an efficient algorithm for mining time interval based patterns in large databases
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An Efficient Algorithm for Mining Time Interval-based Patterns in Large Databases . Yi-Cheng Chen, Ji -Chiang Jiang, Wen-Chih Peng and Suh -Yin Lee Department of Computer Science National Chiao Tung University Hsinchu , Taiwan 300

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an efficient algorithm for mining time interval based patterns in large databases

An Efficient Algorithm for Mining Time Interval-based Patterns in Large Databases

Yi-Cheng Chen, Ji-Chiang Jiang, Wen-ChihPeng and Suh-Yin Lee

Department of Computer Science

National Chiao Tung University

Hsinchu, Taiwan 300

{ejen.cs95g, perrys0620.cs96g}@nctu.edu.tw [email protected] [email protected]

CIKM, 2010

outline
OUTLINE
  • 1.INTRODUCTION
  • 2.PROBLEM DEFINITION
  • 3.INCISION STRATEGY
  • 4.COINCIDENCE REPRESENTATION
  • 5.CTMiner ALGORITHM
  • 6.EXPERIMENTAL RESULTS
  • 7.CONCLUSION AND FUTURE WORK
1 introduction
1.INTRODUCTION
  • All related researches in this domain are based on Allen’s temporal logics.
  • Which there are 13 temporal relations between any two event intervals .
1 introduction1
1.INTRODUCTION

Compare with previous works:

  • Kam et al. - hierarchical representation.
  • Hoppner - scan database by sliding window.
  • Papapetrou - Hybrid-DFS algorithm.
  • Wu et al. - TPrefixSpan.
  • Patel et al. - Augmented Representation(By additional counting information ), and IEMiner.
1 introduction2
1.INTRODUCTION

Propose :

  • Incision strategy
  • Coincidence representation
  • CTMiner (Coincidence Temporal Miner)
2 problem definition
2.PROBLEM DEFINITION

Event interval and event sequence

  • E = {e1, e2,…, ek} be the set of event symbols.
  • (ei, si, fi), ei∈ E, si , fi,are time points, si < fi
  • Event start:ei.tsEvent finish:ei.tf
  • {(e1, s1, f1), (e2, s2, f2), …, (en, sn, fn)} where si≤si+1 and si< fi
2 problem definition1
2.PROBLEM DEFINITION

Temporal database

  • Database D = {r1, r2, …, rm}, each record ri, where 1≤ i≤ m
  • A record riconsists of a sequence-id and an event interval(start time and finish time).
  • Records in the database D with the same client-id are grouped together.
  • Database D can be viewed as a collection of event sequences.
2 problem definition2
2.PROBLEM DEFINITION

Time set and time sequence

  • An event sequence q = {(e1, s1, f1), (e2, s2, f2), …, (en, sn, fn)}
  • The set T ={s1, f1, s2, f2, …, si, fi,…, sn, fn} is called a time set corresponding to sequence q.
  • Order all the elements in T and eliminate redundant element, we got sequence Ts.sequence Ts = {t1, t2, t3, …, tk}where ti∈ T , ti< ti+1.
2 problem definition4
2.PROBLEM DEFINITION
  • Event slice

(en, sn, fn)(B,1,5),(D,8,4),(E,10,13),(F,10,13)

4 event intervals in sequence 2

Corresponding time set T={1,5,8,14,10,13,10,13}{s1, f1, s2, f2, s3, f3, s4, f4 }

Time sequence Ts ={1,5,8,10,13,14}{t1, t2, t3, …, tk}

2 problem definition5
2.PROBLEM DEFINITION

Event slice

  • Let set L = { +, -, *, Φ}, a set of event sequences Q = {q1, q2, …, qi,…}, qi= {(e1, s1, f1), …, (ej, sj, fj) , … (en, sn, fn)}
2 problem definition6
2.PROBLEM DEFINITION
  • Event slice

start slice D+= (D, 8, 10)intermediate slice D*= (D, 10, 13)finish slice D-= (D, 13, 14)

The event interval B has only one intact slice B = (B, 1, 5)

3 incision strategy1
3.INCISION STRATEGY
  • Incision example
3 incision strategy2
3.INCISION STRATEGY
  • Incision example

The incision strategy can totally avoid the generation of intermediate slices. By trimming the intermediate slices, we can still express the relationship between any two intervals correctly.

4 coincidence representation
4.COINCIDENCE REPRESENTATION
  • Group simultaneously occurring slices together to form the coincidences.
  • Concatenation with all coincidences can describe an event sequence effectively.
  • Simplify the processing of complex pairwise relationships between all intervals efficiently.
4 coincidence representation2
4.COINCIDENCE REPRESENTATION
  • Good scalability
  • Nonambiguity
  • Simple is good
  • Compact space usage
6 experimental results
6.EXPERIMENTAL RESULTS
  • Runtime performance on synthetic data sets
6 experimental results1
6.EXPERIMENTAL RESULTS
  • Real world dataset analysis
7 conclusion and future work
7.CONCLUSION AND FUTURE WORK
  • Coincidence representation is nonambiguous and has several advantages over existing representations .
7 conclusion and future work1
7.CONCLUSION AND FUTURE WORK
  • Further:mining closed and maximal temporal patterns, incremental temporal patterns mining, and the research of method toward data stream.
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