SchenQL: A Concept of a Domain-Specific Query Language on Bibliographic Metadata

2019 ◽  
pp. 239-246 ◽  
Author(s):  
Christin Katharina Kreutz ◽  
Michael Wolz ◽  
Ralf Schenkel
Keyword(s):  
Query Language ◽  
Domain Specific

scholarly journals Design principles of the Metadata Querying Language (MQL) implemented in the ATLAS Metadata Interface (AMI) ecosystem

2020 ◽  
Vol 245 ◽  
pp. 04044
Author(s):  
Jérôme Fulachier ◽  
Jérôme Odier ◽  
Fabian Lambert
Keyword(s):  
Query Language ◽  
Design Principles ◽  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific ◽  
Complex Queries ◽  
Structured Query Language

This document describes the design principles of the Metadata Querying Language (MQL) implemented in ATLAS Metadata Interface (AMI), a metadata-oriented domain-specific language allowing to query databases without knowing the relation between tables. With this simplified yet generic grammar, MQL permits writing complex queries more simply than with Structured Query Language (SQL).

scholarly journals Building Quranic stories ontology using MappingMaster domain-specific language

2022 ◽  
Vol 12 (1) ◽  
pp. 684
Author(s):  
Rusul Yousif Alsalhee ◽  
Abdulhussein Mohsin Abdullah
Keyword(s):  
Query Language ◽  
Role Modeling ◽  
Domain Specific Language ◽  
Modeling Language ◽  
Specific Language ◽  
Domain Specific ◽  
The People ◽  
Holy Quran ◽  
The Holy Quran ◽  
Object Role Modeling

<p>The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL)technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.</p>

scholarly journals A semi-automated BPMN-based framework for detecting conflicts between security, data-minimization, and fairness requirements

2020 ◽  
Vol 19 (5) ◽  
pp. 1191-1227 ◽  
Author(s):  
Qusai Ramadan ◽  
Daniel Strüber ◽  
Mattia Salnitri ◽  
Jan Jürjens ◽  
Volker Riediger ◽  
...  
Keyword(s):  
Data Storage ◽  
Business Processes ◽  
User Study ◽  
Query Language ◽  
Healthcare Management ◽  
Security Requirements ◽  
Domain Specific ◽  
Trade Offs ◽  
Legal Sanctions ◽  
Context Specific

Abstract Requirements are inherently prone to conflicts. Security, data-minimization, and fairness requirements are no exception. Importantly, undetected conflicts between such requirements can lead to severe effects, including privacy infringement and legal sanctions. Detecting conflicts between security, data-minimization, and fairness requirements is a challenging task, as such conflicts are context-specific and their detection requires a thorough understanding of the underlying business processes. For example, a process may require anonymous execution of a task that writes data into a secure data storage, where the identity of the writer is needed for the purpose of accountability. Moreover, conflicts not arise from trade-offs between requirements elicited from the stakeholders, but also from misinterpretation of elicited requirements while implementing them in business processes, leading to a non-alignment between the data subjects’ requirements and their specifications. Both types of conflicts are substantial challenges for conflict detection. To address these challenges, we propose a BPMN-based framework that supports: (i) the design of business processes considering security, data-minimization and fairness requirements, (ii) the encoding of such requirements as reusable, domain-specific patterns, (iii) the checking of alignment between the encoded requirements and annotated BPMN models based on these patterns, and (iv) the detection of conflicts between the specified requirements in the BPMN models based on a catalog of domain-independent anti-patterns. The security requirements were reused from SecBPMN2, a security-oriented BPMN 2.0 extension, while the fairness and data-minimization parts are new. For formulating our patterns and anti-patterns, we extended a graphical query language called SecBPMN2-Q. We report on the feasibility and the usability of our approach based on a case study featuring a healthcare management system, and an experimental user study.

scholarly journals Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas

2021 ◽  
Author(s):  
Alexandr Uciteli ◽  
Christoph Beger ◽  
Jonas Wagner ◽  
Alexander Kiel ◽  
Frank A. Meineke ◽  
...  
Keyword(s):  
Query Language ◽  
Medical Data ◽  
Medical Sciences ◽  
Domain Specific ◽  
Management Platform ◽  
Wide Range ◽  
Data Portal ◽  
Interactive Models ◽  
Ontological Framework ◽  
Cdisc Odm

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.

