Metadata Structure Research Articles

Extended Query Model for MOOC Education Resource Metadata Based on Big data

In traditional education resource metadata extended query methods, data recall is poor. In order to solve this problem, an extended query model for MOOC education resource metadata based on big data information fusion clustering scheduling is proposed. The semantic ontology model is adopted to analyse the storage structure of MOOC education resource metadata and extract binary semantic feature quantity of MOOC education resource metadata to construct a binary semantic decision model for extended query of MOOC education resource metadata; the integrated information processing technology of big data is adopted for extended query and adaptive scheduling of MOOC education resource metadata to improve the ability to retrieve and identify metadata. The simulation results show that the proposed method can provide an average precision ratio of 0.976 in extended query of MOOC education resource metadata and good data recall performance.

Extended query model for MOOC education resource metadata based on big data

In traditional education resource metadata extended query methods, data recall is poor. In order to solve this problem, an extended query model for MOOC education resource metadata based on big data information fusion clustering scheduling is proposed. The semantic ontology model is adopted to analyse the storage structure of MOOC education resource metadata and extract binary semantic feature quantity of MOOC education resource metadata to construct a binary semantic decision model for extended query of MOOC education resource metadata; the integrated information processing technology of big data is adopted for extended query and adaptive scheduling of MOOC education resource metadata to improve the ability to retrieve and identify metadata. The simulation results show that the proposed method can provide an average precision ratio of 0.976 in extended query of MOOC education resource metadata and good data recall performance.

Personalized Real-Time Virtual Tours In Places With Cultural Interest

Virtual tours using drones enhance the experience the users perceive from a place with cultural interest. Drones equipped with 360o cameras perform real-time video streaming of the cultural sites. The user preferences about each monument type should be considered in order to decide the appropriate flying route for the drone. This article describes a scheme for supporting personalized real-time virtual tours at sites with cultural interest using drones. The user preferences are modeled using the MPEG-21 and the MPEG-7 standards, while Web Ontology Language (OWL) ontologies are used for metadata structure and semantics. The Metadata-Aware Analytic Network Process (MANP) algorithm is proposed in order to weigh the user preferences for each monument type. Subsequently, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS) algorithm ranks the candidate heritage routes. Finally, after each virtual tour, the user preferences metadata are updated in order in order the scheme to continuously learn about the user preferences.

Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.

Russian–German Astroparticle Data Life Cycle Initiative

Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples.

OmniArt

Baselines are the starting point of any quantitative multimedia research, and benchmarks are essential for pushing those baselines further. In this article, we present baselines for the artistic domain with a new benchmark dataset featuring over 2 million images with rich structured metadata dubbed OmniArt. OmniArt contains annotations for dozens of attribute types and features semantic context information through concepts, IconClass labels, color information, and (limited) object-level bounding boxes. For our dataset we establish and present baseline scores on multiple tasks such as artist attribution, creation period estimation, type, style, and school prediction. In addition to our metadata related experiments, we explore the color spaces of art through different types and evaluate a transfer learning object recognition pipeline.

A multi-core ready discrete element method with triangles using dynamically adaptive multiscale grids.

SummaryThe simulation of vast numbers of rigid bodies of non‐analytical shapes and of tremendously different sizes that collide with each other is computationally challenging. A bottleneck is the identification of all particle contact points per time step. We propose a tree‐based multilevel meta data structure to administer the particles. The data structure plus a purpose‐made tree traversal identifying the contact points introduce concurrency to the particle comparisons, whilst they keep the absolute number of particle‐to‐particle comparisons low. Furthermore, a novel adaptivity criterion allows explicit time stepping to work with comparably large time steps. It optimises both toward low algorithmic complexity per time step and low numbers of time steps. We study three different parallelisation strategies exploiting our traversal's concurrency. The fusion of two of them yields promising speedups once we rely on maximally asynchronous task‐based realisations. Our work shows that new computer architecture can push the boundary of rigid particle computability, yet if and only if the right data structures and data processing schemes are chosen.

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones.

BrainMap VBM: An environment for structural meta-analysis.

