# Efficient Continuous Subgraph Matching Scheme Based on Trie Indexing for Graph Stream Processing
## 5 Conclusions  
In this paper, a sliding-window-based continuous subgraph matching scheme was proposed to manage graph streams efficiently in a limited memory environment. The proposed scheme can respond to various types of queries by providing wildcard operations on vertices and edges. It identifies a duplicated query area and establishes an index for efficient continuous query processing. It also applies one-time indexing in the duplicated query area to generate several duplicated query results. Furthermore, it can efficiently manage intermediate query results by adopting a two-level catching algorithm. Moreover, cache replacement strategies based on statistics and indexing characteristics were developed, and efficient cache replacement was performed based on the developed algorithms. An independent performance evaluation revealed that efficient query processing can be achieved by the implementation of an appropriate cache replacement strategy and an  
Figure 16: Memory cost according to datasets.  
Figure 15: Average window processing time according to the stream adjustment ratio. (**a**) Wiki-Talk; (**b**) Cit-Patents; (**c**) YouTube.  
appropriate size of cache space according to the datasets. The evaluation result showed that the performance of the proposed scheme increased by 190% at the maximum compared with that of the existing algorithm. The proposed scheme significantly increased the query processing speed regardless of the complexity of the query and the size of the window. The proposed scheme can be used for various applications that analyze graph types frequently searched in SNS graph data, register network attack types as query graphs to detect network traffic types in real-time, and so on. Further research will be conducted to propose continuous query processing methods according to the input of query streams and processing methods that support various graph attributes. Compression techniques are very necessary in graph processing. Since various studies related to graph compression have continuously been conducting, we are sure that we can get the high performance of the proposed scheme by applying them to it in the future. Follow-up research will also verify the excellent performance of the proposed scheme based on additional comparisons with the existing algorithm.  
Conceptualization, D.C., S.L., H.L., J.L., K.B. and J.Y.; methodology, D.C., S.L., H.L., J.L., K.B. and J.Y.; validation, D.C., S.L., S.K., H.L., J.L. and K.B.; formal analysis, D.C., S.L., S.K., H.L., J.L. and K.B.; writing--original draft preparation, D.C., S.L., S.K. and K.B.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (No. 2022R1A2B5B02002456), by Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ016247012023) Rural Development Administration, The Research Year of Chungbuk National University in 2019, and by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflict of interest.