AI for Systems and IoTs
PI
Xifeng Yan,  University of California at Santa Barbara		  
Project Summary
Publications

Project Goal:  Artificial Intelligence for Systems and IoTs (AIOPs and AIOTs)

Project Summary: The amount of data generated by systems and internet of things is ever increasing, such as logs, network flows, and time series.  Mining these system data has the potential to make computing more intelligent, reliable, secure, and maintainable. In the past 20 years, we have invented pattern mining algorithms and successfully applied them to automatic workload forecasting,  failure prediction/analysis, software debugging, malware finding, system optimization, and intelligent customer support.  Today, we are developing domain-specific AI and deep learning techniques to continuously improve the performance and reliability of computer systems and gain business insights from IoT data.  Our work covers a full range of research topics involving machine intelligence (how to automate and optimize), human intelligence (how to best use), and knowledge base (how to manage learned intelligence). 

Publications

More to come ...

  1. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting,
    by S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, X. Yan
    NeurIPS'19 (The Thirty-third Annual Conference on Neural Information Processing Systems) [pdf]
  2. You May Not Need Order in Time Series Forecasting,
    by Y. Zhang, Q. Jiang, S. Li, X. Jin, X. Ma, X. Yan,
    TPP'19 (Temporal Point Process workshop at NeurIPS), 2019 [pdf]
  3. Behavior Query Discovery in System-Generated Temporal Graphs,
    by B. Zong, X. Xiao, Z. Li, Z. Wu, Z. Qian, X. Yan, A. Singh, and G. Jiang,
    VLDB'16 (Proc. of the 42th Int. Conf. on Very Large Databases), 2016. [pdf]
  4. Distributed Representations of Expertise,
    by F. Han, S. Tan, H. Sun, M. Srivatsa, D. Cai, X. Yan,
    SDM'16 (SIAM Int. Conf. on Data Mining), 2016. [pdf]
  5. Towards Scalable Critical Alert Mining,
    by B. Zong, Y. Wu, J. Song, A. Singh, H. Cam, J. Han and X. Yan,
    KDD'14 (Proc. of the 20th Int. Conf. on Knowledge Discovery and Data Mining), Aug 2014. [pdf]
  6. Cloud Service Placement via Subgraph Matching,
    by B. Zong, R. Raghavendra, M. Srivatsa, X. Yan, A. Singh, and K.-W. Lee,
    ICDE'14 (    Proc. 2014 Int. Conf. on Data Engineering), 2014 [pdf]
  7. Extracting Probable Command and Control Signatures for Detecting Botnets,
    by A. Zand, G. Vigna, X. Yan and C. Kruegel,
    SAC'14     (The Security Track of the 2014 ACM Symp. on Applied Computing), 2014. [pdf]
  8. Workload characterization and prediction in the cloud: A multiple time series approach
    by A. Khan, X. Yan, S. Tao, N. Anerousis
    NOMS'12 (Network Operations and Management Symposium), 2012 [pdf]
  9. Understanding Task-driven Information Flow in Collaborative Networks,
    by G. Miao, S. Tao, W. Cheng, J. Moulic, L. Moser and X. Yan,
    WWW'12 (Proc. 2012 Int. World Wide Web Conference), April 2012 [pdf]
  10. Generative Models for Ticket Resolution in Expert Networks
    G. Miao, L. Moser, X. Yan, S. Tao, Y. Chen, and N. Anerousis
    SIGKDD'10 (Proc. of 2010 Int. Conf. on Knowledge Discovery and Data Mining), Jul. 2010 [pdf]
  11. Synthesizing Near-Optimal Malware Specifications from Suspicious Behaviors,
    M. Fredrikson, M. Christodorescu, S. Jha, R. Sailer, and X. Yan,
    Oakland'10 (31st IEEE Symp. on Security & Privacy), May 2010 [pdf]
  12. Identifying Bug Signatures Using Discriminative Graph Mining,
    by H. Cheng, D. Lo, Y. Zhou, X. Wang and X. Yan,
    ISSTA'09 (Proc. 2009 Int. Symp. On Software Testing and Analysis), Jul. 2009 [pd
  13. Statistical Debugging: A Hypothesis Testing-based Approach,
    by  C. Liu, L. Fei, X. Yan, J. Han and S. Midkiff,
     IEEE-TSE'06 (IEEE Transaction on Software Engineering), 32(10):831-848, 2006. [pdf]
  14. Mining Behavior Graphs for `Backtrace' of Noncrashing Bugs, 
    by C. Liu, X. Yan, H. Yu, J. Han, and P. S. Yu,
    SDM'05a (Proc. of 2005 SIAM Int. Conf. on Data Mining), 2005. [pdf]
  15. SOBER: Statistical Model-based Bug Localization, 
    by C. Liu, X. Yan, L. Fei, J. Han, and S. Midkiff,
    FSE'05 (Proc. of 2005 13th ACM SIGSOFT Symp. on the Foundations of Software Engineering), 2005.   [pdf] [website]
  16. Using Data Mining for Discovering Patterns in Autonomic Storage Systems,  
    by Z. Li, S. Srinivasan, Z. Chen, Y. Zhou, P. Tzvetkov, X. Yan, and J. Han,
    ACM Workshop on Algorithms and Architectures for Self-Managing Systems, Proc. of 2003 Federated Computing Research Conference (FCRC'03), 2003. [pdf]

 

Dissertations (TBD)