Extracting OLAP Cubes From Document-Oriented NoSQL Database Based on Parallel Similarity Algorithms

Davardoost, Farnaz; Sangar, Amin Babazadeh; Majidzadeh, Kambiz

doi:10.1109/cjece.2019.2953049

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…For the NOSQL database, we used Hadoop 2.7.3 and the Mongo dB database. We compare our proposed solution to previous methods such as LSHMR [27] and Vernica to assess its performance. [28] e MapReduce programming model is used in both solutions.…”

Section: Resultsmentioning

confidence: 99%

“…is algorithm is based on the LSH approximate similarity algorithm, which uses chunking and partitioning for Big Data management, as well as the MapReduce parallel processing system. Davardoost et al [27] proposed a new method for extracting OLAP cubes from document-oriented NOSQL called LSHMR. e LSHMR programming model was based on the LSH and MapReduce programming models.…”

Section: Related Workmentioning

confidence: 99%

“…e collection is unstructured, and the documents that are already there have different fields [32]. e information is stored as pairs of keys and values in the structure of JSON or XML documents in the documentoriented NOSQL database [27,33]. Figure 2 shows an instance of a document-oriented database.…”

Section: Formal Definition Of the Document-oriented Nosqlmentioning

confidence: 99%

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An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

Davardoost

Sangar

Majidzadeh

2022

Mathematical Problems in Engineering

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Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classifier method was used for this purpose. The proposed solution is divided into three stages of preparation, Naïve Bayes, and NBMR. Our proposed algorithm, NBMR, is based on the Naïve Bayes classifier (NBC) and the MapReduce (MR) programming model. Each NOSQL database document with nearly the same attribute will belong to the same class, and as a result, OLAP cubes can be used to perform data analysis. Because the proposed model allows for distributed and parallel Naïve Bayes Classifier computing, it is appropriate and suitable for large-scale data sets. Our proposed model is a proper and efficient approach when considering the speed and reduced the number of required comparisons.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

Davardoost

Sangar

Majidzadeh

2022

Mathematical Problems in Engineering

Self Cite

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“…For the clearly interpret the constructed neural network, the information processing should be presented explicitly as connections between combinations of values of N signs X j and classes  m . Such a view can be attracted using a discrete model of a ИНФОРМАЦИОННО-УПРАВЛЯЮЩИЕ СИСТЕМЫ multidimensional OLAP (online analytical processing) cube with binary measures (cell values) [13].…”

Section: Neural Network As a Discretization Model Of The Signs Spacementioning

confidence: 99%

Interpretation of a trained neural network based on genetic algorithms

Pimenov

2020

ICS

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Introduction: Artificial intelligence development strategy involves the use of deep machine learning algorithms in order to solve various problems. Neural network models trained on specific data sets are difficult to interpret, which is due to the “black box” approach when knowledge is formed as a set of interneuronal connection weights. Purpose: Development of a discrete knowledge model which explicitly represents information processing patterns encoded by connections between neurons. Methods: Adaptive quantization of a feature space using a genetic algorithm, and construction of a discrete model for a multidimensional OLAP cube with binary measures. Results: A genetic algorithm extracts a discrete knowledge carrier from a trained neural network. An individual's chromosome encodes a combination of values of all quantization levels for the measurable object properties. The head gene group defines the feature space structure, while the other genes are responsible for setting up the quantization of a multidimensional space, where each gene is responsible for one quantization threshold for a given variable. A discrete model of a multidimensional OLAP cube with binary measures explicitly represents the relationships between combinations of object feature values and classes. Practical relevance: For neural network prediction models based on a training sample, genetic algorithms make it possible to find the effective value of the feature space volume for the combinations of input feature values not represented in the training sample whose volume is usually limited. The proposed discrete model builds unique images of each class based on rectangular maps which use a mesh structure of gradations. The maps reflect the most significant integral indicators of classes that determine the location and size of a class in a multidimensional space. Based on a convolution of the constructed class images, a complete system of production decision rules is recorded for the preset feature gradations.

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“…OLAP is oriented explicitly towards analysis to support the decision-making process [6]. OLAP databases are also capable of resolving large query loads and a variety of data that cannot be resolved by relational databases [7]. OLAP systems are based on a multidimensional model commonly called a cube.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: a Case Study

Andreswari¹,

Syahputra²,

Lubis³

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

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Extracting OLAP Cubes From Document-Oriented NoSQL Database Based on Parallel Similarity Algorithms

Cited by 10 publications

References 37 publications

An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

Interpretation of a trained neural network based on genetic algorithms

Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: a Case Study

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