Rong Gu — Page 2 | Search arXiv

arXiv2021

Hash Adaptive Bloom Filter

Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter maps each element with uniformly random hash functions, few flexibilities are provided even if the information of negative keys (elements are not in the set) are available. The problem gets worse when the misidentification of negative keys brings different costs. To address the above problems, we propose a new Hash Adaptive Bloom Filter (HABF) that supports the customization of hash functions for keys. The key idea of HABF is to customize the hash functions for positive keys (elements are in the set) to avoid negative keys with high cost, and pack customized hash functions into a lightweight data structure named HashExpressor. Then, given an element at query time, HABF follows a two-round pattern to check whether the element is in the set. Further, we theoretically analyze the performance of HABF and bound the expected false positive rate. We conduct extensive experiments on representative datasets, and the results show that HABF outperforms the standard Bloom filter and its cutting-edge variants on the whole in terms of accuracy, construction time, query time, and memory space consumption (Note that source codes are available in [1]).

cs.DB↗

arXiv2020

Distributed Subgraph Enumeration via Backtracking-based Framework

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. The state-of-the-art distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework (B-BENU) for distributed subgraph enumeration. B-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. B-BENU does not shuffle any partial matching result. Instead, it stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand. To support dynamic data graphs, we propose the concept of incremental pattern graphs and turn continuous subgraph enumeration into enumerating incremental pattern graphs at each time step. We develop the Streaming-BENU framework (S-BENU) to enumerate their matches efficiently. We implement B-BENU and S-BENU with the local database cache and the task splitting techniques. The extensive experiments show that B-BENU and S-BENU can scale to big data graphs and complex pattern graphs. They outperform the state-of-the-art methods by up to one and two orders of magnitude, respectively.

cs.DC↗