survey

Public Access

Data-Driven Techniques in Computing System Management

Authors:

Yue HuangAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 3

Article No.: 45, Pages 1 - 43

https://doi.org/10.1145/3092697

Published: 27 July 2017 Publication History

Abstract

Modern forms of computing systems are becoming progressively more complex, with an increasing number of heterogeneous hardware and software components. As a result, it is quite challenging to manage these complex systems and meet the requirements in manageability, dependability, and performance that are demanded by enterprise customers. This survey presents a variety of data-driven techniques and applications with a focus on computing system management. In particular, the survey introduces intelligent methods for event generation that can transform diverse log data sources into structured events, reviews different types of event patterns and the corresponding event-mining techniques, and summarizes various event summarization methods and data-driven approaches for problem diagnosis in system management. We hope this survey will provide a good overview for data-driven techniques in computing system management.

References

[1]

2015a. FileZilla: An open-source and free FTP/SFTP solution. Retreived from http://filezilla-project.org.

[2]

2015b. ITIL. Retrieved from http://www.itil-officialsite.com. (2015).

[3]

Avinash Achar, A. Ibrahim, and P. S. Sastry. 2013. Pattern-growth based frequent serial episode discovery. Data Knowl. Eng. 87 (2013), 91--108.

Digital Library

[4]

Avinash Achar, Srivatsan Laxman, and P. S. Sastry. 2012. A unified view of the apriori-based algorithms for frequent episode discovery. Knowl. Inf. Syst. 31, 2 (2012), 223--250.

Digital Library

[5]

Charu C. Aggarwal and Jiawei Han. 2014. Frequent Pattern Mining. Springer.

[6]

Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In ACM SIGMOD Record, Vol. 22. ACM, 207--216.

Digital Library

[7]

Rakesh Agrawal and Ramakrishnan Srikant. 1995. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering, 1995. IEEE, 3--14.

[8]

Michal Aharon, Gilad Barash, Ira Cohen, and Eli Mordechai. 2009. One graph is worth a thousand logs: Uncovering hidden structures in massive system event logs. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD’09). 227--243.

Digital Library

[9]

Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan. 2012. Mutual information-based selection of optimal spatial--temporal patterns for single-trial EEG-based BCIs. Pattern Recogn. 45, 6 (2012), 2137--2144.

Digital Library

[10]

Algirdas Avizienis, Jean-Claude Laprie, and Brian Randell. 2001. Fundamental Concepts of Dependability. University of Newcastle upon Tyne, Computing Science.

[11]

Jay Ayres, Jason Flannick, Johannes Gehrke, and Tomi Yiu. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 429--435.

Digital Library

[12]

Moonish Badaloo. 2006. An examination of server consolidation: Trends that can drive efficiencies and help businesses gain a competitive edge. White Paper on IBM Global Services (2006).

[13]

Andrew Barron, Jorma Rissanen, and Bin Yu. 1998. The minimum description length principle in coding and modeling. IEEE Trans. Inf. Theory 44, 6 (1998), 2743--2760.

Digital Library

[14]

Robert M. Bell and Yehuda Koren. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proceedings of IEEE International Conference on Data Mining (ICDM’07). 43--52.

Digital Library

[15]

Albert Bifet and Ricard Gavaldà. 2011. Mining frequent closed trees in evolving data streams. Intell. Data Anal. 15, 1 (2011), 29--48.

Digital Library

[16]

Christopher M. Bishop and others. 2006. Pattern Recognition and Machine Learning. Vol. 1. Springer, New York.

[17]

Jasmina Bogojeska, Ioana Giurgiu, David Lanyi, George Stark, and Dorothea Wiesmann. 2014. Impact of HW and OS type and currency on server availability derived from problem ticket analysis. In Proceedings of the IEEE Network Operations and Management Symposium (NOMS’14), 1--9.

[18]

Jasmina Bogojeska, David Lanyi, Ioana Giurgiu, George Stark, and Dorothea Wiesmann. 2013. Classifying server behavior and predicting impact of modernization actions. In Proceedings of the International Conference on Network and Service Management (CNSM’13). 59--66.

