Modeling KDD processes within the inductive database framework

Jean François Boulicaut; Mika Klemettinen; Heikki Mannila

doi:10.1007/3-540-48298-9_31

Modeling KDD processes within the inductive database framework

Jean François Boulicaut, Mika Klemettinen, Heikki Mannila

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

29 Citations (Scopus)

Abstract

One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally defined generalizations about the data. We formalize this concept and show how it can be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be defined using normal database terminology. We demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.

Original language	English
Title of host publication	Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings
Editors	A. Min Tjoa, Mukesh Mohania
Publisher	Springer
Pages	293-302
Number of pages	10
ISBN (Print)	3540664580, 9783540664581
DOIs	https://doi.org/10.1007/3-540-48298-9_31
Publication status	Published - 1999
MoE publication type	A4 Conference publication
Event	International Conference on Data Warehousing and Knowledge Discovery - Florence, Italy Duration: 30 Aug 1999 → 1 Sept 1999 Conference number: 1

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1676
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Data Warehousing and Knowledge Discovery
Abbreviated title	DaWak
Country/Territory	Italy
City	Florence
Period	30/08/1999 → 01/09/1999

Access to Document

10.1007/3-540-48298-9_31

Cite this

Boulicaut, J. F., Klemettinen, M., & Mannila, H. (1999). Modeling KDD processes within the inductive database framework. In A. M. Tjoa, & M. Mohania (Eds.), Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings (pp. 293-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1676). Springer. https://doi.org/10.1007/3-540-48298-9_31

Boulicaut, Jean François ; Klemettinen, Mika ; Mannila, Heikki. / Modeling KDD processes within the inductive database framework. Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings. editor / A. Min Tjoa ; Mukesh Mohania. Springer, 1999. pp. 293-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{18df327ea8e1408482660400a5ef12e6,

title = "Modeling KDD processes within the inductive database framework",

abstract = "One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally defined generalizations about the data. We formalize this concept and show how it can be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be defined using normal database terminology. We demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.",

author = "Boulicaut, {Jean Fran{\c c}ois} and Mika Klemettinen and Heikki Mannila",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; International Conference on Data Warehousing and Knowledge Discovery, DaWak ; Conference date: 30-08-1999 Through 01-09-1999",

year = "1999",

doi = "10.1007/3-540-48298-9_31",

language = "English",

isbn = "3540664580",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "293--302",

editor = "Tjoa, {A. Min} and Mukesh Mohania",

booktitle = "Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings",

address = "Germany",

}

Boulicaut, JF, Klemettinen, M & Mannila, H 1999, Modeling KDD processes within the inductive database framework. in AM Tjoa & M Mohania (eds), Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1676, Springer, pp. 293-302, International Conference on Data Warehousing and Knowledge Discovery, Florence, Italy, 30/08/1999. https://doi.org/10.1007/3-540-48298-9_31

Modeling KDD processes within the inductive database framework. / Boulicaut, Jean François; Klemettinen, Mika; Mannila, Heikki.
Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings. ed. / A. Min Tjoa; Mukesh Mohania. Springer, 1999. p. 293-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1676).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Modeling KDD processes within the inductive database framework

AU - Boulicaut, Jean François

AU - Klemettinen, Mika

AU - Mannila, Heikki

N1 - Conference code: 1

PY - 1999

Y1 - 1999

N2 - One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally defined generalizations about the data. We formalize this concept and show how it can be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be defined using normal database terminology. We demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.

AB - One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally defined generalizations about the data. We formalize this concept and show how it can be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be defined using normal database terminology. We demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.

U2 - 10.1007/3-540-48298-9_31

DO - 10.1007/3-540-48298-9_31

M3 - Conference article in proceedings

AN - SCOPUS:84903198782

SN - 3540664580

SN - 9783540664581

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 293

EP - 302

BT - Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings

A2 - Tjoa, A. Min

A2 - Mohania, Mukesh

PB - Springer

T2 - International Conference on Data Warehousing and Knowledge Discovery

Y2 - 30 August 1999 through 1 September 1999

ER -

Boulicaut JF, Klemettinen M, Mannila H. Modeling KDD processes within the inductive database framework. In Tjoa AM, Mohania M, editors, Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings. Springer. 1999. p. 293-302. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-48298-9_31

Modeling KDD processes within the inductive database framework

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this