# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 1 Introduction  
In this Web 2.0 era, the Semantic Web (SW) plays a significant role in data publishing with an open and interoperable nature. The SW is an extension of the World Wide Web created by the World Wide Web Consortium (W3C) to make Internet data machine-readable, and is also referred to as the Web of Data. [1]. The SW increased its position with the advanced features of complex queries such as interactive analytical queries [2]. The data availability also increased even for complex data types such as spatial data or temporal data. The representation of geospatial and temporal data on the SW is common and growing significantly [3]. Moving objects (MOs) are objects that change their locations continuously over time. The data produced by MOs have grown significantly with the availability of devices that track the position of moving objects, for example, by using smartphones and GPS [4]. MO data have triggered an interest in mobility data analysis or Mobility Online Analytical Processing (Mobility OLAP), which has been grown steadily year after year. Mobility OLAP has attracted many researchers to create mobility patterns. Mobility OLAP could be implemented, for example, in transportation, tourism, weather forecasting, and network sensor analysis [5]. Mobility OLAP utilizes data warehousing techniques that typically include a segment of trajectory or trajectory of MOs at a point in time. Generally, MO data are stored in lengthy sequences of spatiotemporal coordinates (x,y,t). These sequences are divided into smaller segments of a movement known as trajectories [5]. Moving object databases keep track of MOs' locations over time and are designed to facilitate querying, for example, \"Which cars are within fifteen minutes of Melaka Central Market?\" However, they do not encourage a complicated analysis, such as, \"List for each month, more than 50% of total trucks operating at speeds greater than 70 km per hour.\" In this respect, integration with data warehousing technologies is necessary, which gives rise to the concept of the mobility data warehouse (DW) [6]. DW was designed according to a multidimensional (MD) modeling approach. DW could contain MOs data that can be analyzed in conjunction with other data such as city networks, road networks, agricultural data, climate observations, environmental data, and geographical. The MOs data already exists on the SW as statistical data [6]. 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 model of integration data, transparency data, and semantically linked data using description logic for inference tasks [2]. The DW and SW research have been converged and are mutually beneficial. Combining these two technologies makes OLAP have more exploratory semantic sources available for interactive analytics queries and a bigger catalog worldwide. Many examples exist such as UK Environmental Data ([[http://environment.data.gov.uk](http://environment.data.gov.uk)]([http://environment.data.gov.uk](http://environment.data.gov.uk)), accessed on 5 April 2021), the Finnish LOD dataset ([[https://www.ldf.fi/datasets.html](https://www.ldf.fi/datasets.html)]([https://www.ldf.fi/datasets.html](https://www.ldf.fi/datasets.html)), accessed on 5 April 2021), Australian Linked Data ([[https://www.linked.data.gov.au](https://www.linked.data.gov.au)]([https://www.linked.data.gov.au](https://www.linked.data.gov.au)), accessed on 5 April 2021), and European Statistic ([[https://ec.europa.eu/eurostat/web/nuts/linked-open-data](https://ec.europa.eu/eurostat/web/nuts/linked-open-data)]([https://ec.europa.eu/eurostat/web/nuts/linked-open-data](https://ec.europa.eu/eurostat/web/nuts/linked-open-data)), accessed on 5 April 2021) [7]. A vast amount of MO data containing spatial or non-spatial data have been published on the SW, yielding a need for advanced analysis of such data [8].