Database query generation from spoken sentences

Facebook Integrated Chatbot for Bulgarian Language Aiding Learning Content Delivery

TEM Journal ◽

10.18421/tem103-01 ◽

2021 ◽

pp. 1011-1015

Author(s):

George Pashev ◽

Silvia Gaftandzhieva

Keyword(s):

Content Delivery ◽

Database Query ◽

Learning Content ◽

Query Generation ◽

Pros And Cons ◽

Linguistic Approach ◽

Software Prototype

The paper presents a chatbot oriented linguistic approach and a software prototype which addresses the need of delivering learning content based on queries in Bulgarian language. Two distinct database query generation approaches are presented, discussed and implemented. Pros and cons for each of them are discussed.

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What's Happening to Profits at Cazenovia Creations?

AIS Educator Journal ◽

10.3194/1935-8156-8.1.24 ◽

2013 ◽

Vol 8 (1) ◽

pp. 24-29 ◽

Cited By ~ 1

Author(s):

Margaret Garnsey ◽

Andrea Hotaling

Keyword(s):

Financial Analysis ◽

Financial Statements ◽

Internal Controls ◽

Net Income ◽

Database Query ◽

Microsoft Access ◽

Level Data ◽

Query Tool ◽

Transaction Level

ABSTRACT In this case, students assume the role of an accounting professional asked by a client to investigate why net income is not as strong as expected. The students must first analyze a set of financial statements to identify areas of possible concern. After determining the areas to investigate, the students use a database query tool to see if they can determine causes by examining transaction level data. Finally, the students are asked to professionally communicate their findings and recommendations to their client. The case provides students with experience in using query-based approaches to answering business questions. It is appropriate for students with basic query and financial analysis skills and knowledge of internal controls. A Microsoft Access database with transaction details for the final seven months of the current year as well as financial statements for the current and prior year are provided.

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Visualization of Database Query Improved by Fuzzy Logic Using Virtual Reality

10.21528/cbic2011-05.1 ◽

2016 ◽

Author(s):

Vanessa Avelino Xavier de Camargo ◽

Marcos Wagner de Souza Ribeiro

Keyword(s):

Virtual Reality ◽

Fuzzy Logic ◽

Database Query

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Semantic Query Labeling Through Synthetic Query Generation

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval ◽

10.1145/3404835.3463071 ◽

2021 ◽

Author(s):

Elias Bassani ◽

Gabriella Pasi

Keyword(s):

Semantic Query ◽

Query Generation

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Technical Perspective

ACM SIGMOD Record ◽

10.1145/3471485.3471499 ◽

2021 ◽

Vol 50 (1) ◽

pp. 59-59

Author(s):

Marcin Zukowski

Keyword(s):

Query Processing ◽

Hash Tables ◽

Database Query ◽

Query Performance ◽

Research Opportunities ◽

Database Query Processing

Hash tables are possibly the single most researched element of the database query processing layers. There are many good reasons for that. They are critical for some key operations like joins and aggregation, and as such are one of the largest contributors to the overall query performance. Their efficiency is heavily impacted by variations of workloads, hardware and implementation, leading to many research opportunities. At the same time, they are sufficiently small and local in scope, allowing a starting researcher, or even a student, to understand them and contribute novel ideas. And benchmark them. . . Oh, the benchmarks. . . :)

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Resource-Efficient Database Query Processing on FPGAs

Proceedings of the 17th International Workshop on Data Management on New Hardware (DaMoN 2021) ◽

10.1145/3465998.3466006 ◽

2021 ◽

Author(s):

Mehdi Moghaddamfar ◽

Christian Färber ◽

Wolfgang Lehner ◽

Norman May ◽

Akash Kumar

Keyword(s):

Query Processing ◽

Database Query ◽

Database Query Processing

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SPARQL Query Generator (SQG)

Journal on Data Semantics ◽

10.1007/s13740-021-00133-y ◽

2021 ◽

Author(s):

Yanji Chen ◽

Mieczyslaw M. Kokar ◽

Jakub J. Moskal

Keyword(s):

Data Retrieval ◽

Data Representation ◽

Sparql Query ◽

Experimental Results ◽

Generation Process ◽

Manual Work ◽

Retrieval Systems ◽

Large Numbers ◽

Query Generation ◽

Good Coverage

AbstractThis paper describes a program—SPARQL Query Generator (SQG)—which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.

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