[19]

Khellaf Bouandas and Aomar Osmani. 2007. Mining association rules in temporal sequences. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2007 (CIDM’07). IEEE, 610--615.

[20]

Jean S. Bozman and Katherine Broderick. 2010. Server refresh: Meeting the changing needs of enterprise IT with hardware/software optimization. IDC Whitepaper (2010).

[21]

Joel W. Branch, Yixin Diao, Emi K. Olsson, Larisa Shwartz, and Li Zhang. 2013. Predicting service delivery costs under business changes. (Aug. 30 2013). Patent No. 14/015,293.

[22]

Joel W. Branch, Yixin Diao, and Larisa Shwartz. 2014. A framework for predicting service delivery efforts using IT infrastructure-to-incident correlation. In Proceedings of the Network Operations and Management Symposium (NOMS’14). IEEE.

[23]

Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, and Jin Soung Yoo. 2006. Mixed-drove spatio-temporal co-occurence pattern mining: A summary of results. In Proceedings of IEEE International Conference on Data Mining (ICDM’06), Vol. 6. 119--128.

Digital Library

[24]

Joong Hyuk Chang. 2011. Mining weighted sequential patterns in a sequence database with a time-interval weight. Knowl.-Based Syst. 24, 1 (2011), 1--9.

Digital Library

[25]

Gianpaolo Cugola and Alessandro Margara. 2012. Processing flows of information: From data stream to complex event processing. ACM Comput. Surv. 44, 3 (2012), 15.

Digital Library

[26]

Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth. 1998. Rule discovery from time series. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD’98), Vol. 98. 16--22.

[27]

Otávio M. de Carvalho, Eduardo Roloff, and Philippe O. A. Navaux. 2013. A survey of the state-of-the-art in event processing. In 11th Workshop on Parallel and Distributed Processing (WSPPD’13).

[28]

Giuseppe A. Di Lucca, Massimiliano Di Penta, and Sara Gradara. 2002. An approach to classify software maintenance requests. In Proceedings of International Conference on Software Maintenance. IEEE, 93--102.

[29]

Yixin Diao, Hani Jamjoom, and David Loewenstern. 2009. Rule-based problem classification in it service management. In Proceedings of the IEEE International Conference on Cloud Computing, 2009 (CLOUD’09). IEEE, 221--228.

Digital Library

[30]

Yixin Diao, Linh Lam, Larisa Shwartz, and David Northcutt. 2014. Predicting service delivery cost for non-standard service level agreements. In Proceedings of the IEEE Network Operations and Management Symposium 2014 (NOMS’14). IEEE, 1--9.

[31]

Yi Ding and Xue Li. 2005. Time weight collaborative filtering. In Proceedings of the ACM International Conference on Information and Knowledge Mangament (CIKM’05). 485--492.

Digital Library

[32]

Ricardo Luis dos Santos, Juliano Araujo Wickboldt, Roben Castagna Lunardi, Bruno Lopes Dalmazo, Lisandro Zambenedetti Granville, Luciano Paschoal Gaspary, Claudio Bartolini, and Marianne Hickey. 2011. A solution for identifying the root cause of problems in IT change management. In Proceedings of the 2011 IEEE Symposium on Integrated Network Management (IM’11), 586--593.

[33]

Rick Durrett. 2010. Probability: Theory and Examples. Cambridge University Press.

[34]

Common Event Expression. 2015. Common Event Expression. (2015). Retrieved from http://cee.mitre.org.

[35]

Noam A. Fraenkel, Guy Goldstein, Ido Sarig, and Refael Haddad. 2004. Root cause analysis of server system performance degradations. (May 18 2004). Patent No. 6,738,933.

[36]

Lajos Jenő Fülöp, Gabriella Tóth, Róbert Rácz, János Pánczél, Tamás Gergely, Arpád Beszédes, and Lóránt Farkas. 2010. Survey on complex event processing and predictive analytics. In Proceedings of the 5th Balkan Conference in Informatics. 26--31.

[37]

Jing Gao, Guofei Jiang, Haifeng Chen, and Jiawei Han. 2009. Modeling probabilistic measurement correlations for problem determination in large-scale distributed systems. In Proceedings of International Conference on Distributed Computing Systems (ICDCS’09). 623--630.

