Subgraph matching is a fundamental task in graph analysis systems. In real-world applications, graph query engines often need to process a large number of subgraph matching queries, many of which share common substructures. Materializing the results of these shared subqueries as view patterns can enable computation reuse and significantly improve query efficiency. However, existing view materialization techniques suffer from either high memory usage or limited acceleration benefits.This paper introduces MAVIS, a novel view-based subgraph matching algorithm. MAVIS partitions view patterns into connected subgraphs called super-nodes and performs super-node-oriented materialization to balance memory consumption and processing speed. To further improve efficiency, it proposes a tree-based super-node partitioning method that avoids generating invalid candidates during materialization. Additionally, a customized query answering algorithm is designed to leverage the materialized views for faster query execution.Extensive experiments on real-world datasets demonstrate that MAVIS achieves a superior trade-off between memory usage and acceleration, and it outperforms existing approaches.

Multi-way join, which refers to the join operation among multiple tables, is widely used in database systems. With the development of the Internet and social networks, a new variant of the multi-way join query has emerged, requiring continuous monitoring of the query results as the database is updated. This variant is called continuous multi-way join. The join order of continuous multi-way join significantly impacts the operation's cost. However, existing methods for continuous multi-way join order selection are heuristic, which may fail to select the most efficient orders. On the other hand, the high-cost order computation will become a system bottleneck if we directly transfer join order selection algorithms for static multi-way join to the dynamic setting. In this paper, we propose a new A daptive J oin O rder S election algorithm for the C ontinuous multi-way join queries named AJOSC. It uses dynamic programming to find the optimal join order with a new cost model specifically designed for continuous multi-way join. We further propose a lower-bound-based incremental re-optimization algorithm to restrict the search space and recompute the join order with low cost when data distribution changes. Experimental results show that AJOSC is up to two orders of magnitude faster than the state-of-the-art methods.

Regular path query (RPQ) is a basic operation for graph data analysis, and persistent RPQ in streaming graphs is a new-emerging research topic. In this paper, we propose a novel algorithm for persistent RPQ in streaming graphs, named LM-SRPQ. It solves persistent RPQ with a combination of intermediate result materialization and real-time graph traversal. Compared to prior art, it merges redundant storage and computation, achieving higher memory and time efficiency. We carry out extensive experiments with both real-world and synthetic streaming graphs to evaluate its performance. Experiment results confirm its superiority compared to prior art in both memory and time efficiency.

Data stream processing has become a hot issue in recent years due to the arrival of big data era. There are three fundamental stream processing tasks: membership query, frequency query and Top-K query. While most existing solutions address these queries in fixed windows, this paper focuses on a more challenging task: answering these queries in sliding windows. While most existing solutions address different kinds of queries by using different algorithms, this paper focuses on a generic framework. In this paper, we propose a generic framework, namely Sliding sketches, which can be applied to many existing solutions for the above three queries, and enable them to support queries in sliding windows. We apply our framework to five state-of-the-art sketches for the above three kinds of queries. Theoretical analysis and extensive experimental results show that after using our framework, the accuracy of existing sketches that do not support sliding windows becomes much higher than the corresponding best prior art. We released all the source code at Github.

A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and more. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novelGraphStreamSketch (GSS for short) to summarize the graph streams, which has linear space cost$O(|E|)$(E is the edge set of the graph) and high update speed, and supports most kinds of queries over graph streams with controllable errors. Experimental results show that our solution is up to 142 times faster than the adjacency list when processing updates in graph streams, and its memory consumption is as small as$30\%$of the adjacency list. Though error is introduced as a trade off in our solution, both theoretical analysis and experiment results confirm that such error is small and controllable. The relative error is below$10^{-2}$in edge weight query, and the precision is above$90\%$is 1-hop precursor/successor queries.

Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. However, approximately counting triangles in real-world streaming graphs with edge duplication and expiration remains an unsolved problem. In this paper, we propose SWTC algorithm to address approximate sliding-window triangle counting problem in streaming graphs with edge duplication. In SWTC, we propose a fixed-length slicing strategy that addresses both unbiased sampling and cardinality estimation issues with a bounded memory usage. We theoretically prove the superiority of our method in sample graph size and estimation accuracy under given memory upper bound. Extensive experiments also confirm that our approach has higher accuracy compared with the baseline method under the same memory usage.

Data stream processing has become a hot issue in recent years due to the arrival of big data era. There are three fundamental stream processing tasks: membership query, frequency query and heavy hitter query. While most existing solutions address these queries in fixed windows, this paper focuses on a more challenging task: answering these queries in sliding windows. While most existing solutions address different kinds of queries by using different algorithms, this paper focuses on a generic framework. In this paper, we propose a generic framework, namely Sliding sketches, which can be applied to many existing solutions for the above three queries, and enable them to support queries in sliding windows. We apply our framework to five state-of-the-art sketches for the above three kinds of queries. Theoretical analysis and extensive experimental results show that after using our framework, the accuracy of existing sketches that do not support sliding windows becomes much higher than the corresponding best prior art. We released all the source code at Github.

A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and more. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization, like CM sketches, gSketches, TCM and gMatrix, either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novel Graph Stream Sketch (GSS for short) to summarize the graph streams, which has linear space cost O(|E|) (E is the edge set of the graph) and constant update time cost (O(1)) and supports most kinds of queries over graph streams with the controllable errors. Both theoretical analysis and experiment results confirm the superiority of our solution with regard to the time/space complexity and query results' precision compared with the state-of-the-art.