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Space Efficient Top-k Query Encoding Based on Data Distribution
Wooyoung Park, Srinivasa Rao Satti
http://doi.org/10.5626/JOK.2020.47.3.235
We consider an encoding that supports a range top-k query on a two-dimensional array without accessing the original array. We propose a more space-efficient encoding method for top-k query with better average-case query time. Our experiments also show that our encoding is more space-efficient than the earlier ones. Also, based on the learning-based data structure, we propose the use of the learning-based data structure on succinct data structures.
A Predictive Query Processing Method Considering the Movement of both a User and Objects
http://doi.org/10.5626/JOK.2018.45.12.1302
Recently, with the increase in use of mobile devices such as smart phones and tablet PCs with GPS, it is possible to analyze a large volume of data aggregated from various sensors. Accordingly, a variety of location-based services (LBSs) have attracted attention. To effectively provide these services, techniques for efficient spatial query processing have been studied. In this paper, we propose a method to overcome the limitation of not returning the desired query result to the user, because existing studies did not consider movement of the user. Specifically, we propose an algorithm to efficiently process a predictive query in the road network that returns the best available K moving objects, in consideration of the time of the user`s moving and that of the user`s waiting. In this process, we apply the technique to gradually expand the range of user and object`s movement simultaneously. Also, an appropriate index structure is used to efficiently process queries even in the road network with a large number of vertices and moving objects. Experimental results reveal the difference in the query result compared to existing studies and also reveal significant results in terms of efficiency.
An Efficient Algorithm for Monitoring Continuous Top-k Queries
JaeHee Jang, HaRim Jung, YougHee Kim, Ung-Mo Kim
In this study, we propose an efficient method for monitoring continuous top-k queries. In contrast to the conventional top-k queries, the presented top-k query considers both spatial and non-spatial attributes. We proposed a novel main-memory based grid access method, called Bit-Vector Grid Index (BVGI). The proposed method quickly identifies whether the moving objects are included in some of the grid cell by encoding a non-spatial attribute value of the moving object to bit-vector. Experimental simulations demonstrate that the proposed method is several times faster than the previous method and uses considerably less memory.
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