Natural Language to SQL Generation for Observational Study Designs: Current Challenges and Possible Directions (Preprint)

2020 ◽  
Author(s):  
Han Wang ◽  
Wesley Yeung ◽  
Mengling Feng
Keyword(s):  
Natural Language ◽  
Observational Studies ◽  
Relational Databases ◽  
Query Language ◽  
Data Extraction ◽  
Process Data ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Study Designs

UNSTRUCTURED Electronic Health Record (EHR) systems used in hospitals and healthcare institutes generate vast amounts of data stored in relational databases. Structured Query Language (SQL) is a common language used to update, extract and pre-process data in EHR databases. Pre-processing is a necessary step before statistical modeling and causal inference studies can be carried out in observational studies. Data extraction and pre-processing using SQL require a collaborative effort between data engineers and researchers such as clinicians or biostatisticians. Natural Language to SQL (NL2SQL) models converts study designs in natural language to SQL queries to obtain the desired cohort and risk factors. While they cannot completely replace the need for cross-disciplinary collaboration, they have the potential to enable clinicians and biostatisticians who are not trained in SQL to explore EHR databases on their own and reduce the burden placed on data engineers by automating less-complex tasks. There has been substantial research on NL2SQL tasks on general knowledge databases but their application in EHR databases that contain domain-specific knowledge are not well studied. In this paper, we will introduce the general NL2SQL tasks, and discuss in-depth about the potential challenges in developing NL2SQL tools for EHR databases.

Concept-Oriented Query Language for Data Modeling and Analysis

2011 ◽  
pp. 85-101 ◽  
Author(s):  
Alexandr Savinov
Keyword(s):  
Partial Order ◽  
Query Language ◽  
Data Modeling ◽  
Multidimensional Analysis ◽  
Inclusion Relation ◽  
Modeling And Analysis ◽  
Domain Specific

This chapter describes a novel query language, called the concept-oriented query language (COQL), and demonstrates how it can be used for data modeling and analysis. The query language is based on a novel construct, called concept, and two relations between concepts, inclusion and partial order. Concepts generalize conventional classes and are used for describing domain-specific identities. Inclusion relation generalizes inheritance and is used for describing hierarchical address spaces. Partial order among concepts is used to define two main operations: projection and de-projection. This chapter demonstrates how these constructs are used to solve typical tasks in data modeling and analysis such as logical navigation, multidimensional analysis, and inference.

A Domain-Specific Query Language for Information Services Mash-up

2008 ◽  
Author(s):  
Weilong Ding ◽  
Jing Cheng ◽  
Kaiyuan Qi ◽  
Yan Li ◽  
Zhuofeng Zhao ◽  
...  
Keyword(s):  
Query Language ◽  
Information Services ◽  
Domain Specific

scholarly journals SchenQL: in-depth analysis of a query language for bibliographic metadata

2021 ◽  
Author(s):  
Christin Katharina Kreutz ◽  
Michael Wolz ◽  
Jascha Knack ◽  
Benjamin Weyers ◽  
Ralf Schenkel
Keyword(s):  
Information Needs ◽  
User Study ◽  
Query Language ◽  
Information Access ◽  
Domain Experts ◽  
Domain Specific ◽  
Complex Information ◽  
Depth Analysis ◽  
Information Exploration ◽  
High Level

AbstractInformation access to bibliographic metadata needs to be uncomplicated, as users may not benefit from complex and potentially richer data that may be difficult to obtain. Sophisticated research questions including complex aggregations could be answered with complex SQL queries. However, this comes with the cost of high complexity, which requires for a high level of expertise even for trained programmers. A domain-specific query language could provide a straightforward solution to this problem. Although less generic, it can support users not familiar with query construction in the formulation of complex information needs. In this paper, we present and evaluate SchenQL, a simple and applicable query language that is accompanied by a prototypical GUI. SchenQL focuses on querying bibliographic metadata using the vocabulary of domain experts. The easy-to-learn domain-specific query language is suitable for domain experts as well as casual users while still providing the possibility to answer complex information demands. Query construction and information exploration are supported by a prototypical GUI. We present an evaluation of the complete system: different variants for executing SchenQL queries are benchmarked; interviews with domain-experts and a bipartite quantitative user study demonstrate SchenQL’s suitability and high level of users’ acceptance.

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