Common Schema Research Articles

Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning

Abstract Objectives Federated learning (FL) is a group of methodologies where statistical modelling can be performed without exchanging identifiable patient data between cooperating institutions. To realize its potential for AI development on clinical data, a number of bottlenecks need to be addressed. One of these is making data Findable-Accessible-Interoperable-Reusable (FAIR). The primary aim of this work is to show that tools making data FAIR allow consortia to collaborate on privacy-aware data exploration, data visualization, and training of models on each other’s original data. Methods We propose a “Schema-on-Read” FAIR-ification method that adapts for different (re)analyses without needing to change the underlying original data. The procedure involves (1) decoupling the contents of the data from its schema and database structure, (2) annotation with semantic ontologies as a metadata layer, and (3) readout using semantic queries. Open-source tools are given as Docker containers to help local investigators prepare their data on-premises. Results We created a federated privacy-preserving visualization dashboard for case mix exploration of 5 distributed datasets with no common schema at the point of origin. We demonstrated robust and flexible prognostication model development and validation, linking together different data sources—clinical risk factors and radiomics. Conclusions Our procedure leads to successful (re)use of data in FL-based consortia without the need to impose a common schema at every point of origin of data. Advances in knowledge This work supports the adoption of FL within the healthcare AI community by sharing means to make data more FAIR.

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

AbstractThe W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

From complex prepositions to complex subordinators: challenging generalizations

ABSTRACT This paper presents a first approximation of the relationships within and between complex prepositions and complex subordinators from a cognitive vantage point. Because of the similarities between the two categories at the syntactic and functional levels of analysis, we first hypothesize the existence of a common productive schema that encompasses members of both classes within the framework of Construction Grammar. We then put forward a methodology developed in Diachronic Construction Grammar to analyze the within- and between-category constructional links in complex prepositions and complex subordinators in American English. We use multivariate quantitative methods to highlight the similarities and dissimilarities between the categories and make them visually identifiable. The application of this methodology to historical corpus data reveals that contrary to our expectations, complex prepositions form a relatively stable schema over time, without converging towards a common schema with the loosely connected complex subordinators under scrutiny.

Sharing sensitive research data in the practice of personalised medicine

Fragmentation of health data and biomedical research data is a major obstacle for precision medicine based on data-driven decisions. The development of personalized medicine requires the efficient exploitation of health data resources that are extraordinary in size and complexity, but highly fragmented, as well as technologies that enable data sharing across institutions and even borders. Biobanks are both sample archives and data integration centers. The analysis of large biobank data warehouses in federated datasets promises to yield conclusions with higher statistical power. A prerequisite for data sharing is harmonization, i.e., the mapping of the unique clinical and molecular characteristics of samples into a unified data model and standard codes. These databases, which are aligned to a common schema, then make healthcare information available for privacy-preserving federated data sharing and learning. The re-evaluation of sensitive health data is inconceivable without the protection of privacy, the legal and conceptual framework for which is set out in the GDPR (General Data Protection Regulation) and the FAIR (findable, accessible, interoperable, reusable) principles. For biobanks in Europe, the BBMRI-ERIC (Biobanking and Biomolecular Research Infrastructure - European Research Infrastructure Consortium) research infrastructure develops common guidelines, which the Hungarian BBMRI Node joined in 2021. As the first step, a federation of biobanks can connect fragmented datasets, providing high-quality data sets motivated by multiple research goals. Extending the approach to real-word data could also allow for higher level evaluation of data generated in the real world of patient care, and thus take the evidence generated in clinical trials within a rigorous framework to a new level. In this publication, we present the potential of federated data sharing in the context of the Semmelweis University Biobanks joint project. Orv Hetil. 2023; 164(21): 811-819.

