Splitter: Mining finegrained sequential patterns in semantic trajectories

Chao Zhang, Jiawei Han, Lidan Shou, Jiajun Lu, Thomas La Porta

Research output: Contribution to journalConference articlepeer-review


Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent "items". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns. That said, it remains a challenging task to mine what we call fine-grained sequential patterns, which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously. We propose SPLITTER to effectively mine such fine-grained sequential patterns in two steps. In the first step, it retrieves a set of spatially coarse patterns, each attached with a set of trajectory snippets that precisely record the pattern's occurrences in the database. In the second step, SPLITTER breaks each coarse pattern into fine-grained ones in a top-down manner, by progressively detecting dense and compact clusters in a higher-dimensional space spanned by the snippets. SPLITTER uses an effective algorithm called weighted snippet shift to detect such clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process. Our experiments on both real and synthetic data sets demonstrate the effectiveness and efficiency of SPLITTER.

Original languageEnglish (US)
Pages (from-to)769-780
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number9
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: Sep 1 2014Sep 5 2014

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


Dive into the research topics of 'Splitter: Mining finegrained sequential patterns in semantic trajectories'. Together they form a unique fingerprint.

Cite this