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TopIn this Section, we study previous work based on two major discovery techniques, Markov chain models and spatiotemporal data mining, for extracting movement patterns of an object from historical trajectories (Zhegown, 2020) (Wang et.al, 2020) (Wang, 2020). Markov chain models have been widely used in order to estimate the probability of an object’s movements from one region or state to another at next time period. Ishikawa et al. derive the Markov transition probabilities between cells from indexed trajectories (Kuliko, 2017) (Mehrotra and K. Chakrabarti, 2018) (Shende, M. P., 2012).
In their further study (Zheng, 2020) (Yuan et al., 2019), a special type of histogram, called mobility histogram, is used to describe mobility statistics based on the Markov chain model. They also represent the histogram as cube-like logical structures and support an online analytical processing (OLAP)-style analysis. Authors (Wang et al., 2018) classify an object’s mobility patterns into three states (stationary state, linear movement, and random movement) and apply Markov transition probabilities to explain a movement changes one state to another consider the location tracking problem in PCS networks (Wang et al., 2016) (Sozio et al., 2018) (Al-Sharif et al., 2017) (Venkatadri, M., & Pasricha, A., 2019). All studies are based on the same Markov process in order to describe users’ movements from one or multiple Personal Communications Services (PCS) cells to another cell. However, they have different ways to model users’ motilities using Morkov models, thus, show distinct results to each other (Lander et al., 2018) (Yang et al, 2017).
Data mining play important role to discover the knowledge and some time it is also called knowledge discovery, sometime this technique helps to extracting the data from large database (Han et al., 2016) (Kuliko, 2017). Data mining clustering approach generate answer and classifying movement analysis of data by using some clustering and prediction algorithm (Lander et al., 2018) (Wu, et al., 2017).
Data mining only a tool and framework which help to mine temporal and pictorials trajectory moving web-based data. Through the classified trajectory moving web-based data can be represent by cubes and relational transaction approach (Wang et al., 2018) (Mishra et al., 2017). The survey of instance of data mining methods based on classical relational and transactional data can be found in traditional clustering trajectory movement. Based on that trajectory mining cluster many researchers suggest the many technologies Global Navigation Satellite System (GNSS) and Radio frequency identification (RFID) (Wang et al., 2016).