# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 1 Introduction
The abundance of open mobility data on the SW offers enhanced analytical options. Many examples web-based linked open data (LOD) created opportunities for the advanced study of such data [5; 9]. These data sets contain observations and measures that are suitable for analysis (e.g., transport analysis, patient tracking, air/water quality measurements, immigration levels, etc.). However, mobility analysis on the SW still did not support the data representation and analytical query [9]. Mobility analysis on the SW still lacks a DW model with facts, measures, and dimensions for spatiotemporal/moving objects. This technology also did not support basic analytical queries for spatiotemporal/mobility operations. These operations include mobility roll-up, slice, dice, drill-down, and query functions that support mobility data types such as spatiotemporal aggregate, slice, and dice operations. [PERSON] et al. proposed a new query language, SPARQL-ST, an extension of SPARQL for complex spatiotemporal queries [10]. Further, a query language with the addition of geographic features on SPARQL was developed, namely GeoSPARQL. GeoSPARQL is an SW vocabulary that provides geospatial data representation. GeoSPARQL has two essential parts: an ontology for storing geospatial objects and query functions for processing relationships between the geospatial entities [11]. GeoSPARQL has some impressive features for geospatial implementation on the SW. This language did not accommodate analytical queries and had no extension for spatiotemporal and mobility data representation and queries. On the other hand, [PERSON] et al. proposed a mobility DW with the concepts beyond the existing mobility analysis in the object-relational database [12], but it could not be implemented on the SW.  
Mobility analysis on the SW still lacks a DW model with spatiotemporal/moving objects facts, measures, and dimensions. This technology also did not support basic analytical queries for spatiotemporal/mobility operations. These operations include mobility roll-up, slice, dice, drill-down, and query functions supporting mobility data types such as spatiotemporal aggregate and slice and dice operations [9]. This research constructs the representation of Mobility DW using the new vocabulary of QB4 MobOLAP that allows the concept of Mobility DW to be defined over RDF data. Hence, the Mobility DW could be created and published on the SW. The study also defines the mobility analytics operators so that mobility analytics could be queried using SPARQL. This paper introduces three contributions. First, QB4 MobOLAP, a new vocabulary to represent Mobility DW on the SW. QB4 MobOLAP extends the most recent OLAP vocabulary version with mobility concepts, including mobility facts and measures. The trajectory, level, dimensions, hierarchy, and mobility aggregation functions are also defined. Second, it introduces an extension of SPARQL query for analytical Mobility OLAP operators and functions. Third, this paper also explains the validation of the vocabulary model, operators, functions, and algorithm for the extension of SPARQL by implementing them in a practical use case. The remainder of the paper is structured as follows: Section 2 describes related works, Section 3 consists of the material and methods involved, and Section 4 includes the results and discussion, while Section 5 concludes the paper and describes the challenges related to future work.