Digital Library

[38]

Minos N. Garofalakis, Rajeev Rastogi, and Kyuseok Shim. 1999. SPIRIT: Sequential pattern mining with regular expression constraints. In Proceedings of the 25th International Conference on Very Large Data Bases (VLDB’99). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 223--234.

Digital Library

[39]

Ioana Giurgiu, Jasmina Bogojeska, Sergii Nikolaiev, George Stark, and Dorothea Wiesmann. 2014. Analysis of labor efforts and their impact factors to solve server incidents in datacenters. In 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’14). IEEE, 424--433.

Digital Library

[40]

Genady Grabarnik, Abdi Salahshour, Balan Subramanian, and Sheng Ma. 2004. Generic adapter logging toolkit. In Proceedings of the 1st IEEE International Conference on Autonomic Computing (ICAC’04). 308--309.

[41]

Peter D. Grünwald. 2007. The Minimum Description Length Principle. The MIT Press.

[42]

Peter D. Grünwald, In Jae Myung, and Mark A Pitt. 2005. Advances in Minimum Description Length: Theory and Applications. MIT Press.

[43]

Tias Guns, Siegfried Nijssen, and Luc De Raedt. 2013. k-Pattern set mining under constraints. IEEE Trans. Knowl. Data Eng. 25, 2 (2013), 402--418.

Digital Library

[44]

Rajeev Gupta, K. Hima Prasad, Laura Luan, Daniela Rosu, and Christopher Ward. 2009. Multi-dimensional knowledge integration for efficient incident management in a services cloud. In Proceedings of the IEEE International Conference on Services Computing, 2009 (SCC’09). IEEE, 57--64.

Digital Library

[45]

Valery Guralnik and Jaideep Srivastava. 1999. Event detection from time series data. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’99). 33--42.

Digital Library

[46]

Jiawei Han, Jian Pei, Behzad Mortazavi-Asl, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu. 2000. FreeSpan: Frequent pattern-projected sequential pattern mining. In Proceedings of the 20th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining (SIGKDD’00). 355--359.

Digital Library

[47]

Jiawei Han, Jianyong Wang, Ying Lu, and Petre Tzvetkov. 2002. Mining top-k frequent closed patterns without minimum support. In Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE, 211--218.

Digital Library

[48]

Joseph L. Hellerstein, Sheng Ma, and Chang-Shing Perng. 2002a. Discovering actionable patterns in event data. IBM Syst. J. 43, 3 (2002), 475--493.

Digital Library

[49]

Joseph L. Hellerstein, Sheng Ma, and C.-S. Perng. 2002b. Discovering actionable patterns in event data. IBM Syst. J. 41, 3 (2002), 475--493.

Digital Library

[50]

Antonio Hernandez-Barrera. 1996. Finding an o(n²log n) algorithm is sometimes hard. In Proceedings of the 8th Canadian Conference on Computational Geometry. 289--294.

[51]

Frank Höppner. 2001. Discovery of temporal patterns. In Principles of Data Mining and Knowledge Discovery, 192--203.

[52]

Paul Horn. 2001. Automatic Computing: IBM’s Prospective on the State of Information Technology. Retrieved from http://www.research.ibm.com/autonomic.

[53]

K. Houck, S. Calo, and A. Finkel. 1995. Towards a practical alarm correlation system. In Integrated Network Management IV. Springer, 226--237.

[54]

Navendu Jain and Rahul Potharaju. 2016. Problem inference from support tickets. (Jan. 5 2016). Patent No. 9,229,800.

[55]

Yexi Jiang, Chang-Shing Perng, and Tao Li. 2011. Natural event summarization. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM’11). 765--774.

Digital Library

[56]

Yexi Jiang, Chang-Shing Perng, and Tao Li. 2014. META: Multi-resolution framework for event summarization. In SIAM International Conference on Data Mining.

[57]

Yexi Jiang, Chang-Shing Perng, Tao Li, and Rong Chang. 2012. Intelligent cloud capacity management. In Proceedings of the 2012 IEEE Network Operations and Management Symposium (NOMS’12). IEEE, 502--505.

