Formation of hypercube representation of relational database

Zykin, S. V.

doi:10.1134/s0361768806060077

Cited by 5 publications

(2 citation statements)

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“…Корректность представления данных достигалась за счет преобразования реляционного представления данных в иерархическое представление. Позднее было предложено обобщение данного подхода [18], в котором корректность достигается за счет зависимостей соединения.…”

Section: связанные работыunclassified

Testing Dependencies and Inference Rules in Databases

Zykin

2022

Model. anal. inf. sist.

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The process of testing dependencies and inference rules can be used in two ways. First, testing allows verification hypotheses about unknown inference rules. The main goal, in this case, is to search for the relation - a counterexample that illustrates the feasibility of the initial dependencies and contradicts the consequence. The found counterexample refutes the hypothesis, the absence of a counterexample allows searching for a generalization of the rule and conditions for its feasibility (logically imply). Testing cannot be used as a proof of the feasibility of inference rules, since the process of generalization requires the search for universal inference conditions for each rule, which cannot be programmed, since even the form of these conditions is unknown. Secondly, when designing a particular database, it may be necessary to test the feasibility of a rule for which there is no theoretical justification. Such a situation can take place in the presence of anomalies in the superkey. The solution to this problem is based on using join dependency inference rules. For these dependencies, a complete system of rules (axioms) has not yet been found. This paper discusses: 1) a technique for testing inference rules using the example of join dependencies, 2) a scheme of a testing algorithm is proposed, 3) some hypotheses are considered for which there are no counterexamples and inference rules, 4) an example of using testing when searching for a correct decomposition of a superkey is proposed.

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Section: связанные работыunclassified

Testing Dependencies and Inference Rules in Databases

Zykin

2022

Model. anal. inf. sist.

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“…The authors in [37] presented a study on the design of a decision support system for an aviation engine control system; the results indicate that fact tables do not provide any information on how to group records when calculating the data aggregation, where they adopted the implementation of the hypercube as a separate database to support functional dependency, and multiple elements combined into one using a specially selected function union that provides the most efficient access to data in terms of speed. The researchers in [38] concluded that a joint query would also not produce the desired result, since joint queries must have concurrent schemas (groups of attributes after the SELECT operator); thus, it is necessary to specify all relationships with these attributes in all queries that are standardized. The OLAP hypercube solves this problem automatically without additional user efforts by using a joint query.…”

Section: Literature Reviewmentioning

confidence: 99%

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

Hazboun

Owda

2021

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Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

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