Travelled to:1 × China
1 × Germany
1 × Italy
4 × USA
Collaborated with:D.Zhang Q.Lin Q.Fu R.Ding H.Zhang T.Xie H.Z.0002 S.Yang J.Li B.Wu H.Zhou H.Lin H.Lin T.Qin F.Lv S.Wang J.Zhao S.He M.R.Lyu C.Sun H.Zhang Q.Wang S.Khoo C.Luo Z.Wang J.Shen Y.Dang Y.Xu R.Yao M.Chintalapati K.Hsieh K.Sui C.Li Y.Wu X.Zhang B.Qiao C.Xie X.Yang Q.Cheng Z.Li J.C.0003 X.He F.Shen
Talks about:system (4) servic (4) log (4) data (3) understand (2) softwar (2) program (2) analysi (2) onlin (2) incid (2)
Person: Jian-Guang Lou
DBLP: Lou:Jian=Guang
Contributed to:
2015
2014
2014
2013
2013201220102015
20182019Wrote 11 papers:
- ICSE-v2-2015-ZhouLZLLQ #big data #empirical #framework #platform #quality
- An Empirical Study on Quality Issues of Production Big Data Platform (HZ, JGL, HZ, HL, HL, TQ), pp. 17–26.
- FSE-2014-SunZLZWZK #query #re-engineering
- Querying sequential software engineering data (CS, HZ, JGL, HZ, QW, DZ, SCK), pp. 700–710.
- KDD-2014-LuoLLFDZW #correlation
- Correlating events with time series for incident diagnosis (CL, JGL, QL, QF, RD, DZ, ZW), pp. 1583–1592.
- ASE-2013-LouLDFZX #case study #experience #online
- Software analytics for incident management of online services: An experience report (JGL, QL, RD, QF, DZ, TX), pp. 475–485.
- MSR-2013-FuLLDZX #analysis #behaviour #comprehension
- Contextual analysis of program logs for understanding system behaviors (QF, JGL, QL, RD, DZ, TX), pp. 397–400.
- ASE-2012-DingFLLZSX #mining #online #repository
- Healing online service systems via mining historical issue repositories (RD, QF, JGL, QL, DZ, JS, TX), pp. 318–321.
- KDD-2010-LouFYLW #mining #workflow
- Mining program workflow from interleaved traces (JGL, QF, SY, JL, BW), pp. 613–622.
- ASE-2015-LvZLWZZ #api #code search #comprehension #effectiveness #named
- CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E) (FL, HZ, JGL, SW, DZ, JZ), pp. 260–270.
- ESEC-FSE-2018-HeLLZLZ #analysis #identification #problem
- Identifying impactful service system problems via log analysis (SH, QL, JGL, HZ0, MRL, DZ), pp. 60–70.
- ESEC-FSE-2018-LinHDZSXLLWYCZ #predict
- Predicting Node failure in cloud service systems (QL, KH, YD, HZ0, KS, YX, JGL, CL, YW, RY, MC, DZ), pp. 480–490.
- ESEC-FSE-2019-ZhangXLQZDXYCLC #detection #robust
- Robust log-based anomaly detection on unstable log data (XZ, YX, QL, BQ, HZ0, YD, CX, XY, QC, ZL, JC0, XH, RY, JGL, MC, FS, DZ), pp. 807–817.