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.

Comparison of the Features of EPUB E-Book and SCORM E-Learning Content Model

E-books nowadays have greatly evolved in its presentation and functions, however its features for education need to be investigated and inspired because people who are accustomed to using printed books may consider and approach it in the same way as they do printed ones. Therefore, the authors compared the EPUB e-book content model with the SCORM e-learning content model from the respects of their content presentation, metadata and package structures. Drs. Chang and Hung found that 1) EPUB has the possibility to implement the advantage of content sharing and reusing. 2) EPUB e-books can present educational materials with multimedia and interactive components based on web technology. However, content creators should beware of the limited supported media types 3) EPUB lacks dedicated educational metadata. 4) EPUB e-books have a content reflow mechanism to adjust layouts to fit small screen devices and are able to use all resources offline. Finally, they determined the research issues and strategies that are worthy of further investigation and development for EPUB e-books in education based on our findings.

Concurrency control algorithms for deduplicated cloud storage

Deduplication of data is essential to effectively use cloud storage. As the metadata in deduplicated cloud storage are shared across multiple users, concurrent updates may result in inconsistencies. A coarse-grained locking strategy that has been proposed earlier to overcome this difficulty is not suited for inline deduplication owing to poor performance. In the present work, a fine-grained locking strategy that overcomes this shortcoming is proposed. A metadata structure along with a set of concurrent control mechanisms to accomplish this is presented. This strategy is shown to improve the throughput by as much as 60% with only marginal lock overhead.

A Model for Using DataCite DOIs in Observatory Bibliographies

The use of DOI persistent identifiers has become an attractive mechanism for citing datasets in articles. However, taking the users’ interests into account requires careful consideration of the way in which we apply these identifiers. Our objective is the application of DOIs in such a way that individual datasets are properly cited while presenting the citations to the reader in a user-friendly manner. This is achieved by making judicious use of the metadata structure provided by DataCite.

Using semantic metadata for continuous development of requirements and goals in the Smart Mobility domain – An empirical study

The continuous development and consistent documentation of requirements and goals is a major challenge in product development. Inconsistency can lead to immense changes in development time, costs or quality. If major inconsistencies are not detected, a project failure may occur. As systems-of-systems (SoS) are developed by many stakeholders from independent organizational units using many different methods, processes and tools, this brings new challenges to obtain consistency and continuity on multiple levels and in different phases of the product development process. As changes to requirements can have a wide impact on the distributed and partly independent working development teams, requirements and goals as well as their heterogeneous interrelations and boundary conditions have to be modelled as a continuous system of objectives. The KaRDF approach developed at the IPEK – Institute of Product Engineering at the Karlsruhe Institute of Technology uses the semantic web technology RDF to link development artefacts independent of their representation in order to build consistent product models for systems-of-systems on different level of detail supporting multiple development and product generations. This research work presents the results of an empirical study in the Smart Mobility domain where KaRDF was used to support the development of SoS. The KaRDF ontology and inference rules are implemented in a web-based software tool which is used by project managers from a German car manufacturer for adding semantic metadata to their development artefacts. The results show that through usage of the defined semantic metadata structure and inference rules, continuity and consistency in product development of systems-of-systems is improved and the ability of the approach to integrate with heterogeneous methods, processes and tools used in a systems-of-systems development environment is proven.

