## 1 Introduction  
The evolution of the Linked Open Data (LOD) cloud has made a strong wave of research approaches in Big Data [1]. The LOD cloud consists of 1269 machine-readable datasets with 16,201 links (as of May 2020). LOD refers to a set of best practices for publishing and interlinking structured data for access by both humans and machines. The core concept of LOD is that the general Web architecture can be used to share structured data on a global scale. Technically, the LOD cloud employs the resource description framework (RDF) for data interchange and SPARQL for querying. The uniform resource identification (URI) is used to link related data across multiple resources. Vocabularies and ontologies define concepts and relationships to organize knowledge bases. In the RDF dataset, data items are expressed in the form of subject, predicate, and object triples. Because RDF triples are modeled as graphs, we cannot directly adopt existing solutions from relational databases and XML technologies [2]. Therefore, new storage and searching techniques are required to improve LOD query performance. To achieve the best storage and searching strategy for the LOD cloud, it is necessary to consider existing approaches. Currently, four alternative approaches are available. First, we can store independent data copies in a local repository, benefiting from convenient conditions for efficient query processing; we call this the local approach. Local approaches gather data from all known resources in advance, preprocess the combined data, and store the results in a local repository [3]. Using such a repository, this approach can provideexcellent query response time owing to a lack of network traffic. However, this approach has many shortcomings. First, retrieved results may not reflect the most recent data. Second, copying all data can be expensive, and the performance penalty may become excessively high as the dataset volume increases. There is a large amount of unnecessary data collection, processing, and storage. Third, clients can only use a fragment of the Web of data copied into the repository. The second approach is based on accessing distributed data on the fly using a recursive URI lookup process; we call this the live exploration approach. This approach performs queries over multiple SPARQL endpoints offed by publishers for their LOD datasets [4]. This approach has several advantages, such as synchronizing copied data is not required, searching is more dynamic with up-to-date data, and new resources can be easily added without a time lag for indexing and integrating data. Moreover, this approach requires less storage. However, this approach may not guarantee that all publishers offer reliable SPARQL endpoints for their LOD datasets [4]. The third technique is the index approach. LOD index structures are quite similar to traditional database query processing techniques. Existing data summaries and approximation techniques can be adapted to develop an index structure for LOD queries. For instance, [PERSON] et al. [5] considered multidimensional histograms (MDHs) as a summarizing index for LOD query processing. [PERSON] et al. [3] proposed an approximate multidimensional index structure (i.e., QTree), which combines the MDH and R-trees [6]. In contrast to MDHs, where regions have a fixed size, QTree is a tree-based data structure where variable-size regions more accurately cover the content of resources [5]. Although MDHs provide an easy method for constructing and maintaining the index, it is considered a rather rough approximation. QTree presents a more accurate approximation than MDHs; however, QTree requires a high cost for building and maintaining the trees. Finally, the fourth technique is the hybrid approach. This approach combines two storage and searching approaches. Consequently, the hybrid approach can conceivably obtain the advantages of both approaches without their respective drawbacks.