A Framework to Convert NoSQL to Relational Model
13 Jun 2018-pp 1-6
TL;DR: A generic framework is proposed in this paper so that different NoSQL databases could be converted to RDBMS, and a case study on MongoDB and Neo4j proves robustness of the proposed mechanism.
Abstract: Due to the exponential growth of NoSQL databases and in addition the circumstance of perusing humongous volumes of information, maximum applications switch RDBMS to NoSQL and pick it as information stockpiling framework. But we all know that RDBMS have several advantages which make it a popular platform across several applications over the decades. Therefore we view the standard problem of converting the RDBMS to NoSQL in reverse approach and we conceptualize a problem where NoSQL is converted back to a RDBMS based system. A generic framework is proposed in this paper so that different NoSQL databases could be converted to RDBMS. This approach is illustrated here using a case study on MongoDB and Neo4j. MongoDB is a document oriented database, fully unstructured and schemaless whereas Neo4j is a graph oriented database, fully unstructured and schemaless. This proves robustness of our proposed mechanism.
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17 Sep 2019
TL;DR: This paper proposes an automatic approach for extracting a physical model starting from a document-oriented NoSQL database, including links between different collections, using the Eclipse Modeling Framework environment.
Abstract: Big Data have received a great deal of attention in recent years. Not only the amount of data is on a completely different level than before but also, we have different type of data including factors such as format, structure, and sources. This has definitely changed the tools we need to handle Big Data, giving rise to NoSQL systems. While NoSQL systems have proven their efficiency to handle Big Data, it’s still an unsolved problem how the extraction of a NoSQL database model could be done. This paper proposes an automatic approach for extracting a physical model starting from a document-oriented NoSQL database, including links between different collections. In order to demonstrate the practical applicability of our work, we have realized it in a tool using the Eclipse Modeling Framework environment.
6 citations
TL;DR: This paper proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically and observed excellent performance, compared to the existing state-of-the-art methods.
Abstract: Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database
6 citations
Posted Content•
TL;DR: In this article, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice, and comparing the advantages, shortcomings and possible use cases of available big data file formats for Hadoop.
Abstract: Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed.
4 citations
Cites background from "A Framework to Convert NoSQL to Rel..."
...It is based on BigTable 3....
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...Scalable and integrates well with backend databases like HBase [4], Cassandra [77], BigTable [75] and BerkleyDB [73] 4....
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...It runs on top of Hadoop and provides capabilities similar to that of BigTable....
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Posted Content•
TL;DR: A feature analysis of 80 NoSQL solutions is presented, elaborating on the criteria and points that a developer must consider while making a possible choice, to determine the suitability of a NoSQL solution for an application area.
Abstract: Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for development of an optimized solution to a specific real world problem, big data systems are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL is the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. Moreover, the requirements of different applications vary on the basis of budget and functionality. This paper presents a feature analysis of 80 NoSQL solutions, elaborating on the criteria and points that a developer must consider while making a possible choice. Bivariate analysis of dataset created for the identified NoSQL solutions was performed to establish relationship between 9 features. Furthermore, cluster analysis of the dataset was used to create categories of solutions to present a statistically supported classification scheme. Finally, applications for different solutions were reviewed and classified under domain-specific categories. Random forest classification was used to determine the most relevant features for applications and correspondingly a decision tree-based prediction model was proposed, implemented and deployed in the form of a web application to determine the suitability of a NoSQL solution for an application area.
3 citations
Cites background from "A Framework to Convert NoSQL to Rel..."
...Collaborative web search [230] 10 BigTable [75] Wide-Column Consistent and PartitionTolerant 1....
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...It runs on top of Hadoop and provides capabilities similar to that of BigTable....
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...Scalable and integrates well with backend databases like HBase [4], Cassandra [77], BigTable [75] and BerkleyDB [73] 4....
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...It is based on BigTable 3....
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...10 BigTable [75] Wide-Column Consistent and PartitionTolerant 1....
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14 Sep 2020
TL;DR: This paper aims to achieve a reverse engineering of NoSQL databases in order to provide an element of semantic knowledge close to human understanding and provides experiments of this approach using a case study taken from the medical field.