[58]

Cristina Kadar, Dorothea Wiesmann, Jose Iria, Dirk Husemann, and Mario Lucic. 2011. Automatic classification of change requests for improved it service quality. In Proceedings of the 2011 Annual Service Research and Innovation Institute Global Conference (SRII’11). IEEE, 430--439.

Digital Library

[59]

George Karypis. 2001. Evaluation of item-based top-N recommendation algorithms. In Proceedings of the ACM International Conference on Information and Knowledge Management. 247--254.

Digital Library

[60]

Jeffrey O. Kephart and David M. Chess. 2003. The vision of autonomic computing. Computer (2003), 41--50.

Digital Library

[61]

Irfan Khan and Anoop Jain. 2012. A comprehensive survey on sequential pattern mining. Int. J. Eng. Res. Technol. 1, 4 (2012).

[62]

Jerry Kiernan and Evimaria Terzi. 2008. Constructing comprehensive summaries of large event sequences. In Proceedings of the 14th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining (KDD’08). 417--425.

Digital Library

[63]

Jerry Kiernan and Evimaria Terzi. 2009. Constructing comprehensive summaries of large event sequences. ACM Trans. Knowl. Discov. Data 3, 4 (Dec. 2009), 21:1--21:31.

Digital Library

[64]

Jon Kleinberg. 2003. Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7, 4 (2003), 373--397.

Digital Library

[65]

Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the 13th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining (KDD’09). 447--456.

Digital Library

[66]

Solomon Kullback and Richard A. Leibler. 1951. On information and sufficiency. Ann. Math. Stat. 22, 1 (1951), 79--86.

[67]

Srivatsan Laxman, P. S. Sastry, and K. P. Unnikrishnan. 2007. A fast algorithm for finding frequent episodes in event streams. In Proceedings of the 13th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining. ACM, 410--419.

[68]

Srivatsan Laxman, P. Shanti Sastry, and K. P. Unnikrishnan. 2004. Fast algorithms for frequent episode discovery in event sequences. In Proceedings of the 3rd Workshop on Mining Temporal and Sequential Data.

[69]

Tao Li (Ed.). 2015. Event Mining: Algorithms and Applications. CRC Press.

Digital Library

[70]

Tao Li, Feng Liang, Sheng Ma, and Wei Peng. 2005. An integrated framework on mining logs files for computing system management. In ACM Special Interest Group International Conference on Knowledge Discovery in Data Mining. 776--781.

Digital Library

[71]

Tao Li, Yexi Jiang, Chunqiu Zeng, Bin Xia, Wubai Zhou, Wentao Wang, Liang Zhang, Li Xue, and Dewei Bao. 2017. FLAP: An end-to-end event log analysis platform for system management. In Proceedings of the 23nd Annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17).

Digital Library

[72]

Tao Li and Sheng Ma. 2004. Mining temporal patterns without predefined time windows. In Proceedings of the 4th IEEE International Conference on Data Mining, 2004 (ICDM’04). IEEE, 451--454.

[73]

Tao Li and Wei Peng. 2005. A clustering model based on matrix approximation with applications to cluster system log files. In Proceedings of the 16th European Conference on Machine Learning. 625--632.

Digital Library

[74]

Tao Li, Wei Peng, Charles Perng, Sheng Ma, and Haixun Wang. 2010. An integrated data-driven framework for computing system management. IEEE Trans. Syst. Man Cybernet. A: Syst. Hum. 40, 1 (Jan. 2010), 90--99.

Digital Library

[75]

Feng Liang, Sheng Ma, and Joseph L. Hellerstein. 2002. Discovering fully dependent patterns. In Proceedings of the SIAM International Conference on Data Mining (SDM’02). SIAM, 511--527.

[76]

Liwei Liu, Nikolay Mehandjiev, and Dong-Ling Xu. 2011. Multi-criteria service recommendation based on user criteria preferences. In Proceedings of the 5th ACM Conference on Recommender Systems. 77--84.

Digital Library

[77]

Zheng Liu, Tao Li, and Junchang Wang. 2016. A survey on event mining for ICT network infrastructure management. ZTE Commun. 14, 2, 47--55.

