A Formal OLAP Algebra for NoSQL based Data Warehouses

Banerjee, Shreya; Bhaskar, Sourabh; Sarkar, Anirban; Debnath, Narayan C.

doi:10.33166/aetic.2021.05.019

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The authors claim that their work is the first to provide formal proof in the area due to work, at the time, lacking formalism or applicability. Banerjee et al (2021) propose a formal OLAP algebra for NoSQL systems. They highlight that the lack of a common OLAP specification is also a problem within the NoSQL world, and therefore, data warehousing portability is limited.…”

Section: Discussion and Other Related Workmentioning

confidence: 99%

“…An earlier survey by Vassiliadis and Sellis (1999) also suggests a wide variety of models, that reaching consensus on a standard is essential, and that results on optimization through the use of a generic logical model would prove helpful.Since the survey in Ciferri et al's work, several other algebras have been proposed (e.g. Kuijpers & Vaisman, 2016;Banerjee et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Initial Optimization Techniques for the Cube Algebra Query Language

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Vella

2022

International Journal of Data Warehousing and Mining

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A common model used in addressing today's overwhelming amounts of data is the OLAP Cube. The OLAP community has proposed several cube algebras, although a standard has still not been nominated. This study focuses on a recent addition to the cube algebras: the user-centric Cube Algebra Query Language (CAQL). The study aims to explore the optimization potential of this algebra by applying logical rewriting inspired by classic relational algebra and parallelism. The lack of standard algebra is often cited as a problem in such discussions. Thus, the significance of this work is that of strengthening the position of this algebra within the OLAP algebras by addressing implementation details. The modern open-source PostgreSQL relational engine is used to encode the CAQL abstraction. A query workload based on a well-known dataset is adopted, and CAQL and SQL implementations are compared. Finally, the quality of the query created is evaluated through the observed performance characteristics of the query. Results show strong improvements over the baseline case of the unoptimized query.

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Section: Discussion and Other Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Initial Optimization Techniques for the Cube Algebra Query Language

Mercieca

Vella

2022

International Journal of Data Warehousing and Mining

View full text Add to dashboard Cite

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“…In addition, each dimension member represents an attribute: M1 1 represents the first attribute (department id) in dimension d 1 (departments). The analysis can be performed by four types of OLAP analytic operations against a multidimensional object [46,47], namely:…”

Section: Multi-dimensional Olap 4d-cube (Hypercube)mentioning

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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A Formal OLAP Algebra for NoSQL based Data Warehouses

Cited by 2 publications

References 12 publications

Initial Optimization Techniques for the Cube Algebra Query Language

Initial Optimization Techniques for the Cube Algebra Query Language

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

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