KDD 2017

IoT in Practice: Case Studies in Data Analytics, with Issues in Privacy and Security.

Presenters:  Albert Bifet, Latifur Khan, Joao Gama, and Wei Fan



The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. The advent of IoT applications is here: industry 4.0, connected industry, industry automation, smart cities, smart grids, energy efficiency, etc. All this IoT applications require advanced analysis of big data streams from sensors and small devices, while addressing security and privacy concerns. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for several learning tasks including distributed algorithms. The second part we present few applications for predictive maintenance, prediction for renewable energies, and social network analysis for telecommunications data streams. The last part dwells upon security concerns regarding IoT data streams containing sensitive and confidential data when predictive analytics is performed over a third-party cloud service.



  • IoT Fundamentals and IoT Stream Mining Algorithms
    • Predictive Learning
    • Descriptive Learning
    • Frequent Pattern mining
  • Case Study: Social Network Analysis
    • Challenges in mining networked data
    • Online sampling
    • Evolving centralities and communities
    • Tracking the dynamics of evolving communities
  • Case Study: Predictive Maintenance
    • Problem Definition
    • Change, Anomaly and Novelty Detection
    • Failure Prediction and Detection
  • Secure IoT Stream Mining
    • Types of attacks (e.g., Controlled Channel and Timing)
    • Securing data and system logs
    • Defense against side-channel attacks
      • Data-Obliviousness
      • Randomization


Short Bio.  


Albert Bifet's Profile

Albert Bifet is Associate Professor at Telecom ParisTech and Honorary Research Associate at the WEKA Machine Learning Group at University of Waikato. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He is serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2016, 2015, 2014, 2013, 2012).

Latifur Khan's Profile

Latifur Khan is a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas where he has been teaching and conducting research since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from the University of Southern California in August of 2000, and December of 1996 respectively. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics. Dr. Khan is an ACM Distinguished Scientist and a Senior Member of IEEE. He has chaired several conferences and serves (or has served) as associate editor on multiple editorial boards including IEEE Transactions on Knowledge and Data Engineering (TKDE) journal. He has conducted tutorial sessions in prominent conferences such as ACM WWW 2005, MIS2005, DASFAA 2007, and WI 2008 ( "Matching Words and Pictures - Problems, Applications, and Progress" ) and PAKDD 2011 ( "Data Stream Mining Challenges and Techniques").

Joao Gama's Profile

Joao Gama received, in 2000, his  Ph.D. degree in Computer Science from the Faculty of Sciences of the University of Porto, Portugal. He joined the Faculty of Economy where he holds the position of Associate Professor. He is also a senior researcher and vice-director of LIAAD, a group belonging to INESC TEC. He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery,  Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML'2005, DS'2009, ADMA'2009, IDA' 2011, and ECM-PKDD'2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECMLPKDD conferences and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is author of several books in Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS.a Researcher at LIAAD, University of Porto, working at the Machine Learning group. His main research interest is in Learning from Data Streams. He published more than 80 articles. He served as Co-chair of ECML 2005, DS09, ADMA09 and a series ofWorkshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is serving as Co-Chair of next ECM-PKDD 2015. He is author of a recent book on Knowledge Discovery from Data Streams.           


Wei Fan's Profile

Wei Fan is the Head of Baidu Research Big Data Lab. He received his PhD in Computer Science from Columbia University in 2001. His main research interests and experiences are in various areas of data mining and database systems, such as, stream computing, high performance computing, extremely skewed distribution, cost-sensitive learning, risk analysis, ensemble methods, easy-touse nonparametric methods, graph mining, predictive feature discovery, feature selection, sample selection bias, transfer learning, time series analysis, bioinformatics, social network analysis, novel applications and commercial data mining systems. His co-authored paper received ICDM'€™2006 Best Application Paper Award, he led the team that used his Random Decision Tree method to win 2008 ICDM Data Mining Cup Championship. He received 2010 IBM Outstanding Technical Achievement Award for his contribution to IBM Infosphere Streams. He is the associate editor of ACM Transaction on Knowledge Discovery and Data Mining (TKDD). At Huawei, he led his colleagues to develop Huawei StreamSMART, €“ a streaming platform for online and real-time processing, query and mining of very fast streaming data. In addition, he also led his colleagues to develop a real-time processing and analysis platform of Mobile Broad Band (MBB) data.  

Albert Bifet,
Aug 13, 2017, 6:31 AM
Albert Bifet,
Aug 13, 2017, 6:30 AM
Albert Bifet,
Aug 13, 2017, 6:26 AM