SFQI: Semi-Formal Query Language Interface to Relational Databases

1995 ◽  
pp. 104-124
Author(s):  
M. N. MdSap ◽  
D. R. McGregor
Keyword(s):  
Relational Databases ◽  
Query Language

GuessXQ

2013 ◽  
pp. 57-76
Author(s):  
Daniela Morais Fonte ◽  
Daniela da Cruz ◽  
Pedro Rangel Henriques ◽  
Alda Lopes Gancarski
Keyword(s):  
Information Retrieval ◽  
Learning Process ◽  
Web Application ◽  
Relational Databases ◽  
Query Language ◽  
General Purpose ◽  
Considerable Effort ◽  
Document Structure ◽  
Query By Example ◽  
Xml Documents

XML is a widely used general-purpose annotation formalism for creating custom markup languages. XML annotations give structure to plain documents to interpret their content. To extract information from XML documents XPath and XQuery languages can be used. However, the learning of these dialects requires a considerable effort. In this context, the traditional Query-By-Example methodology (for Relational Databases) can be an important contribution to leverage this learning process, freeing the user from knowing the specific query language details or even the document structure. This chapter describes how to apply the Query-By-Example concept in a Web-application for information retrieval from XML documents, the GuessXQ system. This engine is capable of deducing, from an example, the respective XQuery statement. The example consists of marking the desired components directly on a sample document, picked-up from a collection. After inferring the corresponding query, GuessXQ applies it to the collection to obtain the desired result.

Web Services as XML Data Sources in Enterprise Information Integration

2011 ◽  
pp. 972-985
Author(s):  
Ákos Hajnal ◽  
Tamás Kifor ◽  
Gergely Lukácsy ◽  
László Z. Varga
Keyword(s):  
Web Services ◽  
Web Service ◽  
Information Integration ◽  
Digital Libraries ◽  
Relational Databases ◽  
Query Language ◽  
Data Sources ◽  
Enterprise Information ◽  
Xml Data ◽  
The Web

More and more systems provide data through web service interfaces and these data have to be integrated with the legacy relational databases of the enterprise. The integration is usually done with enterprise information integration systems which provide a uniform query language to all information sources, therefore the XML data sources of Web services having a procedural access interface have to be matched with relational data sources having a database interface. In this chapter the authors provide a solution to this problem by describing the Web service wrapper component of the SINTAGMA Enterprise Information Integration system. They demonstrate Web services as XML data sources in enterprise information integration by showing how the web service wrapper component integrates XML data of Web services in the application domain of digital libraries.

scholarly journals Automated Generation of Personal Data Reports from Relational Databases

2011 ◽  
Vol 10 (02) ◽  
pp. 193-208 ◽  
Author(s):  
Georgios John Fakas ◽  
Ben Cawley ◽  
Zhi Cai
Keyword(s):  
Prior Knowledge ◽  
Data Protection ◽  
Relational Databases ◽  
Query Language ◽  
Personal Data ◽  
Query Languages ◽  
Hash Tables ◽  
Automated Generation ◽  
Novel Approach ◽  
Performance Results

This paper presents a novel approach for extracting personal data and automatically generating Personal Data Reports (PDRs) from relational databases. Such PDRs can be used among other purposes for compliance with Subject Access Requests of Data Protection Acts. Two methodologies with different usability characteristics are introduced: (1) the GDSBased Method and (2) the By Schema Browsing Method. The proposed methdologies combine the use of graphs and query languages for the construction of PDRs. The novelty of these methodologies is that they do not require any prior knowledge of either the database schema or of any query language by the users. An optimisation algorithm is proposed that employs Hash Tables and reuses already found data. We conducted several queries on two standard benchmark databases (i.e. TPC-H and Microsoft Northwind) and we present the performance results.

scholarly journals DBTagger

2021 ◽  
Vol 14 (5) ◽  
pp. 813-821
Author(s):  
Arif Usta ◽  
Akifhan Karakayali ◽  
Özgür Ulusoy
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Relational Database ◽  
Relational Databases ◽  
Query Language ◽  
State Of The Art ◽  
Independent Solution ◽  
Rule Based ◽  
Mapping Problem ◽  
Stop Word

Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using adhoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called DBTagger (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, 92.4% on the average. Our results also indicate that DBTagger is faster than its counterparts up to 10000 times and scalable for bigger databases.

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.

Using the whole image and restrictions in the research of properties of some signature operations in Table Algebra

2019 ◽  
Vol 33 ◽  
pp. 112-121
Author(s):  
Nadiya Kakhuta ◽  
Alexey Senchenko
Keyword(s):  
Binary Relation ◽  
Relational Databases ◽  
Query Language ◽  
Relational Algebra ◽  
General Interest ◽  
Table Algebras ◽  
Modern Analogue ◽  
Table Data ◽  
Table Algebra ◽  
Composition Of Relations

The features of the whole image relative to many binary relation, and restrictions on a binary relation on the set for some of the signature operations of Table Algebra are used in the work. Constructions of the whole image and restrictions are of general interest for Mathematics, and Table Algebra is a modern analogue of Codd's well-known Relational Algebra. It forms the theoretical foundation of modern query language databases. Elements of the carrier of Table Algebra specify relational table data structures, and signature operations are based on the basic table manipulations in Relational Algebra and SQL-like languages. The following results in the research of the features of the whole image were obtained: interconnections between the whole image and restrictions were found; the monotony and distribution of the whole image and restrictions on unions, a criterion of their emptiness and interconnections with first and second projection relations were proved; the whole image of the composition of relations and composition restrictions were found; the distribution of restriction on intersection of sets was set; the estimates of the distribution of the whole image of intersection and difference of sets were given; criteria for distribution of the whole image relative to the intersection and differences of sets were found. In addition, the clues were provided with the help of the whole image and restrictions on some of the signature operations of Table Algebra: intersection, union, difference, projection and joining. These representations allowed us to obtain some features of these operations, which derive directly from the features of the whole image and restrictions. It is supposed to get similar views on other signature operations of Table Algebras and to allocate their features arising from such representation. The obtained results can be used in the theory of Table Algebra as an approach to the research of the features of their signature operations, this can be used in query optimization in relational databases.

Query Languages for Graph Databases

2019 ◽  
pp. 645-659
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years, many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that, the main idea of this chapter is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

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