$\sf {SIMkNN}$ : A Scalable Method for in-Memory k NN Search over Moving Objects in Road Networks

Nowadays, many location-based applications require the ability of querying $k$ -nearest neighbors over a very large scale of moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a much larger memory space at the server side. In this paper, we present $\sf {SIMkNN}$ , a scalable and in-memory k NN query processing technique. $\sf {SIMkNN}$ is dual-index driven, where we adopt a R-tree to store the topology of the road network and a hierarchical grid model to manage the moving objects in non-uniform distribution. To answer a k NN query in real time, $\sf {SIMkNN}$ adopts the strategy that incrementally enlarges the search area for network distance based nearest neighbor evaluation. It is far from trivial to perform the space expansion within the hierarchical grid index. For a given cell, we first define its neighbors in different directions, then propose a cell communication technique which allows each cell in the hierarchical grid index to be aware of its neighbors at any time. Accordingly, an efficient space expansion algorithm to generate the estimation area is proposed. The experimental evaluation shows that $\sf {SIMkNN}$ outperforms the baseline algorithm in terms of time and memory efficiency.