Abstract
The availability of location-aware devices generates tremendous volumes of moving object trajectories. The processing of these large-scale trajectories requires innovative techniques that are capable of adapting to changes in cloud systems to satisfy a wide range of applications and non-programmer end users. We introduce a Resilient Moving Object Index that is capable of balancing both spatial and object localities to maximize the overall performance in numerous environments. It is equipped with compulsory, discrete, and impact factor prediction models. The compulsory and discrete models are used to predict a locality pivot based on three fundamental aspects: computation resources, nature of the trajectories, and query types. The impact factor model is used to predict the influence of contrasting queries. Moreover, we provide a framework to extract efficient training sets and features without adding overhead to the index construction. We conduct an extensive experimental study to evaluate our approach. The evaluation includes two testbeds and covers spatial, temporal, spatio-temporal, continuous, aggregation, and retrieval queries. In most cases, the experiments show a significant performance improvement compared to various indexing schemes on a compact trajectory dataset as well as a sparse dataset. Most important, they demonstrate how our proposed index adapts to change in various environments.
Highlights
Enormous volumes of moving object trajectories are generated rapidly due to the availability of low-cost geospatial chipsets that can take advantage of the advanced technologies used in many fields
Our goal was to develop a resilient index that can satisfy a wide range of application demands and queries on the cloud, while overcoming the challenges raised by the configuration of cloud resources, the adopted distributed system, the nature of the trajectories, and the diversity in the queries
A resilient index should adjust its structure to maximize the benefits of the available resources without the need for any fine-tuning by the end user
Summary
Enormous volumes of moving object trajectories are generated rapidly due to the availability of low-cost geospatial chipsets that can take advantage of the advanced technologies used in many fields. Most of our daily devices (e.g., smartphones, smartwatches, navigation systems, tablets, etc.) are able to accurately pinpoint our location. As a result, they open new horizons, and many wide-ranging commercial applications have become feasible. Ridesharing (e.g., Uber, Lyft, etc.) is a distinct example of the influence of the location-aware devices on transportation services These applications rely on the availability of smartphones and wireless networks to automate a procedure that used to require human interaction. New services such as electric bike and scooter rentals, carsharing, security, and monitoring are leveraging the use of GPS tracker devices. Tremendous historical moving object trajectories are produced on a scale that requires innovative storing and processing techniques
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