[78]

Chen Luo, Jian-Guang Lou, Qingwei Lin, Qiang Fu, Rui Ding, Dongmei Zhang, and Zhe Wang. 2014. Correlating events with time series for incident diagnosis. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2014 ACM SIGKDD, 1583--1592.

Digital Library

[79]

Sheng Ma and Joseph L. Hellerstein. 2001a. Mining mutually dependent patterns. In Proceedings IEEE International Conference on Data Mining (ICDM’01). IEEE, 409--416.

[80]

Sheng Ma and Joseph L. Hellerstein. 2001b. Mining partially periodic event patterns with unknown periods. In Proceedings of the 17th International Conference on Data Engineering, 2001. IEEE, 205--214.

Digital Library

[81]

Adetokunbo Makanju, A. Nur Zincir-Heywood, and Evangelos E. Milios. 2009. Clustering event logs using iterative partitioning. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD’09). 1255--1264.

Digital Library

[82]

Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. 1995. Discovering frequent episodes in sequences extended abstract. In Proceedings of the 1st Conference on Knowledge Discovery and Data Mining.

[83]

Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 3 (1997), 259--289.

Digital Library

[84]

Christopher D. Manning and Hinrich Schuetze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.

[85]

Carl H. Mooney and John F. Roddick. 2013. Sequential pattern mining--approaches and algorithms. In Proc. ACM Int. Conf. Comput. Surv. 45, 2 (2013), 19.

Digital Library

[86]

Thin Nguyen, Dinh Phung, Brett Adams, and Svetha Venkatesh. 2013. Event extraction using behaviors of sentiment signals and burst structure in social media. Knowl. Inf. Syst. 37, 2 (2013), 279--304.

[87]

Juan Carlos Niebles, Hongcheng Wang, and Li Fei-Fei. 2008. Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79, 3 (2008), 299--318.

Digital Library

[88]

Xia Ning and George Karypis. 2011. SLIM: Sparse linear methods for top-N recommender systems. In Proceedings of IEEE International Conference on Data Mining (ICDM’11). 497--506.

Digital Library

[89]

Adam J. Oliner, Alex Aiken, and Jon Stearley. 2008. Alert detection in system logs. In Proceedings of IEEE International Conference on Data Mining (ICDM’08). 959--964.

Digital Library

[90]

Thomas J. Owens. 2007. Survey of Event Processing. Technical Report. DTIC Document.

[91]

Debprakash Patnaik, Srivatsan Laxman, Badrish Chandramouli, and Naren Ramakrishnan. 2012. Efficient episode mining of dynamic event streams. In Proceedings of the IEEE International Conference on Data Mining (ICDM’12). 605--614.

Digital Library

[92]

Jian Pei, Guozhu Dong, Wei Zou, and Jiawei Han. 2002. On computing condensed frequent pattern bases. In Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE, 378--385.

Digital Library

[93]

Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu. 2001. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE’01). IEEE Computer Society, 0215--0215.

[94]

Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, and Hua Zhu. 2000. Mining access patterns efficiently from web logs. In Knowledge Discovery and Data Mining: Current Issues and New Applications. Springer, 396--407.

[95]

Wei Peng, Tao Li, and Sheng Ma. 2005. Mining logs files for data-driven system management. SIGKDD Explor. Newsl. 7, 1 (June 2005), 44--51.

Digital Library

[96]

Wei Peng, Charles Perng, Tao Li, and Haixun Wang. 2007. Event summarization for system management. In Proceedings of the 13th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining (KDD’07). 1028--1032.

Digital Library

[97]

Quang-Khai Pham, Guillaume Raschia, Noureddine Mouaddib, Regis Saint-Paul, and Boualem Benatallah. 2009. Time sequence summarization to scale up chronology-dependent applications. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM’09). 1137--1146.

Digital Library

[98]

Rahul Potharaju, Joseph Chan, Luhui Hu, Cristina Nita-Rotaru, Mingshi Wang, Liyuan Zhang, and Navendu Jain. 2015. ConfSeer: Leveraging customer support knowledge bases for automated misconfiguration detection. Proc. VLDB Endow. 8, 12 (2015), 1828--1839.

