A top‐ k spatial keyword (T k Sk) query ranks objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top‐ k spatial keyword queries. However, most of the studies focus on Euclidean space or only investigate the snapshot queries where both the query and data object are static. A few algorithms study T k Sk queries in undirected road networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, T k Sk queries have not been thoroughly investigated in directed and dynamic spatial networks where each edge has a particular orientation and its weight changes according to the traffic conditions. Therefore, in this study, we address this problem by presenting a new method, called COSK, for processing continuous top‐k spatial keyword queries for moving queries in directed and dynamic road networks. We first propose an efficient framework to process snapshot T k SK queries. Furthermore, we propose a safe‐exit‐based approach to monitor the validity of the results for moving T k SK queries. Our experimental results demonstrate that COSK significantly outperforms existing techniques in terms of query processing time and communication cost.

Flash memory is used widely in the data storage market, particularly low-price MultiLevel Cell (MLC) flash memory, which has been adopted by large-scale storage systems despite its low performance. To overcome the poor performance of MLC flash memory, a system architecture has been designed to optimize chip-level parallelism. This design increases the size of the page unit and the block unit, thereby simultaneously executing operations on multiple chips. Unfortunately, its Flash Translation Layer (FTL) generates many unused sectors in each page, which leads to unnecessary write operations. Furthermore, it reuses an earlier log block scheme, although it generates many erase operations because of its low space utilization. To solve these problems, we propose a hybrid associative FTL (Hybrid-FTL) to enhance the performance of the chip-level parallel flash memory system. Hybrid-FTL reduces the number of write operations by utilizing all of the unused sectors. Furthermore, it reduces the overall number of erase operations by classifying data as hot, cold, or fragment data. Hybrid-FTL requires less mapping information in the DRAM and in the flash memory compared with previous FTL algorithms.