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  • 2020-2024  (6)
  • 2020-2022  (1)
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  • 1
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    VoCC: Vortex Correlation Clustering Based on Masked Hough Transformation in Spatial Databases (2023)
    Association for Computing Machinery
    Details
    Publication Date: 2023-09-28
    Description: A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.
    Type: Conference or Workshop Item , NonPeerReviewed
    Location Call Number Limitation Availability
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    OceanRep: Conference paper
  • 2
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    A Framework for Extracting Scientific Measurements and Geo-Spatial Information from Scientific Literature (2022)
    IEEE
    Details
    Publication Date: 2023-03-17
    Description: Research papers are often the primary source of scientific information dissemination, as researchers encapsulate their findings in these documents. Generally such findings are of complex types, diverse expressions and also carry rich context. The traditional approach for extracting certain scientific information from these documents is manual extraction, which is very time consuming. Due to the rapid increase in number of publications, using the full potential of these rich data sources by manual extraction is becoming infeasible. In this paper, we propose a framework for the automatic extraction of targeted (user defined) quantitative information, e.g. temperature sensor values, with its geo-spatial context from scientific documents. Given a database of scientific documents and a targeted user-defined geo-tagable measurement variables, mass accumulation rate (MAR) and sedimentation rate (SR), the problem we are addressing is to retrieve all the values together with their geo-spatial information respectively. Though there has been done a lot in information retrieval, to the best of our knowledge, this problem has not been explored, yet. We design a novel heterogeneous linking solution, that links measurements with locations, which are found by our tailored extraction pipeline. In experimental studies based on our novel dataset of Marine Geology papers, we showcase the capabilities of our linking framework using common geo-tagable Marine Geology measurements.
    Type: Book chapter , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Book chapter
  • 3
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    Perspectives on Marine Data Science as a Blueprint for Emerging Data Science Disciplines (2021)
    Frontiers
    Details
    Publication Date: 2024-02-07
    Description: Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
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    SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction (2023)
    Association for Computing Machinery
    Details
    Publication Date: 2024-02-05
    Description: Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessibility to a sufficiently dense data source, which is rather the rare case in reality. Traffic sensors in road networks are generally highly sparse in their distribution: fleet-based traffic sensing is sparse in space but also sparse in time. There are also other traffic application, besides road traffic, like moving objects in the marine space, where observations are sparsely and arbitrarily distributed in space. In this paper, we tackle the problem of traffic prediction on sparse and spatially irregular and non-deterministic traffic observations. We draw a border between imputations and this work as we consider high sparsity rates and no fixed sensor locations. We advance correlation mining methods with a Sparse Unstructured Spatio Temporal Reconstruction (SUSTeR) framework that reconstructs traffic states from sparse non-stationary observations. For the prediction the framework creates a hidden context traffic state which is enriched in a residual fashion with each observation. Such an assimilated hidden traffic state can be used by existing traffic prediction methods to predict future traffic states. We query these states with query locations from the spatial domain.
    Type: Conference or Workshop Item , NonPeerReviewed
    Location Call Number Limitation Availability
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    OceanRep: Conference paper
  • 5
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    Getting the big picture in cross-domain fusion (2022)
    Springer
    Details
    Publication Date: 2023-01-04
    Description: A central promise of cross-domain fusion (CDF) is the provision of a “bigger picture” that integrates different disciplines and may span very different levels of detail. We present a number of settings that call for this bigger picture, with a particular focus on how information from several domains can be made easily accessible and visualizable for different stakeholders. We propose harnessing an approach that is now well established in interactive maps, which we refer to as the “Google maps approach” (Google LLC, Mountain View, CA, USA), which combines effective filtering with intuitive user interaction. We expect this approach to be applicable to a range of CDF settings.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
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    Perspectives on Marine Data Science as a Blueprint for Emerging Data Science Disciplines (2021)
    In:  EPIC3Frontiers in Marine Science, 8, ISSN: 2296-7745
    Details
    Publication Date: 2021-12-22
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
    Location Call Number Limitation Availability
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    PAPER (OA)
  • 7
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    5th Data Science Symposium, GEOMAR (2021)
    GEOMAR
    In:  EPIC35th Data Science Symposium, Online/ Kiel, 2021-01-22-2021-01-22Kiel, GEOMAR
    Details
    Publication Date: 2023-06-21
    Description: Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR)
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
    Location Call Number Limitation Availability
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    PAPER (OA)
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