Character Change in Mainstream Movies: Structures of Moral Development

Character change is an essential component of Hollywood storytelling, yet little has been written about how it is typically structured. This paper addresses that deficit. Through close formal analysis of a small but narratively diverse group of films—Casablanca (1942), The Apartment (1960), About a Boy (2002) and Wild (2014)—evidence is presented for the existence of a common schema, one that is used to organize the ethical development of morally-flawed protagonists. While this represents only one type of character change, specifying its structural dynamics productively extends our knowledge of commercial cinema's narrative norms as they have persisted in history. It also provides a basis for challenging the view, most fully developed by Carroll (1984), Smith (1995) and Plantinga (2018), that sympathy with or allegiance to characters is a matter of general moral assessment. I argue that because the schema requires a protagonist to repeatedly fall short of an ideal morality, one that is always clearly delineated in the narrative, then allegiance to them cannot be the result of moral approval or their ethical development would be meaningless. My solution is to propose a pluralist account of allegiance, one which emphasizes the role of viewer concern for flawed protagonists who, in all of my examples, suffer sustained emotional and psychological distress.

Graph schema and best graph type to compare discrete groups: Bar, line, and pie.

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

A novel smart product-service system configuration method for mass personalization based on knowledge graph

Nowadays, manufacturing enterprises are struggling to satisfy personalized and dynamic user requirements. Smart Product-service System (Smart PSS), as a promising and sustainable business model, can respond to personalized and dynamic demands through effective configuration/reconfiguration processes. Mass personalization (MP), which emphasizes diversified values to improve user experience by considering both functional and affective elements, can significantly contribute to Smart PSS. However, Smart PSS field still lacks a unified knowledge representation framework and an effective configuration method in the MP context. Aiming to fulfill the gap, this research proposes a novel Smart PSS configuration method oriented to MP to meet personalized and dynamic requirements of users, especially satisfy affective and functional requirements simultaneously throughout the reconfiguration life-cycle. Due to the extreme complexity of heterogeneous data from Smart PSS and MP, Knowledge Graph (KG) is introduced as a powerful tool for managing intricate system knowledge. The common schemas of three KGs are constructed as the basis of configuration, with design knowledge, related field knowledge, and user information integrated comprehensively. KG-based question answering is combined with similarity calculation as a hybrid method in the configuration process to provide satisfactory and complete solutions. Moreover, the configuration process is placed in the reconfiguration life-cycle of Smart PSS to deal with dynamic changes through the latest knowledge in updating KGs. Furthermore, an illustrative case study of the intelligent management system for family medication is demonstrated. Through the comparison with other approaches, the proposed method is proved to be valuable for the implementation of Smart PSS configuration for MP.

A universal approach for multi-model schema inference

The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

Verbs of cutting in the middle construction: Examining the compositional cospecification of patientive vs. Agent-instrument middles

The purpose of this paper is to examine both prototypical Ergative-like middles (e.g. The cake cuts well) and their metonymically-motivated extensions, called Agent-Instrument middles (e.g. The saw cuts like a dream). The former incorporate as subjects patientive entities and the latter instruments. Both structures allow the incorporation of verbs of cutting, but they represent divergent portions of the action chain, and thus, they differ in the nature of their subject entities and their processes of compositional cospecification in terms of qualia structure. On the whole, on the basis of the prototype effects of the middle construction analysed here, they should be considered proper middles. This paper is based on a corpus study of contextualised examples (1700+) to examine their collostructional schema in the English middle construction (cf. Stefanowitsch and Gries 2003). Particularly, we explore the Verb + Adverb collocation found in middles by focusing on 29 predicates belonging to Levin’s (1993: 156) class of verbs of cutting, in combination with facility-/quality-oriented adjuncts (cf. Davidse and Heyvaert, 2007). Following a cognitive perspective, this paper examines the semantic roles of the subject referents analysed (patients and instruments), as well as their distinct processes of compositional cospecification as factors that contribute to the prototype effects of the middle construction. On the basis of corpus data, despite the syntactic, semantic, and pragmatic common schemas found in prototypical Ergative-like middles and Agent-Instrument middles, the prototype effects of the middle construction highlight the divergent processes of compositional cospecification that these two types of middles follow. More specifically, a shift of semantic weight from a telic to a constitutive value occurs in Agent-Instrument middles, while there is lack of such a semantic shift in terms of qualia structure in prototypical Ergative-like middles with patientive subjects. Thus the middle construction cannot be considered as a discrete category of its own, but rather a prototype category (cf. Taylor, 1995; Hundt, 2007) that permits the accommodation of central and peripheral members. Consequently, in addition to the traditionally accepted structures in which the subject referent is a patientive entity, other less archetypal nominals can occur as middle subjects, particularly, instruments whose action affects implied and patientive entities.

