual modelling and the analysis of non-conventional data are important challenges. We have proposed a UML profile that has been tested on data from a medical domain, obtaining very satisfactory results. The notation is useful for understanding domain data and automating knowledge discovery tasks.