# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 2 Related Works  
### Business Intelligence on Semantic Web  
Business Intelligence (BI) is a platform that may be used to integrate, transform, and present analysis data as a means of decision-making support. BI employs proven technologies to obtain information and knowledge that is relevant to the business. These technologies are Extract Transform Loading (ETL), Data Warehouse (DW), Multidimensional Data (MD), and Online Analytical Processing (OLAP) [11]. Simultaneously, as a concept of sharing data on the Web, SW has attracted many scientists for more than a decade. SW has a great vision of representing data on the web in a computer-readable way and adding semantic annotation. Information on the Web could be effectively enriched, retrieved, and processed by machines or humans. SW technologies open a new integration data model, including transparency data and semantically linked data, using description logic for inference tasks [2]. The directions of BI and SW research have converged and are mutually beneficial. Combining these two technologies makes BI have more exploratory semantic sources available for interactive analytics queries. The usage of BI technology over the SW begun with external source access to support BI's decision making. These data varied from structured, semi-structured, and unstructured data formats. New terms arose, such as BI 2.0 or On-Demand BI [13], Fusion Cube [14], and Self-Service BI. [15]. BI proposed sophisticated tools to analyze vast amounts of data with outstanding performance. In contrast, the SW-linked data offered an exploratory opportunity for valuable information from big data across the internet and could be used to enrich business analytics with inferencing capability [16; 17; 18; 19; 20; 14]. OLAP development using SW is categorized into three types of approaches. The first approach is an integration-based approach that focuses on data source integration; the second is an analysis-based approach that focuses on analytical processing. The third is a combination of these two approaches [9]. An integration-based approach focuses on extracting, transforming, and loading (ETL) from RDF data sources to DW. Many researchers proposed the use of this approach in their studies. [PERSON] et al. defined a multidimensional concept of DW warehouses originating from single domain ontology representing multiple heterogeneous data sources from many business domains [21]. [PERSON] et al. developed a technique to practice ontology and the mapping of files that connect the data sources from external sources to the ontology. Then, the ETL process was performed according to the ontology and mapping files. OLAP ontology defines measures and dimensions using the Ontology Web Language (OWL) and Resource Description Framework (RDF) [22]. Likewise, [PERSON] et al. proposed a framework for designing a model of MD analysis with the semantic annotation stored in a Semantic DW (SDW). This approach stores the SDW as web resources, domain ontologies, and semantic annotations using XML [23]. Additionally, [PERSON] et al. proposed a method to handle data heterogeneity to build DW with the integration process's domain ontology, and medical domain ontology played an important role [24]. Likewise, [PERSON] et al. developed an ETL tool that could support semantic mappings from the data source with inter-attribute definitions to support the DW schema's extraction and transformation process. The tool was implemented to develop DW in the food and beverage logistics area, resulting in an effective method [25]. An analysis-based approach directly focuses on analytical processes for SW/RDF data, without using the ETL process. In this approach, multidimensional data requires vocabulary to model RDF data cubes, dimensions, and measures. The de facto standard of vocabulary for the RDF Data Cube is QB [26]. [PERSON] et al. proposed a querying model of statistical Linked Data using common OLAP operations on RDF data cubes.