A bi-level formulation for multiple kernel learning via self-paced training

Multiple kernel learning (MKL) is a crucial issue which has been widely researched over the last two decades. Although existing MKL algorithms have achieved satisfactory performance in a broad range of applications, these methods do not adequately consider the adverse effects of unreliable or less reliable instances. To handle this shortcoming, we formulate multiple kernel learning in a bi-level learning paradigm consisting of the kernel combination weight learning (KWL) stage and the self-paced learning (SPL) stage, which alternatively negotiate with each other. The KWL stage dynamically absorbs reliable instances into model learning to accurately capture neighborhood relationships and obtains kernel coefficients via maximizing both global and local kernel alignment in a common schema. The SPL stage automatically evaluates the reliability of training samples via self-paced training. The extensive experiments indicate the robustness and superiority of the presented approach in comparison with existing MKL methods.

Metadata Integration Framework for Data Integration of Socio-Cultural Anthropology Digital Repositories: A Case Study of Princess Maha Chakri Sirindhorn Anthropology Centre

Data integration is one of the most challenging tasks for digital collections whose data are stored across various repositories. Data integration across digital repositories has several challenges. First, data heterogeneity in terms of data schema and data values usually occurs across diverse data sources. Second, heterogeneity in data representation and semantic issues are among the problems. The same data may appear in different repositories with varied data representations, i.e., metadata schema. Recent research has focused on matching several related metadata schemas. In this paper, a metadata integration framework is proposed to support digital repositories in socio-cultural anthropology at the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC), Thailand. The proposed framework is defined based on the Metadata Lifecycle Model (MLM). It utilizes non-procedural schema mappings to express data relationships in diverse schemas. A case study of metadata integration over the SAC digital repositories was conducted to validate the framework. The SAC common metadata schema was designed to support data mapping across 13 digital repositories. The SAC “One Search” system was developed to exemplify the system implementation of the framework. Evaluation results showed that the proposed metadata integration framework can support domain experts in socio-cultural anthropology in unified searching across the repositories.

Standardized and extensible reference data model for clinical research in Living Labs

Over the last few years, Living Labs (LL) have emerged as resilient research and innovation infrastructures to facilitate and promote activities, which can deal with the complexity of health and wellbeing research. Such systems integrate multiple types of data, including personal info or generated by ICT tools and devices (i.e., wearable sensors). Healthcare data models have the ability to integrate such data and focus on relevant content that will follow a specific data representation template that can be extended based on the needs of each LL. In this study, we propose a reference metamodel based on existing schemas to improve the efficiency of the LL research by pursuing a labor-intensive workflow with a hierarchical structure that simplifies the transformation process and minimizes data variation. OmH library has been used as the basis of the metamodel along with other standards in healthcare to design a complete and straightforward data reference model for LL. The presented data model has been designed within the Vitalise (H2020-INFRAIA 2021-2024) project and visualizes representations of the collected data elements and their relations. The main purpose is to provide an extensible data model that: 1) harmonizes representation formats of the information exchanged that will be used from the LL, 2) empowers other LL to develop solutions that adhere to this common definition and 3) provides a shared, common schema. The designed model will precisely represent and handle data from different devices or platforms (i.e., heart rate, BMI) and efficiently describe the collected dataset to produce meaningful metadata.

Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally.

