Path Expressions in SQL

Junkkari, Marko; Vainio, Johanna; Iltanen, Kati; Arvola, Paavo; Kari, Heidi; Kekäläinen, Jaana

doi:10.4018/jdm.2016070101

Cited by 4 publications

(1 citation statement)

References 35 publications

(36 reference statements)

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“…Architecturally, a distinct practical advantage is that it achieves this through a series of automated transformations, rather than (manual or automated) specifications of non-trivial mappings in a separate mapping layer. A further practical advantage is the support for path queries, which has been shown in user experiments to make query formulation faster with at least the same level of accuracy or fewer errors [10,11].…”

Section: Introductionmentioning

confidence: 99%

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Fillottrani

Keet

2020

Data Intelligence

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Modern information systems require the orchestration of ontologies, conceptual data modeling techniques, and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs. A major question in delivering such systems, is which components to design and put together to make up the required “knowledge to data” pipeline, as each component and process has trade-offs. In this paper, we introduce a new knowledge-to-data architecture, KnowID. It pulls together both recently proposed components and we add novel transformation rules between Enhanced Entity-Relationship (EER) and the Abstract Relational Model to complete the pipeline. KnowID's main distinctive architectural features, compared to other ontology-based data access approaches, are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.

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Section: Introductionmentioning

confidence: 99%

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Fillottrani

Keet

2020

Data Intelligence

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Path Querying in Graph Databases: A Systematic Mapping Study

García,

Angles

2024

IEEE Access

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Path querying refers to the evaluation of path queries in a graph database. New research in this topic is crucial for the development of graph database systems as path queries are associated with relevant use-cases and application domains. The aim of this article is to identify and establish what is currently known about path querying in graph databases. To achieve this, we conducted a systematic mapping study (SMS) in which we explored four digital libraries and collected research papers published from 1970 to 2022. These articles were filtered, classified and analyzed to extract quantitative and qualitative information which is presented in this article. Additionally, we provide a concise description of keywords, use-cases and application domains associated with path querying in graph databases.

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Effect of semantic distance on learning structured query language: An empirical study

Shin

2022

Front. Psychol.

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Students of database courses usually encounter difficulties in learning structured query language (SQL). Numerous studies have been conducted to improve how students learn SQL. However, learning SQL remains difficult. This study analyzed the difficulties in learning SQL from the viewpoint of semantic distance by using semantic network theory. An experiment involving a database course was performed to assess the influence of semantic distance on learners’ understanding of SQL. The participants were requested to perform a query-writing task at the end of the course to investigate their understanding of SQL. The data analysis results indicated that the participants developed a better understanding of the formulation-to-planning transformation than the planning-to-coding transformation. This implies that the semantic distance of the planning-to-coding transformation is greater than that of the formulation-to-planning transformation, and the semantic distance of the planning-to-coding transformation is attributable to the semantic transformation from natural language to SQL, which are two essentially different languages and belong to different knowledge categories. Accordingly, this study concludes that SQL learning difficulties can mainly be ascribed to the planning-to-coding transformation because the large semantic distance. The findings suggest that SQL instructions should emphasize the semantic mapping of the planning-to-coding transformation by incorporating materials related to the transformation and should shorten the semantic distance involved in learning SQL. These two principles can be used to evaluate the effectiveness of SQL teaching methods in assisting SQL learning, and motivate researchers to develop more effective teaching methods from the viewpoint of semantic distance.

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Path Expressions in SQL

Cited by 4 publications

References 35 publications

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Path Querying in Graph Databases: A Systematic Mapping Study

Effect of semantic distance on learning structured query language: An empirical study

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