Abstract: In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property “schema less” which means absence of the data model when creating a database. This property offers an undeniable flexibility allowing the user to add new data without making any changes on the data model. However, the lack of an explicit data model makes it difficult to express queries on the database. Therefore, users (developers and decision-makers) still need the database data model to know how data are stored and related, and then to write their queries. In previous works, we have proposed a process to extract the physical model of a document-oriented NoSQL database. In this paper, we aim to extend this work to achieve a reverse engineering of NoSQL databases in order to provide an element of semantic knowledge close to human understanding. The reverse engineering process is ensured by a set of transformation algorithms. We provide experiments of our approach using a case study taken from the medical field.
2 citations
References
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11 Jun 2015
TL;DR: This paper focuses on one implementation of the NoSQL database technology, namely MongoDB, and makes a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL.
Abstract: In this paper we will try to present a comparative study of non-relational databases and relational databases. We mainly focus our presentation on one implementation of the NoSQL database technology, namely MongoDB, and make a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL. We will also present the advantages of using a non-relational database compared to a relational database, integrated in a forum in the field of personal and professional development. The NoSQL database used to develop the forum is MongoDB, and was chosen from a variety of non-relational databases, thanks to some aspects that we will highlight in this article. The database integration in the framework will also be presented.
112 citations
"A Framework to Convert NoSQL to Rel..." refers methods in this paper
...Document oriented database analysis are illustrated in [9][10] ....
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08 Nov 2014
TL;DR: A schema conversion model for transforming SQL database to NoSQL which can provide high performance of join query with nesting relevant tables, and a graph transforming algorithm for containing all required content ofjoin query in a table by offering correctly nested sequence is proposed.
Abstract: With the increasing maturity of NoSQL databases as well as the situation of reading more than writing on large volumes of data, many applications turn to NoSQL and pick it as data storage system. Migrating from SQL database to NoSQL and providing efficient query become growing imperative. However, join operation is not supported in NoSQL database and read separately for multiple times which brings poor performance is unavoidable. This paper proposes a schema conversion model for transforming SQL database to NoSQL which can provide high performance of join query with nesting relevant tables, and a graph transforming algorithm for containing all required content of join query in a table by offering correctly nested sequence. A rigorous proof about the transform algorithm has been conducted and the experiment has verified the correctness of the conversion model and the high performance of join operation.
75 citations
"A Framework to Convert NoSQL to Rel..." refers methods in this paper
...required for SQL database to NoSQL[4][15] database....
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...ACM ISBN 978-1-4503-6574-1/18/06 $15.00 1 DOI: 10.1145/3265007.3265011 required for SQL database to NoSQL[4][15] database....
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...After all the analysis we conclude that we need a SQL to NoSQL conversion engine so that easily we can transmit data from RDBMS to NoSQL[14][15]....
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01 Feb 2017
TL;DR: The need for NoSQL databases in the present situation is ascertained and advancement of document-oriented database — MongoDB in particular is emphasized by describing with a quantitative example that SQL databases are prone to deterioration when data is over loaded and MongoDB comes with inbuilt load balancer which makes it a better solution in applications with high data load.
Abstract: Our world has evolved to an optimal point of advancement. The extravagant growth has helped in the invention of technologies, industry standards, gadgets, and devices that produce enormous amount of data that all require an essential data management and manipulation system. The data acquired from the various input and output sources are indulged in providing a certain infrastructure are also susceptible to damages if not treated well which may result in loss of data. To overcome this loss, various strategies that run parallel to prevent such loss are being used, one such example is the NoSQL MongoDB. MongoDb is a cross-platform, document oriented database that provides, high performance and easy scalability ensuring effective data management with its prominent feature of auto sharding. Sharding splits the database across multiple servers, increasing the capacity and scalability as required. This feature handles distribution of data in different nodes to maximize disk space and dynamically load balance queries. Partitioning the databases appropriately is a major step that determines the efficiency of sharding. This involves choosing an index of the MongoDB, competently as a shared key for further horizontal scaling of the database. Our current research involves the study of this load balancer. This paper intends to ascertain the need for NoSQL databases in the present situation and emphasize advancement of document-oriented database — MongoDB in particular by describing with a quantitative example that SQL databases are prone to deterioration when data is over loaded and MongoDB comes with inbuilt load balancer which makes it a better solution in applications with high data load. We describe the technology of sharding — auto load balancing feature of MongoDB and hope to provide a comprehensive insight of the process.