Different graph types might differ in group comparison due to differences in underlying graph schemas. Thus, this study examined whether graph schemas are based on perceptual features (i.e., each graph has a specific schema) or common invariant structures (i.e., graphs share several common schemas), and which graphic type (bar vs. dot vs. tally) is the best to compare discrete groups. Three experiments were conducted using the mixing-costs paradigm. Participants received graphs with quantities for three groups in randomized positions and were given the task of comparing two groups. The results suggested that graph schemas are based on a common invariant structure. Tally charts mixed either with bar graphs or with dot graphs showed mixing costs. Yet, bar and dot graphs showed no mixing costs when paired together. Tally charts were the more efficient format for group comparison compared to bar graphs. Moreover, processing time increased when the position difference of compared groups was increased.

Virtual Learning in Graduate Medical Education: Applying Learning Theory for Effective Educational Videos.

Virtual Learning in Graduate Medical Education: Applying Learning Theory for Effective Educational Videos.

India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research

Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.

Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables

Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities are mapped to blocks. Blocking is necessary for preempting comparing all possible pairs of entities, as (in the second ‘similarity’ step) only entities within blocks are paired and compared, allowing for significant computational savings with a minimal loss of performance. Unfortunately, learning a blocking scheme in an unsupervised fashion is a non-trivial problem, and it has not been properly explored for heterogeneous, semi-structured datasets, such as are prevalent in industrial and Web applications. This article presents an unsupervised algorithmic pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on KGs, as well as structurally heterogeneous tables that may not share a common schema. We evaluate the approach on six real-world dataset pairs, and show that it is competitive with supervised and semi-supervised baselines.

National Immunization Technical Advisory Groups (NITAGs): A schema for evaluating and comparing foundation instruments and NITAG operations

The individual and community health benefits of vaccination have received significant attention and are now well understood. However, much less is known about immunization as a regulated space, its principles and standards and its institutions and instruments. In 2011, the World Health Organization (WHO) recommended that National Immunization Technical Advisory Groups (NITAGs) be established in each member country. NITAGSs are envisioned as independent, multidisciplinary expert groups within the national immunization framework, tasked with providing evidence-based evaluations and recommendations to governmental decision-makers about specific vaccines, vaccine-dosing, vaccine program development and immunization policy and practice more generally. As of 2020, 171 WHO countries have formed NITAGs. The widespread formation of NITAGs has highlighted an absence of sustained scholarship around immunization as a policy area subject to law, and it has given rise to many governance and operational questions. In 2017, for example, representatives of the Global NITAG Network (GNN) agreed that there is insufficient understanding of the impact of law on the functioning of NITAGs. Similarly, the Strategic Advisory Group of Experts on Immunization called for research into the variety of ways in which legislation and regulation have been used to promote immunization at a national level and to achieve different ends in relation to immunization and NITAG functioning. In answer to this call, the NITAG Environmental Scan (Project) was initiated. Drawing on scholarship around good governance, this article offers a comprehensive common assessment schema for critically and systematically approaching questions about NITAG governance and operation, applying that schema to the foundation instrument of the Côte d’Ivoire’s NITAG. It also reports on how well the schema is engaged by the NITAG foundation instruments in other GNN countries.

The new dynamics of residential aged care in Australia: continuity and change.