Digital Library

[99]

Rahul Potharaju, Navendu Jain, and Cristina Nita-Rotaru. 2013. Juggling the jigsaw: Towards automated problem inference from network trouble tickets. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI’13). 127--141.

[100]

V. Chandra Shekhar Rao and P. Sammulal. 2013. Article: Survey on sequential pattern mining algorithms. Int. J. Comput. Appl. 76, 12 (August 2013), 24--31.

[101]

IBM Market Research. 2003. Autonomic Computing Core Technology Study. (2003).

[102]

Sheldon M. Ross. 1996. Stochastic Processes. Vol. 2. John Wiley 8 Sons, New York, NY.

[103]

Enno Ruijters and Mariëlle Stoelinga. 2015. Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15 (2015), 29--62.

Digital Library

[104]

Abdus Salam and M. Sikandar Hayat Khayal. 2012. Mining top-k frequent patterns without minimum support threshold. Knowl. Inf. Syst. 30, 1 (2012), 57--86.

Digital Library

[105]

Gerard Salton and Michael McGill. 1984. Introduction to Modern Information Retrieval. McGraw-Hill.

Digital Library

[106]

Badrul M. Sarwar, George Karypis, Joseph A. Konstan, and John T. Riedl. 2000. Application of dimensionality reduction in recommender system—A case study. In Proceedings of the ACM Workshop on Web Mining and Web Usage Analysis (WebKDD’00).

[107]

Sigurd Schneider, Ivan Beschastnikh, Slava Chernyak, Michael D. Ernst, and Yuriy Brun. 2010. Synoptic: Summarizing system logs with refinement. In Proceedings of the Workshop on Managing Large-Scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques (SLAML’10) (2010).

[108]

Vikrant Shimpi, Maitreya Natu, Vaishali Sadaphal, and Vaishali Kulkarni. 2014. Problem identification by mining trouble tickets. In Proceedings of the 20th International Conference on Management of Data. Computer Society of India, 76--86.

[109]

Ramakrishnan Srikant and Rakesh Agrawal. 1996. Mining quantitative association rules in large relational tables. ACM SIGMOD Rec. 25, 2 (1996), 1--12.

Digital Library

[110]

Seshan Srirangarajan, Michael Allen, Ami Preis, Mudasser Iqbal, Hock Beng Lim, and Andrew J Whittle. 2013. Wavelet-based burst event detection and localization in water distribution systems. J. Sign. Process. Syst. 72, 1 (2013), 1--16.

Digital Library

[111]

John Stearley. 2004. Towards informatic analysis of Syslogs. In Proceedings of the IEEE International Conference on Cluster Computing. 309--318.

[112]

Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. 2005. Introduction to Data Mining. Addison Wesley.

Digital Library

[113]

Liang Tang and Tao Li. 2010. LogTree: A framework for generating system events from raw textual logs. In Proceedings of IEEE International Conference on Data Mining (ICDM’10). 491--500.

Digital Library

[114]

Liang Tang, Tao Li, and Chang-Shing Perng. 2011. LogSig: Generating system events from raw textual logs. In Proceedings of ACM International Conference on Information and Knowledge Management. 785--794.

Digital Library

[115]

Liang Tang, Tao Li, and Larisa Shwartz. 2012. Discovering lag intervals for temporal dependencies. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 633--641.

Digital Library

[116]

Liang Tang, Tao Li, Larisa Shwartz, and Genady Grabarnik. 2013a. Recommending resolutions for problems identified by monitoring. In Proceedings of IEEE/IFIP International Symposium on Integrated Network Management. 134--142.

[117]

Liang Tang, Tao Li, Larisa Shwartz, Florian Pinel, and Genady Ya Grabarnik. 2013b. An integrated framework for optimizing automatic monitoring systems in large IT infrastructures. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1249--1257.

Digital Library

[118]

Nikolaj Tatti and Jilles Vreeken. 2012. The long and the short of it: summarising event sequences with serial episodes. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 462--470.

Digital Library

[119]

Loren Terveen and Will Hill. 2001. Beyond recommender systems: Helping people help each other. In HCI in the New Millennium. 487--509.

