A human factors experimental comparison of SQL and QBE

Yen, Minnie Yi-Miin; Scamell, Richard W.

doi:10.1109/32.223806

Cited by 43 publications

(25 citation statements)

References 22 publications

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“…Therefore, for a VQS, effectiveness is rather measured in terms of a binary measure of success (i.e., correct/incorrect query), which could be accompanied with a fuzzy measure (i.e., rate of correctness) weighting and combining different type of query errors (cf. [36,19]). As far as SPARQL is concerned, the measure of correctness could be built on the semantic similarity between the user query and correct query (cf.…”

Section: Usability and Expressivenessmentioning

confidence: 99%

Qualifying Ontology-Based Visual Query Formulation

Soylu

Giese

2015

Advances in Intelligent Systems and Computing

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Abstract. This paper elaborates on ontology-based end-user visual query formulation, particularly for users who otherwise cannot/do not desire to use formal textual query languages to retrieve data due to the lack of technical knowledge and skills. Then, it provides a set of quality attributes and features, primarily elicited via a series of industrial enduser workshops and user studies carried out in the course of an industrial EU project, to guide the design and development of successor visual query systems.

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Section: Usability and Expressivenessmentioning

confidence: 99%

Qualifying Ontology-Based Visual Query Formulation

Soylu

Giese

2015

Advances in Intelligent Systems and Computing

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“…When retrieving information from database, a good interface allows users to formulate their queries accurately and quickly (Curl, Olfman & Sutzinger 1998). Key interface factors which potentially a!ect user performance during query formulation are the data model and query language used (Ogden, 1985;Chan, Wei & Siau, 1993;Yen & Scamell, 1993;Chan & Lim, 1998). Moreover, these factors may be moderated by the level of complexity of the queries (Small & Weldon, 1983).…”

Section: Introductionmentioning

confidence: 99%

Three important determinants of user performance for database retrieval

Chan

Tan

Wei

1999

International Journal of Human-Computer Studies

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“…Query-By-Example [3] is a good solution for non-sophisticated user, but it is limited by domain knowledge to set sample queries, and cannot support user-defined queries very well. There are already lots of works using visual interface with "Drag-and-Drop" graphic icons or objects to formulate general SQL queries for RDBMS, such as Ajax Query Builder [4].…”

Section: Related Workmentioning

confidence: 99%

ViMDQL: An Easy-to-Use Drag-and-Drop Visual Query Composer for Multidimensional Data

2017

JWSA

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Abstract. In this study, we present ViMDQL, a useful system to tack the challenge of composing multidimensional data based analytical queries easily. Currently, fundamental query blocks such as import and export, create, retrieve, update, delete are supported, which can be used to loading and exporting data, creating, retrieving, updating, join, sampling and removing multidimensional data. Analytic functionalities such as aggregation, statistics are also supported. We demonstrated that, since ViMDQL make users can express their query intent by drag and drop to link query blocks together, which enable users can easily compose queries like the Stacker Game, it has been proved to be a productivity tool for graphically building multidimensional data based queries.

show abstract

A human factors experimental comparison of SQL and QBE

Cited by 43 publications

References 22 publications

Qualifying Ontology-Based Visual Query Formulation

Qualifying Ontology-Based Visual Query Formulation

Three important determinants of user performance for database retrieval

ViMDQL: An Easy-to-Use Drag-and-Drop Visual Query Composer for Multidimensional Data

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