Objective This paper tests the hypothesis that increases in recorded dependency levels of permanent residential aged care clients are associated with reduced length of stay and higher turnover. A secondary objective is to compare the Aged Care Funding Instrument with its predecessor, the Resident Classification Scale, on a common schema. Methods Administrative data for all Commonwealth-subsidised residential aged care services in Australia from 2008-09 to 2018-19 were obtained from the National Aged Care Data Clearinghouse. More than 750000 episodes of permanent residential aged care were analysed. The categories from the two rating systems were mapped to a six-level schema, primarily based on the dollar value of the categories at the time of transition. Results There was a strong trend towards higher dependency ratings across admissions, residents, and separations. However, contrary to expectation, measures of system activity showed a slowing of the system: length of stay increased and turnover decreased. Conclusions The mapping of dependency rating schemes to a common rating enables the analysis of long-term trends in residential care dynamics. There is no evidence that the marked increases in reported dependency ratings led to accelerated system activity, consistent with an earlier study. This analysis forms a solid base for ongoing analysis of care appraisals in the context of a possible new rating scheme. It highlights the interplay between policy changes and provider behaviour, and the need for robust data to monitor care appraisals and system dynamics. What is known about the topic? Residential aged care subsidies are determined by care needs in relation to assessed dependency levels, using the Aged Care Funding Instrument since 2008, and before that, the Resident Classification Scale. Between 2008-09 and 2018-19, there was considerable growth in residents classified at more dependent levels, and this would be expected to result in greater turnover in the system. What does this paper add? This paper maps the rating schemes to a simplified, common rating that enables the analysis of long-term trends in residential care dynamics. It shows that the system is slowing, contrary to the trends expected if residents were more frail as the reported ratings imply. The paper examines possible explanations of these trends, and addresses policy implications. What are the implications for practitioners? In the context of a potential new client-dependency classification, this study shows the importance of robust measures of the dynamics of the system-and the underlying data-vis -à -vis the means by which client dependency is assessed.

Fostering Generative Partnerships in an Inclusive Business Model

How does a social venture build multiple partnerships among stakeholders to enable a wide range of social value propositions and alleviate economic inequality? We address this question by developing a new concept on generative partnerships, defined as the collaboration between nonprofits and business organizations to co-create social value proposition and attract enhanced collaboration to solve social issues in an inclusive business model. We study two Chinese cooperatives, Co-op Lishui Shangeng (L) and Co-op Wuyang Chunyu (W), to show how they created a social ecosystem through an inclusive business model. These two cooperatives have also developed co-brandings L and W, respectively. We find that hybrid organizations could generate partnerships among different agents to form a social ecosystem. Drawing on the generative relationship theory, we identify four stages of fostering generative partnerships: (1) a value blueprint, (2) a pilot demonstration, (3) scaling-up, and (4) snowballing. After developing propositions regarding directedness, heterogeneity, and interaction of agents, we further elaborate two common schemas on the process of generating hybrid partnerships in this social ecosystem.

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Oceanographic research is a multidisciplinary endeavor that involves the acquisition of an increasing amount of in-situ and remotely sensed data. A large and growing number of studies and data repositories are now available on-line. However, manually integrating different datasets is a tedious and grueling process leading to a rising need for automated integration tools. A key challenge in oceanographic data integration is to map between data sources that have no common schema and that were collected, processed, and analyzed using different methodologies. Concurrently, artificial agents are becoming increasingly adept at extracting knowledge from text and using domain ontologies to integrate and align data. Here, we deconstruct the process of ocean science data integration, providing a detailed description of its three phases: discover, merge, and evaluate/correct. In addition, we identify the key missing tools and underutilized information sources currently limiting the automation of the integration process. The efforts to address these limitations should focus on (i) development of artificial intelligence-based tools for assisting ocean scientists in aligning their schema with existing ontologies when organizing their measurements in datasets; (ii) extension and refinement of conceptual coverage of – and conceptual alignment between – existing ontologies, to better fit the diverse and multidisciplinary nature of ocean science; (iii) creation of ocean-science-specific entity resolution benchmarks to accelerate the development of tools utilizing ocean science terminology and nomenclature; (iv) creation of ocean-science-specific schema matching and mapping benchmarks to accelerate the development of matching and mapping tools utilizing semantics encoded in existing vocabularies and ontologies; (v) annotation of datasets, and development of tools and benchmarks for the extraction and categorization of data quality and preprocessing descriptions from scientific text; and (vi) creation of large-scale word embeddings trained upon ocean science literature to accelerate the development of information extraction and matching tools based on artificial intelligence.