With the spread of GPS-equipped portable devices, Location-Based Services (LBSs) flourished. Some crucial LBSs require real-time processing of moving spatial-keyword queries over moving objects, such as an ambulance seeking for volunteers. The research community proposed solutions for scenarios assuming that either the queries or the queried objects are moving, but solutions are needed assuming that both are moving. This work proposes SkyEye; a model that efficiently processes moving continuous top-k spatial-keyword queries over moving objects in a directed streets network. SkyEye computes queries' answer sets for time intervals and smartly updates the answer sets based on the recent history. Novel optimization techniques and indexing structures are leveraged to improve SkyEye's efficiency and scalability. The mathematical foundations of these optimization techniques are thoroughly demonstrated. Finally, extensive experiments showed that SkyEye has significant performance improvements in terms of efficiency, scalability, and accuracy compared to a baseline model. [ABSTRACT FROM AUTHOR]

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