Discovering patterns in sequences of events
Artificial Intelligence
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Fast parallel and serial approximate string matching
Journal of Algorithms
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
A new approach to text searching
Communications of the ACM
Fast text searching: allowing errors
Communications of the ACM
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Incremental updates of inverted lists for text document retrieval
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
String searching algorithms
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
A fast string searching algorithm
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Constructing Suffix Trees On-Line in Linear Time
Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1 - Volume I
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Searching Large Lexicons for Partially Specified Terms using Compressed Inverted Files
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Color Set Size Problem with Application to String Matching
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
Approximate String-Matching over Suffix Trees
CPM '93 Proceedings of the 4th Annual Symposium on Combinatorial Pattern Matching
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
A data mining-based engineering design support system: a research agenda
Data mining for design and manufacturing
Using Site Semantics to Analyze, Visualize, and Support Navigation
Data Mining and Knowledge Discovery
Prediction of Web Page Accesses by Proxy Server Log
World Wide Web
Monitoring Change in Mining Results
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering Local Patterns from Multiple Temporal Sequences
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Active Mining in a Distributed Setting
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Efficient Feature Mining in Music Objects
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
Information Systems - Databases: Creation, management and utilization
Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data
Design and application of hybrid intelligent systems
Interactive sequence discovery by incremental mining
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Efficient mining of sequential patterns with time constraints by delimited pattern growth
Knowledge and Information Systems
Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences
Data Mining and Knowledge Discovery
On similarity of financial data series based on fractal dimension
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Case-based sequential ordering of songs for playlist recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
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Most daily and scientific data are sequential in nature. Discoveringimportant patterns from such data can benefit the user and scientist bypredicting coming activities, interpreting recurring phenomena, extractingoutstanding similarities and differences for close attention, compressingdata, and detecting intrusion. We consider the following incrementaldiscovery problem for large and dynamic sequential data. Suppose thatpatterns were previously discovered and materialized. An update is made tothe sequential database. An incremental discovery will take advantage ofdiscovered patterns and compute only the change by accessing the affectedpart of the database and data structures. In addition to patterns, thestatistics and position information of patterns need to be updated to allowfurther analysis and processing on patterns. We present an efficientalgorithm for the incremental discovery problem. The algorithm is applied tosequential data that honors several sequential patterns modeling weatherchanges in Singapore. The algorithm finds what it is supposed to find.Experiments show that for small updates and large databases, the incrementaldiscovery algorithm runs in time independent of the data size.