[120]

David Thoenen, Jim Riosa, and Joseph L. Hellerstein. 2001. Event relationship networks: A framework for action oriented analysis in event management. In Proceedings of the 2001 IEEE/IFIP International Symposium on Integrated Network Management. IEEE, 593--606.

[121]

Brad Topol, David Ogle, Donna Pierson, Jim Thoensen, John Sweitzer, Marie Chow, Mary Ann Hoffmann, Pamela Durham, Ric Telford, Sulabha Sheth, and Thomas Studwell. 2003. Automating problem determination: A first step toward self-healing computing systems. IBM White Paper. (October 2003).

[122]

Ricardo Vilalta and Sheng Ma. 2002. Predicting rare events in temporal domains. In Proceedings of the 2002 IEEE International Conference on Data Mining, 2002 (ICDM’03). IEEE, 474--481.

[123]

Chao Wang and Srinivasan Parthasarathy. 2006. Summarizing itemset patterns using probabilistic models. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 730--735.

Digital Library

[124]

Peng Wang, Haixun Wang, Majin Liu, and Wei Wang. 2010. An algorithmic approach to event summarization. In Proceedings of the 2010 ACM International Conference on Management of Data (SIGMOD’10). 183--194.

Digital Library

[125]

Peilong Wang, Xuan Yang, Yuanyuan Zhang, and Jiadong Ren. 2012. An algorithm based on temporary table for mining top-k closed frequent patterns in data streams. Int. J. Dig. Content Technol. Appl. 6, 20 (2012).

[126]

Qing Wang, Wubai Zhou, Chunqiu Zeng, Tao Li, Larisa Shwartz, and Genady Ya. Grabarnik. 2017. Constructing the knowledge base for cognitive IT service management. In Proceedings of the 14th IEEE International Conference on Services Computing (SCC’17).

[127]

Chris Wrench, Frederic Stahl, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, and Detlef D. Nauck. 2016. Data stream mining of event and complex event streams: A survey of existing and future technologies and applications in big data. Enterprise Big Data Engineering, Analytics, and Management, Martin Atzmueller, Samia Oussena, and Thomas Roth-Berghofer (Eds.). IGI Global, 24--47.

[128]

Wei Xu, Ling Huang, Armando Fox, David A. Patterson, and Michael I. Jordan. 2008. Mining console logs for large-scale system problem detection. In Proceedings of the International Conference on Systems Modeling Language (SysML’08).

[129]

Xifeng Yan, Hong Cheng, Jiawei Han, and Dong Xin. 2005. Summarizing itemset patterns: A profile-based approach. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 314--323.

Digital Library

[130]

Jiong Yang, Wei Wang, and Philip S. Yu. 2003. Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng. 15, 3 (March 2003), 613--628.

Digital Library

[131]

Junjie Yao, Bin Cui, Yuxin Huang, and Xin Jin. 2010. Temporal and social context based burst detection from folksonomies. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI). 21--27.

[132]

Mohammed J. Zaki. 2001. SPADE: An efficient algorithm for mining frequent sequences. Mach. Learn. 42, 1--2 (2001), 31--60.

[133]

Chunqiu Zeng, Tao Li, Larisa Shwartz, and Genady Ya Grabarnik. 2014a. Knowledge guided hierarchical multi-label classification over ticket data. (unpublished).

[134]

Chunqiu Zeng, Liang Tang, Tao Li, Larisa Shwartz, and Genady Ya. Grabarnik. 2017b. An integrated framework for mining temporal logs from fluctuating events. (unpublished).

[135]

Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, and Tao Li. 2016. Online context-aware recommendation with time varying multi-armed bandit. In Proceedings of the 20th ACM Special Interest Group International Conference on Knowledge Discovery and Data Mining (SIGKDD’16). 2025--2034.

Digital Library

[136]

Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, and Genady Ya. Grabarnik. 2016. Resolution recommendation for event tickets in service management. IEEE Trans. Netw. Serv. Manag. 13, 4 (2016), 954--967.

