A large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

The need to track the origin of shared data/datasets has become apparent, highlighting the necessity of monitoring factors such as trust related to the data/datasets with the widespread use of social media. The concept of Linked Data Space needs to be considered in conjunction with organizations and their provenance with respect to their origin assuming that the shared data is semantic and considering organizations' access to relevant semantic data. In this context, this study elaborates on the concept of Linked Data Space, introducing the terms Internal Data and External Data to the literature. An architecture for Linked Data Space and data management for organizations is defined in addition to these concepts. Furthermore, the study explains how organizations can access External Data in the Linked Data Space and how provenance metadata and ontologies will be created. These developed methods are illustrated in the News Aggregator Scenario, a main scenario for provenance, demonstrating how it can work in a use case.