Mining Big Time-series Data on the Web
Yasushi Sakurai, Yasuko Matsubara (Kumamoto U) and Christos Faloutsos (CMU/SCS)

Yasushi Sakurai Yasuko Matsubara Christos Faloutsos


Description (pdf): [PDF]
Abstract: Online news, blogs, SNS and many other Web-based services has been attracting considerable interest for business and marketing purposes. Given a large collection of time series, such as web-click logs, online search queries, blog and review entries, how can we efficiently and effectively find typical time-series patterns? What are the major tools for mining, forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability. The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find meaningful patterns in large-scale time-series data. Specifically we review the state of the art in three related fields: (1) similarity search, pattern discovery and summarization, (2) non-linear modeling and forecasting, and (3) the extension of time-series mining and tensor analysis. We also introduce case studies that illustrate their practical use for social media and Web-based services.


  • Part0: Introduction
  • Part1: Similarity search, pattern discovery and summarization
  • Part2: Non-linear modeling and forecasting
  • Part3: Extension of time-series data: tensor analysis
  • Part4: Conclusions