Design and optimization of VLC enabled data center network

Qin, Yudong; Guo, Deke; Lin, Xu; Cheng, Geyao

doi:10.26599/tst.2018.9010105

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…To be more specific, this temporal association rule states, "if an antecedent Q.u x ; u y / occurs, then the consequent q.u x ; u y / is likely to occur in time t ." Mining temporal graph association rules (TGARs) in a temporal graph could be very useful for many practical applications [8][9][10] , two of which are listed as follows.…”

Section: Introductionmentioning

confidence: 99%

Discovering Association Rules with Graph Patterns in Temporal Networks

Huang

Zhang

Guo

et al. 2023

Tsinghua Sci. Technol.

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Discovering regularities between entities in temporal graphs is vital for many real-world applications (e.g., social recommendation, emergency event detection, and cyberattack event detection). This paper proposes temporal graph association rules (TGARs) that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints. We introduce quality measures (e.g., support, confidence, and diversification) to characterize meaningful TGARs that are useful and diversified. In addition, the proposed support metric is an upper bound for alternative metrics, allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs. The diversification score strikes a balance between interestingness and diversity. Although the problem is NP-hard, we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph. We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible. Moreover, we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space. We experimentally verify the effectiveness, efficiency, and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

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Section: Introductionmentioning

confidence: 99%

Discovering Association Rules with Graph Patterns in Temporal Networks

Huang

Zhang

Guo

et al. 2023

Tsinghua Sci. Technol.

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“…Dynamic graphs emerge in different domains, such as financial transaction network, mobile communication network, data center network [20][21][22], uncertain network [2], etc. These graphs usually contain a very large number of vertices with different attributes, and have complex relationships among vertices.…”

Section: Introductionmentioning

confidence: 99%

Continuous multi-query optimization for subgraph matching over dynamic graphs

Wang

Zhang

Guo

et al. 2022

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There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.

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