43 citations
"A Framework to Convert NoSQL to Rel..." refers methods in this paper
...We also investigate the execution of MongoDB(a document oriented database) versus MySQL database in view of insertion and retrieval tasks utilizing a web/android application to investigate load balancing[11]....
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20 Jul 2017
TL;DR: A document based NoSQL database MongoDB which commonly uses JSON data is discussed to explore the merits of NoSQL databases.
Abstract: Introduction of structural, semi-structural and unstructured data has tested and challenged the scalability, flexibility and processing ability of the conventional relational database management systems (RDBMS). The new eras of frameworks demand horizontal scaling of the databases. Data with various formats including unstructured data has to be stored and processed in the databases. NoSQL methodologies are answers to these issues. The conventional approaches used in relational and object oriented DBMS cannot adopt the flexible scaling required for today's systems. Likewise, Big Databases have come into existence. But systems with advanced facilities have not emerged, which can support such databases. These enormous databases could not be stored in one physical system. It is recommended to have a distributed framework for this. With a specific goal to enhance the unstructured data processing, a NoSQL framework can be utilized. A document based NoSQL database MongoDB which commonly uses JSON data is discussed to explore the merits of NoSQL databases.
35 citations
"A Framework to Convert NoSQL to Rel..." refers methods in this paper
...For these reasons different types of NoSQL[1] [5][9] databases are used over the last few years....
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...Document oriented database analysis are illustrated in [9][10] ....
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01 Oct 2017
TL;DR: The comparison between MongoDB, Cassandra, Redis and Neo4j concluded that all of them follow horizontal scaling and are schema free, except Neo4J, which doesn't have complete ACID properties.
Abstract: The current research explores and differentiates between various forms in which NoSQL databases exist. It examines the need of NoSQL and how they have become an important alternative to relational databases. NoSQL databases can be categorized into four major classifications which are: key value stores, graph databases, wide column stores, and document stores. These categories are compared on the basis of functional features and non-functional features. The non-functional features include performance, scalability, flexibility, structure and complexity. The functional features include de-normalization, joins, atomicity, aggregation and keys. Then for further analysis, one database is selected from each of these categories that is, MongoDB (document stores), Cassandra (wide column stores), Redis (key value stores), and Neo4j (graph databases). Selected databases are compared on their data model, CAP theorem, distributive properties and other factors. By performing the comparison on non-functional features, it has been found that a document store can be used if high performance, flexibility and scalability are required and if we have represented the data in JSON format. Column store can be used for semi structured data which requires high performance and scalability. Redis is anin-memory store and performs exceptionally fast in the case of single shard operation. Graph databases can be used when it comes to highly interconnected data and continuously evolving data models. The comparison between MongoDB, Cassandra, Redis and Neo4j concluded that all of them follow horizontal scaling and are schema free. Except Neo4j, others don't have complete ACID properties. Write and delete operations are fast for databases MongoDB, Redis and Cassandra, whereas read operation is comparatively slow in Cassandra. In case of Neo4j, REST performance is similar to MongoDB, whereas embedded is comparatively slow. We also discuss how these databases work in a distributed environment.
34 citations
"A Framework to Convert NoSQL to Rel..." refers background or methods in this paper
...Moreover, NoSQL[1] databases normally use clusters of cheap commodity servers to deal with the huge information and large volumes, while RDBMS has a tendency to depend on costly servers and storage system....
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...For these reasons different types of NoSQL[1] [5][9] databases are used over the last few years....
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...It can be majorly divided into four categories: key value stores, graph databases, wide column stores, and document stores[5]....
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