Facilitating Real Time Data Consumption by Using a Graph Path Cache
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P108Keywords:
Real-Time Data, Graph Path Cache, Distributed Systems, Data Streaming, In-Memory Caching, Graph Databases, Query OptimizationAbstract
In today's data-driven society, it is still very hard to let individuals easily access vast volumes of graph information that is continually changing in real time. Conventional graph query systems often have difficulties in delivering low-latency responses due to the computational demands of traversing large, interconnected databases. To fix this problem, we provide a Graph Path Cache (GPC), which is a smart technique to cache data that keeps and reuses graph paths that are often requested. The purpose of GPC is to develop a solution to integrate fast data analytics with the speed that current apps demand, such as social network analysis, fraud detection, and recommendation systems. The suggested design has dynamic route caching, adaptive eviction mechanisms & graph-aware indexing to reduce the number of times computations need to be done & maintain the data more consistent. GPC is different from regular caching technologies that function at the node or edge levels since it stores full traversal pathways. This makes it easy to search things up & saves money on redoing queries. Experimental assessments show that the GPC framework makes big increases in both latency & throughput. For example, query execution rates may be up to 60% quicker & the backend load goes down a lot when using these realistic workloads. These findings indicate that GPC not only helps things run more smoothly, but it also works well with huge graphs & more queries. This method changes how data access is managed in real-time graph systems by turning route reuse into a basic optimization approach. This is a new way to get real-time interactivity in large-scale graph analytics.
References
[1] Park, J-S., Michael Penner, and Viktor K. Prasanna. "Optimizing graph algorithms for improved cache performance." IEEE Transactions on parallel and distributed systems 15.9 (2004): 769-782.
[2] Yao, Yuan, et al. "Real-time cache-aided route planning based on mobile edge computing." IEEE Wireless Communications 27.5 (2020): 155-161.
[3] Yang, Ke, et al. "Random walks on huge graphs at cache efficiency." Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles. 2021.
[4] Müller, Thomas, et al. "Real-time neural radiance caching for path tracing." arXiv preprint arXiv:2106.12372 (2021).
[5] Cai, Zhuhua, Dionysios Logothetis, and Georgos Siganos. "Facilitating real-time graph mining." Proceedings of the fourth international workshop on Cloud data management. 2012.
[6] Papailiou, Nikolaos, et al. "Graph-aware, workload-adaptive SPARQL query caching." Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015.
[7] Lakhotia, Kartik, et al. "Gpop: A scalable cache-and memory-efficient framework for graph processing over parts." ACM Transactions on Parallel Computing (TOPC) 7.1 (2020): 1-24.
[8] Sheikh, Saad Zia, and Muhammad Adeel Pasha. "Energy-efficient real-time scheduling on multicores: A novel approach to model cache contention." ACM Transactions on Embedded Computing Systems (TECS) 19.4 (2020): 1-25.
[9] Ahn, Junwhan, et al. "A scalable processing-in-memory accelerator for parallel graph processing." Proceedings of the 42nd annual international symposium on computer architecture. 2015.
[10] Tang, Yimin, et al. "Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism." arXiv preprint arXiv:2501.02803 (2025).
[11] Zeno, Lior, Ang Chen, and Mark Silberstein. "In-Network Address Caching for Virtual Networks." Proceedings of the ACM SIGCOMM 2024 Conference. 2024.
[12] Theiling, Henrik, Christian Ferdinand, and Reinhard Wilhelm. "Fast and precise WCET prediction by separated cache and path analyses." Real-Time Systems 18.2 (2000): 157-179.
[13] Mueller, Frank. "Timing analysis for instruction caches." Real-time systems 18.2 (2000): 217-247.
[14] Schoeberl, Martin, et al. "Towards a time-predictable dual-issue microprocessor: The Patmos approach." Bringing Theory to Practice: Predictability and Performance in Embedded Systems (2011). Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2011.
[15] White, Randall T., et al. "Timing analysis for data caches and set-associative caches." Proceedings Third IEEE Real-Time Technology and Applications Symposium. IEEE, 1997.
