New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Banane, Mouad; Belangour, Abdessamad

doi:10.14569/ijacsa.2019.0100474

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…The second layer manages the SPARQL query processing part, each SPARQL [8] request entered by the user is transformed into a MongoDB Query Language program. The SPARQL language is known by the complexity of its queries by that RDF to a graphical structure, for that, we have developed a system [9] for querying complex SPARQL queries using Apache Spark [10], in paper [11] the authors transform SPARQL queries to Hive [12] program.…”

Section: Related Workmentioning

confidence: 99%

Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

Banane

Belangour

2020

Int. J. Interact. Mob. Technol.

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In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.

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Section: Related Workmentioning

confidence: 99%

Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

Banane

Belangour

2020

Int. J. Interact. Mob. Technol.

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“…The goal of these technologies is to make the information contained in web documents usable by software programs and agents, through a formal metadata system. All of these technologies and the data they structure are called the Semantic Web [23].…”

Section: Related Workmentioning

confidence: 99%

“…In [16] the authors reviewed the storage of large RDF data in NoSQL systems based on the different NoSQL models. In the SPARQL2Hive [23] approach, which presents a technique for transforming SPARQL queries into an Apache Hive program, this solution can be used for RDF data stored in NoSQL databases. In this paper, we present a comparative study of existing systems dedicated to managing large volumes of RDF data, which are generally based on NoSQL database management systems.…”

Section: Related Workmentioning

confidence: 99%

An Evaluation and Comparative study of massive RDF Data management approaches based on Big Data Technologies

Banane¹

2019

IJETER

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Today, many researches are focused on the use of Big Data technologies for managing large volumes of Semantic Web data. The use of Big Data technologies such as NoSQL database management systems guarantees the scalability and high availability of Web data. The amount of data Web increases day after day and in a way excessive. To fully exploit these data and its metadata. This requires this technology of the Semantic Web to be a scalable and high performance at the level of the storage of these Web data. Many research efforts have been devoted to create and develop a distributed RDF data management system. To achieve the scalability and high performance, these systems are implemented on the basis of the Management technology of Big Data called NoSQL. In this paper, we present a comparative study of existing systems dedicated to managing large volumes of RDF data, which are generally based on NoSQL database management systems.

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“…But the source of the transformation process, here a relational schema, does not present the semantic richness that can be expressed in a DCL (notable thanks to the different types of links between classes: aggregation, composition, inheritance, etc.). Papers [8] and [9] propose a schema transformation using model-driven engineering towards Big Data technologies: Apache Hive [10], and Apache Spark [11]. The work of Y. Li et al presented in [7] are intended to specify an MDA transformation process from a conceptual schema (DCL) to an HBase physical schema.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Business Intelligence Decision-Making System in the Era of Big Data

Kalna¹,

Erraissi²,

Banane³

et al. 2019

IJITEE

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Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

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New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Cited by 9 publications

References 11 publications

Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

An Evaluation and Comparative study of massive RDF Data management approaches based on Big Data Technologies

A Scalable Business Intelligence Decision-Making System in the Era of Big Data

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