Efficient Analytical Queries on Semantic Web Data Cubes

The amount of multidimensional data published on the Semantic Web (SW) is constantly increasing, due to initiatives such as Open Data and Open Government Data, among others. Models, languages, and tools, that allow obtaining valuable information efficiently, are thus required. Multidimensional data are typically represented as data cubes and exploited using online analytical processing (OLAP) techniques. The RDF Data Cube Vocabulary, also denoted QB, is the current W3C standard to represent statistical data on the SW. Given that QB does not include key features needed for OLAP analysis, in previous work we have proposed an extension, denoted QB4OLAP, to overcome this problem without the need of modifying already published data. Once data cubes are appropriately represented on the SW, we need mechanisms to analyze them. However, in the current state-of-the-art, writing efficient analytical queries over SW data cubes demands a deep knowledge of standards like RDF and SPARQL. These skills are unlikely to be found in typical analytical users. Further, OLAP languages like MDX are far from being easily understood by the final user. The lack of friendly tools to exploit multidimensional data on the SW is a barrier that needs to be broken to promote the publication of such data. This is the problem we address in this paper. Our approach is based on allowing analytical users to write queries using what they know best: OLAP operations over data cubes, without dealing with SW technicalities. For this, we devised CQL (standing for Cube Query Language), a simple, high-level query language that operates over data cubes. Taking advantage of structural metadata provided by QB4OLAP, we translate CQL queries into SPARQL ones. Then, we propose query improvement strategies to produce efficient SPARQL queries, adapting general-purpose SPARQL query optimization techniques. We evaluate our implementation using the Star Schema benchmark, showing that our proposal outperforms others. The QB4OLAP toolkit, a web application that allows exploring and querying (using CQL) SW data cubes, completes our contributions.

Manifestations of Metadata Structures in Research Datasets and Their Ontic Implications

ABSTRACTThe proliferation of research datasets and their availability in various repositories require metadata that provides sufficient context and organizational clarity to enable their reuse. However, datasets come in myriad forms, structures, and relationships. As characteristics of datasets vary across disciplines, it is reasonable to suggest that the methods by which they are discoverable by metadata should be informed by the considerations unique to differing research areas. As such, for their internal unity to be accurately represented it is necessary that their ontological characteristics be documented reflectively along the same ontic spectrums. This article will explore the relationship between metadata and dataset structures in order to illuminate ontic alignments between them and how this impacts contextualization. We will examine common data structures for dataset metadata and implications each contains. A survey of the types of datasets from the Life Sciences, Social Sciences, Humanities, Geospatial Information Systems, and Research Software will be examined for their ability to be accurately documented by common standards, particularly in discipline-specific repositories. Similarly, an investigation into how metadata is being used in different contexts and how various systems help or hinder its fullest functioning may point towards challenges operating on a deep level.

A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning

The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan's construction can affect the therapy outcome.

Metric assessments of books as families of works

We describe the intellectual and physical properties of books as manifestations, expressions, and works and assess the current indexing and metadata structure of monographs in the Book Citation Index (BKCI). Our focus is on the interrelationship of these properties in light of the Functional Requirements for Bibliographic Records (FRBR). Data pertaining to monographs were collected from the Danish PURE repository system as well as the BKCI (2005–2015) via their International Standard Book Numbers (ISBNs). Each ISBN was then matched to the same ISBN and family‐related ISBNs cataloged in two additional databases: OCLC‐WorldCat and Goodreads. With the retrieval of all family‐related ISBNs, we were able to determine the number of monograph expressions present in the BKCI and their collective relationship to one work. Our results show that the majority of missing expressions from the BKCI are emblematic (i.e., first editions of monographs) and that both the indexing and metadata structure of this commercial database could significantly improve with the introduction of distinct expression IDs (i.e., for every distinct edition) and unifying work‐related IDs. This improved metadata structure would support the collection of more accurate publication and citation counts for monographs and has implications for developing new indicators based on bibliographic levels.

Literature-Based Discovery by Learning Heterogeneous Bibliographic Information Networks

