Automatic generation of trigger rules for integrity enforcement in relational databases with view definition

2019 ◽

Vol 15 (09) ◽

pp. 4

Author(s):

Redouane Esbai ◽

Fouad Elotmani ◽

Fatima Zahra Belkadi

Keyword(s):

Big Data ◽

Relational Databases ◽

Automatic Generation ◽

Transformation Model ◽

Model Driven ◽

Transformation Rules ◽

New Concepts ◽

Nosql Database ◽

Meta Object Facility ◽

View Transformation

<span>The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.</span>

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Automatic Generation of Scripts for Database Creation from Scenario Descriptions

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v7i330180 ◽

2021 ◽

pp. 34-48

Author(s):

L. W. Amarasinghe ◽

R. D. Nawarathna

Keyword(s):

Language Processing ◽

Web Application ◽

Relational Databases ◽

Automatic Generation ◽

Relational Model ◽

Free Text ◽

Software Application ◽

Requirement Specification ◽

Database Technology ◽

Computer Science Faculty

Aims: Database creation is the most critical component of the design and implementation of any software application. Generally, the process of creating the database from the requirement specification of a software application is believed to be extremely hard. This study presents a method to automatically generate database scripts from a given scenario description of the requirement specification. Study Design: The method is developed based on a set of natural language processing (NLP) techniques and a few algorithms. Standard database scenario descriptions presented in popular textbooks on Database Design are used for the validation of the method. Place and Duration of Study: Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka, Between December 2019 to December 2020. Methodology: The description of the problem scenario is processed using NLP operations such as tokenization, complex word handling, basic group handling, complex phrase handling, structure merging, and template construction to extract the necessary information required for the entity relational model. New algorithms are proposed to automatically convert the entity relational model to the logical schema and finally to the database script. The system can generate scripts for relational databases (RDB), object relational databases (ORDB) and Not Only SQL (NoSQL) databases. The proposed method is integrated into a web application where the users can type the scenario in natural or free text. The user can select the type of database (i.e., one of RDB, ORDB, NoSQL) considered in their system and accordingly the application generates the SQL scripts. Results: The proposed method was evaluated using 10 scenario descriptions connected to 10 different domains such as company, university, airport, etc. for all three types of databases. The method performed with impressive accuracies of 82.5%, 84.0% and 83.5% for RDB, ORDB and NoSQL scripts, respectively. Conclusion: This study is mainly focused on the automatic generation of SQL scripts from scenario descriptions of the requirement specification of a software system. Overall, the developed method helps to speed up the database development process. Further, the developed web application provides a learning environment for people who are novices in database technology.

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SpeakQL Natural Language to SQL

ITM Web of Conferences ◽

10.1051/itmconf/20214003018 ◽

2021 ◽

Vol 40 ◽

pp. 03018

Author(s):

Dhairya Shah ◽

Aniruddha Das ◽

Aniket Shahane ◽

Dharmik Parikh ◽

Pranit Bari

Keyword(s):

Natural Language ◽

Language Processing ◽

Relational Databases ◽

Query Language ◽

Automatic Generation ◽

Query Languages ◽

Natural Language Interface ◽

Open Issue ◽

Conventional Structure ◽

Structure Query

Incorporating SQL questions from normal language is a long-standing open issue and has been drawing in extensive intrigue as of late. Natural Language Interface (NLI) is the confluence of Natural Language Processing (NLP) and Human-Computer Interaction, which allows interaction between humans and computers through the utilization of Natural Language. Here we are gonna deal with the problem of automatic generation of Structured Query Language (SQL) queries. SQL is a database language for querying and manipulating relational databases. Despite the spectacular rise in the acceptance of relational databases, there is a fundamental limitaion to the ability to fetch data from those databases. One of the major reasons for this is the fact that the users of these relational databases need to comprehend convoluted structured query languages. In this body of work, we present an interface that allows users to interact with the databases using Natural Lanaguage as opposted to the conventional structure query languages.

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The Role of Visual Distractors in the Simon Effect

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000382 ◽

2017 ◽

Vol 64 (6) ◽

pp. 387-397 ◽

Cited By ~ 1

Author(s):

Luisa Lugli ◽

Stefania D’Ascenzo ◽

Roberto Nicoletti ◽

Carlo Umiltà

Keyword(s):

Simon Effect ◽

Simon Task ◽

Automatic Generation ◽

Spatial Code ◽

Attentional Model ◽

Previous Evidence ◽

Spatial Stimulus ◽

Attentional Resources ◽

Code Changes

Abstract. The Simon effect lies on the automatic generation of a stimulus spatial code, which, however, is not relevant for performing the task. Results typically show faster performance when stimulus and response locations correspond, rather than when they do not. Considering reaction time distributions, two types of Simon effect have been individuated, which are thought to depend on different mechanisms: visuomotor activation versus cognitive translation of spatial codes. The present study aimed to investigate whether the presence of a distractor, which affects the allocation of attentional resources and, thus, the time needed to generate the spatial code, changes the nature of the Simon effect. In four experiments, we manipulated the presence and the characteristics of the distractor. Findings extend previous evidence regarding the distinction between visuomotor activation and cognitive translation of spatial stimulus codes in a Simon task. They are discussed with reference to the attentional model of the Simon effect.

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