## 1 Introduction  
On the web, there is a plethora of data sources. There are Smart cities, IoT and personal devices, human-curated datasets (e.g., Open Maps or Wikipedia), large-scale collaborative data-driven research, satellite data, and open government initiatives, which are all contributing to an enormous amount of data available for analysis. As a result of this diversity, related data can be found in a wide range of database engines.  
The term \"open data\" refers to data that can be accessed, used, and shared by anybody. Open data can be used to help society, the economy, and the environment by governments, businesses, and individuals [1]. There are only two restrictions to open data: that it must be attributed and that it must be shared with others in a non-commercial manner. In order for the data to be accessible, it must be offered in its entirety and at a fair cost, preferably via the internet. Access to and modification of the data is also important. In order to allow for reuse and redistribution, the data must be made available under license terms that allow this, as well. Anyone can use, reuse, and redistribute; there should be no discrimination against fields of endeavor or individuals or groups [2].  
The phrase \"Linked Data\" refers to a collection of best practices for publishing and connecting structured data on the Web. The data discovery technique is to access the data from integration tasks, such as schema matching, query reformulation, and mapping, from data lakes. It is about building the connections necessary for a person or machine to examine the data network [3]. When linked data are accessible, related data can be discovered. As with the hypertextual web, the data web is constructed using web documents. Unlike the web of data, where links are anchors for relationships in hypertext pages written in Hypertext Markup Language (HTML), data links are between arbitrary items represented by Resource Description Framework (RDF). Uniform Resource Identifier (URIs) can be used to identify any type of item or concept [4]. However, when it comes to HTML or RDF, the same requirements apply in order to help the web grow:  
* Utilize URIs to identify objects;
* Utilize HTTP URIs to enable users to look up those names;
* When someone searches for a URI, give relevant information by utilizing industry standards (RDF, SPARQL);
* Include hyperlinks to additional URIs, so that users can continue to learn new things.  
Nowadays, there is interest in integrating structured data with text, web pages, semi-structured data, as well as time series. Lots of data are available on the web in numerous kinds of formats. There is no DBMS (Database Management Systems) that performs well on all types of data. The \"one size fits all\" approach taken by FDBS (Federated Database Management System) is encountering challenges in delivering data management solutions for diverse data [5]. It is possible to have performance and efficiency issues if FDBS is used to manage several types of data. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available [6]. Multiple models and data stores are needed in today's database management trends. Data storage facilities work with a variety of data types which have their own local language.  
Earlier data integration was accomplished through the use of a framework known as query discovery, the primary objective of which is to identify a query (or transformation) that translates data from one format to another. The objective is to identify the appropriate operators for joining, nesting, grouping, linking, and twisting data [7]. There is a strong emphasis on data science and analysis. Data science is frequently performed on vast repositories, sometimes referred to as data lakes, that contain a large number of distinct datasets [8]. The datasets may be sparsely or completely schema-less [9].