In-Memory Caching for Enhancing Subgraph Accessibility

Abstract

Graphs have been utilized in various fields because of the development of social media and mobile devices. Various studies have also been conducted on caching techniques to reduce input and output costs when processing a large amount of graph data. In this paper, we propose a two-level caching scheme that considers the past usage pattern of subgraphs and graph connectivity, which are features of graph topology. The proposed caching is divided into a used cache and a prefetched cache to manage previously used subgraphs and subgraphs that will be used in the future. When the memory is full, a strategy that replaces a subgraph inside the memory with a new subgraph is needed. Subgraphs in the used cache are managed by a time-to-live (TTL) value, and subgraphs with a low TTL value are targeted for replacement. Subgraphs in the prefetched cache are managed by the queue structure. Thus, first-in subgraphs are targeted for replacement as a priority. When a cache hit occurs in the prefetched cache, the subgraphs are migrated and managed in the used cache. As a result of the performance evaluation, the proposed scheme takes into account subgraph usage patterns and graph connectivity, thus improving cache hit rates and data access speeds compared to conventional techniques. The proposed scheme can quickly process and analyze large graph queries in a computing environment with small memory. The proposed scheme can be used to speed up in-memory-based processing in applications where relationships between objects are complex, such as the Internet of Things and social networks.

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

“…Regarding frameworks, various proposals have emerged focused on different phases of knowledge discovery in graphs, for example, business understanding [16], data modeling [17], graph mining [18][19][20], graph visualization [21,22], and evaluation [23,24]. Some proposals are focused on how to model realistic graphs that match the patterns found in real-world graphs.…”

“…Searching, processing, and visualizing subgraphs can be a task that requires many computational resources. For this reason, authors such as Bok et al [19] have developed proposals on enhancing subgraph accessibility. They propose a two-level caching strategy that caches subgraphs that are likely to be accessed depending on the usage pattern of subgraphs.…”

“…In the last decade, various proposals have emerged that focus on different phases of knowledge discovery in graph databases. These proposals encompass areas such as business understanding [16], data modeling [17], graph mining [18][19][20], graph visualization [21,22], and evaluation [23,24].…”

Distributed Subgraph Query Processing Using Filtering Scores on Spark

“…A graph G = (V, E) comprises a set V of vertices and a set E of edges to represent multiple relationships between objects in various applications such as social media, biological networks, and the Internet of Things (IoT) [1][2][3][4]. A graph is used with the objective of analyzing interactions, detecting anomaly patterns, and providing recommendation services [5][6][7][8].…”