As moving to big data world where data is increasing in unstructured way with high velocity, there is a need of data-store to store this bundle amount of data. Traditionally, relational databases are used which are now not compatible to handle this large amount of data, so it is needed to move on to non-relational data-stores. In the current study, we have proposed an extension of the Mongo...

As moving to big data world where data is increasing in unstructured way with high velocity, there is a need of data-store to store this bundle amount of data. Traditionally, relational databases are used which are now not compatible to handle this large amount of data, so it is needed to move on to non-relational data-stores. In the current study, we have proposed an extension of the Mongo...

Ontologies provide shared and reusable pieces of knowledge about a specific domain. Building an ontology by hand is a very hard and prone to errors task. Ontology learning from existing resources provides a good solution to this issue. Databases are widely used to store data. They were often considered as the most reliable sources for knowledge extraction. NOSQL databases are more and more used...

Social media is a biggest successful buzzword used in the recent time. Its success opened various opportunities for the developers. Developing any application requires storage of large data into databases. Many databases are available for the developers, Choosing the right one make development easier. MongoDB is a cross platform document oriented, schema-less database eschewed the traditional t...

This paper addresses how to manage planar spatial data using MongoDB, a popular NoSQL database characterized as a document-oriented, rich query language and high availability. The core idea is to flatten a hierarchical R-tree structure into a tabular MongoDB collection, during which R-tree nodes are represented as collection documents and R-tree pointers are expressed as document identifiers. B...

Fostering the development of SPARQL interfaces to heterogeneous databases is a key to efficiently expose legacy data as RDF on the Web. To deal with the variety of modern database formats and query languages, this paper describes a two-step approach to translate a SPARQL query into an equivalent target database query. First, given an xR2RML mapping describing how native database entities can be...

The database landscape has been significantly diversified during the last decade, resulting in the emergence of a variety of non-relational (also called NoSQL) databases, e.g., XML and JSON-document databases, key-value stores, and graph databases. To facilitate access to such databases and to enable data integration of non-relational data sources, we generalize the well-known ontologybased dat...

The database landscape has been significantly diversified during the last decade, resulting in the emergence of a variety of non-relational (also called NoSQL) databases, e.g., XML and JSON-document databases, key-value stores, and graph databases. To facilitate access to such databases and to enable data integration of non-relational data sources, we generalize the well-known ontologybased dat...

Numerous organizations perform data analytics using relational databases by executing data mining queries. These queries include complex joins and aggregate functions. However, due to an explosion of data in terms of volume, variety, veracity, and velocity known as Big Data [1], many organizations such as Foursquare, Adobe, and Bosch have migrated to NoSQL databases [2] such as MongoDB [3] and ...