AAE: An active auto-estimator for improving graph storage

Abstract

Graph has currently become an increasingly popular model in many real applications. The efficiency of graph storage is crucial for these applications. Generally speaking, the tuning task of graph storage relies on the database administrators to find the best graph storage. However, DBAs make the tuning decisions by mainly relying on their experiences and intuition. Due to the limitations of the DBAs, the tuning may have an uncertain performance and worse efficiency. In this paper, we observed that an estimator of the graph workload has the potential ability to guarantee the performance of tuning operations. Unfortunately, because of complex characteristics of the graph evaluation task, there exists no mature estimator for graph workload. We formulate the evaluation task of graph workload as a classification task and carefully design the feature engineering process, including graph data features, graph workload features and graph storage features. For this evaluation task, we propose an active auto-estimator (AAE) for the graph workload evaluation by combining the active learning and deep learning. We test the time efficiency and evaluation accuracy of AAE with two open source graph data, LDBC and Freebase. The experimental results show that our estimator could efficiently complete the graph workload evaluation in milliseconds.