Digital Library

Cited By

Yu QZhao NLi MLi ZWang HZhang WSui KPei D(2024)A survey on intelligent management of alerts and incidents in IT servicesJournal of Network and Computer Applications10.1016/j.jnca.2024.103842224(103842)Online publication date: Apr-2024
https://doi.org/10.1016/j.jnca.2024.103842
Ramos Gutiérrez BReina Quintero AParody LGómez López M(2023)When business processes meet complex events in logisticsComputers in Industry10.1016/j.compind.2022.103788144:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compind.2022.103788
Bose ATandon SBasu MGhosh S(2022)Social Media for Post-Disaster Relief: Mapping Needs and Availabilities to UNOCHA Resource ClassesProceedings of the 23rd International Conference on Distributed Computing and Networking10.1145/3491003.3493236(294-298)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1145/3491003.3493236
Show More Cited By

Index Terms

Data-Driven Techniques in Computing System Management

Recommendations

Data-Driven Techniques in Disaster Information Management

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to ...
Read More
Big Data Management: Advanced Issues and Approaches

The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics BDA; the importance of big data management; the ...
Read More
Big Data Analysis and Implementation in Different Areas Using IoT

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 3

May 2018

550 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3101309

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

Issue’s Table of Contents

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2017

Accepted: 01 April 2017

Revised: 01 December 2016

Received: 01 June 2016

Published in CSUR Volume 50, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Research
Refereed

Funding Sources

Ministry of Education/China Mobile joint research
Scientific and Technological Support Project (Society) of Jiangsu
FIU Dissertation Year Fellowship
Chinese National Natural Science Foundation
National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

35
Total Citations
View Citations
1,141
Total Downloads

Downloads (Last 12 months)135
Downloads (Last 6 weeks)21

Other Metrics

View Author Metrics

Citations

Cited By

Yu QZhao NLi MLi ZWang HZhang WSui KPei D(2024)A survey on intelligent management of alerts and incidents in IT servicesJournal of Network and Computer Applications10.1016/j.jnca.2024.103842224(103842)Online publication date: Apr-2024
https://doi.org/10.1016/j.jnca.2024.103842
Ramos Gutiérrez BReina Quintero AParody LGómez López M(2023)When business processes meet complex events in logisticsComputers in Industry10.1016/j.compind.2022.103788144:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compind.2022.103788
Bose ATandon SBasu MGhosh S(2022)Social Media for Post-Disaster Relief: Mapping Needs and Availabilities to UNOCHA Resource ClassesProceedings of the 23rd International Conference on Distributed Computing and Networking10.1145/3491003.3493236(294-298)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1145/3491003.3493236
Cao LChen YZhang ZGui N(2021)A Multiattention-Based Supervised Feature Selection Method for Multivariate Time SeriesComputational Intelligence and Neuroscience10.1155/2021/69111922021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6911192
Guo FYin ZZhou KLi J(2021)PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoostJournal of Chemistry10.1155/2021/62560212021(1-11)Online publication date: 31-Dec-2021
https://doi.org/10.1155/2021/6256021
Tian YSi LZhang XCheng RHe CTan KJin Y(2021)Evolutionary Large-Scale Multi-Objective Optimization: A SurveyACM Computing Surveys10.1145/347097154:8(1-34)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3470971
Abukmeil MFerrari SGenovese APiuri VScotti F(2021)A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation LearningACM Computing Surveys10.1145/345096354:5(1-40)Online publication date: 9-Jul-2021
https://dl.acm.org/doi/10.1145/3450963
Zhao L(2021)Event Prediction in the Big Data EraACM Computing Surveys10.1145/345028754:5(1-37)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3450287
Yu KLiu LLi J(2021)A Unified View of Causal and Non-causal Feature SelectionACM Transactions on Knowledge Discovery from Data10.1145/343689115:4(1-46)Online publication date: 18-Apr-2021
https://dl.acm.org/doi/10.1145/3436891
Gramuglia EStorvik GStakkeland M(2021)Clustering and Automatic Labelling Within Time Series of Categorical Observations—With an Application to Marine Log MessagesJournal of the Royal Statistical Society Series C: Applied Statistics10.1111/rssc.1248370:3(714-732)Online publication date: 4-Jun-2021
https://doi.org/10.1111/rssc.12483
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents