Mining Sequential Patterns on Data Streams

Chedy Raissi
EMA/LGI2P - LIRMM
Chedy.Raissi@ema.fr


Abstract

Recently, the data mining community has focused on a new
challenging model where data arrives sequentially in the form of
continuous rapid streams. It is often referred to as data streams
or streaming data. Many real-world applications data are more
appropriately handled by the data stream model than by traditional
static databases. Such applications can be: stock tickers, network
traffic measurements, transaction flows in retail chains, click
streams, sensor networks and telecommunications call records. We 
propose a new approach, called Speed
(Sequential Patterns Efficient Extraction in Data
streams), to identify sequential patterns in a data stream. 
To the best of our knowledge this is the first approach defined for
mining sequential patterns in streaming data. The main originality
of our mining method is that we use a novel data structure to
maintain frequent sequential patterns coupled with a fast pruning
strategy. At any time, users can issue requests for frequent
sequences over an arbitrary time interval. Furthermore our
approach produces an approximate answer with an assurance that it
will not bypass user-defined frequency and temporal thresholds.
Finally the proposed method is analyzed by a series of experiments
on different datasets.