Literature-based discovery (LBD) research aims at finding effective computational methods for retrieving previously unknown information connections between clusters of research papers from disparate research areas. Existing methods encompass two general approaches. The first approach searches for these unknown connections by examining the textual contents of research papers. In addition to the existing textual features, the second approach incorporates structural features of scientific literatures, such as citation structures. These approaches, however, have not considered research papers' latent bibliographic metadata structures as important features that can be used for predicting previously unknown relationships between them This thesis investigates a new graph-based LBD method that exploits the latent bibliographic metadata connections between pairs of research papers. The heterogeneous bibliographic information network is proposed as an efficient graph-based data structure for modeling the complex relationships between these metadata. In contrast to previous approaches, this method seamlessly combines textual and citation information in the form of path-based metadata features for predicting future co-citation links between research papers from disparate research fields. The results reported in this thesis provide evidence that the method is effective for reconstructing the historical literature-based discovery hypotheses This thesis also investigates the effects of semantic modeling and topic modeling on the performance of the proposed method. For semantic modeling, a general-purpose word sense disambiguation technique is proposed to reduce the lexical ambiguity in the title and abstract of research papers. The experimental results suggest that the reduced lexical ambiguity did not necessarily lead to a better performance of the method. This thesis discusses some of the possible contributing factors to these results. Finally, topic modeling is used for learning the latent topical relations between research papers. The learned topic model is incorporated into the heterogeneous bibliographic information network graph and allows new predictive features to be learned. The results in this thesis suggest that topic modeling improves the performance of the proposed method by increasing the overall accuracy for predicting the future co-citation links between disparate research papers.

Implementasi Functional Requirement Bibliographic Resources (FRBR) dan Dampaknya pada Katalog Modern

A dynamic change in the collection management system encourages the libraries to re-new their cataloguing system. Functional Requirement Bibliographic Resources (FRBR) is an alternative cataloguing system to replace Anglo American Cataloguing Rules. International Federation of Library Association (IFLA) developed FRBR in 1997 and the FRBR stimulated the discussions among the librarians and their stakeholders. The purpose of this essay is to discuss the contribution FRBR for modern cataloguing and to argue the importance applying FRBR in the modern catalogues. FRBR has a positive contribution for developing the metadata. FRBR overcomes the interoperability issue by providing a standard of metadata. In addition, FRBR improves the accuracy in the information retrieval by using entity relationships. Meanwhile, there was an assumption that the financial and human development issues would interrupt the implementation of FRBR. However, applying FRBR gives more advantages rather than its drawbacks. Firstly, FRBR reduces the workload in the cataloguing process by avoiding data redundancies. Secondly, FRBR reduces the operational expense. Thirdly, FRBR has an ability to connect the complex metadata structure by using Resource Definition Framework (RFD) and Universal Resources Identifier (URI). Both RFD and URI features improve the accuracy of search engine for digital objects. In conclusion, FRBR gives a positive contribution for modern cataloguing due to effectivity in the retrieving information and efficiency in the cataloguing process.

Applying an Archetype-Based Approach to Electroencephalography/Event-Related Potential Experiments in the EEGBase Resource.

The purpose of this study is to investigate the feasibility of applying openEHR (an archetype-based approach for electronic health records representation) to modeling data stored in EEGBase, a portal for experimental electroencephalography/event-related potential (EEG/ERP) data management. The study evaluates re-usage of existing openEHR archetypes and proposes a set of new archetypes together with the openEHR templates covering the domain. The main goals of the study are to (i) link existing EEGBase data/metadata and openEHR archetype structures and (ii) propose a new openEHR archetype set describing the EEG/ERP domain since this set of archetypes currently does not exist in public repositories. The main methodology is based on the determination of the concepts obtained from EEGBase experimental data and metadata that are expressible structurally by the openEHR reference model and semantically by openEHR archetypes. In addition, templates as the third openEHR resource allow us to define constraints over archetypes. Clinical Knowledge Manager (CKM), a public openEHR archetype repository, was searched for the archetypes matching the determined concepts. According to the search results, the archetypes already existing in CKM were applied and the archetypes not existing in the CKM were newly developed. openEHR archetypes support linkage to external terminologies. To increase semantic interoperability of the new archetypes, binding with the existing odML electrophysiological terminology was assured. Further, to increase structural interoperability, also other current solutions besides EEGBase were considered during the development phase. Finally, a set of templates using the selected archetypes was created to meet EEGBase requirements. A set of eleven archetypes that encompassed the domain of experimental EEG/ERP measurements were identified. Of these, six were reused without changes, one was extended, and four were newly created. All archetypes were arranged in the templates reflecting the EEGBase metadata structure. A mechanism of odML terminology referencing was proposed to assure semantic interoperability of the archetypes. The openEHR approach was found to be useful not only for clinical purposes but also for experimental data modeling.