Abstract
In this paper, we propose I-MIN model for knowledge discovery and knowledge management in evolving databases. The model splits the KDD process into three phases. The schema designed during the first phase, abstracts the generic mining requirements of the KDD process and provides a mapping between the generic KDD process and (user) specific KDD subprocesses. The generic process is executed periodically during the second phase and windows of condensed knowledge called knowledge concentrates are created. During the third phase, which corresponds to actual mining by the end users, specific KDD subprocesses are invoked to mine knowledge concentrates. The model provides a set of mining operators for the development of mining applications to discover and renew, preserve and reuse, and share knowledge for effective knowledge management. These operators can be invoked by either using a declarative query language or by writing applications.
The architectural proposal emulates a DBMS like environment for the managers, administrators and end users in the organization. Knowledge management functions, like sharing and reuse of the discovered knowledge among the users and periodic updating of the discovered knowledge are supported. Complete documentation and control of all the KDD endeavors in an organization are facilitated by the I-MIN model. This helps in structuring and streamlining the KDD operations in an organization.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anand SS, Scotney BW, Tan MG (1997) Designing a kernel for data mining. IEEE Expert Syst Appl 12(2):65–74
Bhatnagar V (2001) Intension mining: a new approach to knowledge discovery in databases. PhD Thesis, Jamia Millia Islamia, New Delhi, India
Brunk C, Kelly J, Kohavi R (1997) Mineset: an integrated system for data mining. In: Proceedings of 3rd international conference on knowledge discovery and data mining
Cai Y, Cercone N, Han J (1990) An attribute oriented approach for learning classification rules from relational database. In: Proceedings of 6th international conference on data engineering, pp 281–288
Codasyl Systems Committee (1971) Introduction to “feature analysis of generalized database management Systems.” Comm ACM 14(5):308–318
DasGupta S (2000) An incremental rule-base classifier for intension data mining. Thesis, Indian Institute of Technology, New Delhi, Hauz Khas, India
Data Base Task Group (1971) CODASYL DBTG April 71 report. DBTG, ACM
Date CJ (2000) An introduction to database systems, 7th edn. Addison-Wesley Longman
Domingos P, Hulten G (2001) Catching up with the data: research issues in mining data streams. In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery, Santa Barbara, CA
Fayyad UM, Piatetsky-Shaperio G, Smyth P, Uthurusamy R (eds) (1996) Advances in knowledge discovery in databases. AAAI/MIT Press
Fry JP, Sibley EH (1976) Evolution of database management system. ACM Comput Surv 8(1):7–42
Ganti V, Gehrke J, Ramakrishnan R, Loh WY (1999) FOCUS: a framework for measuring differences in data characteristics. In: Proceedings of 18th symposium on PODS
Gherke J (2001) Report on SIGKDD 2001 conference panel: new research direction in KDD. SIGKDD Explor 3(2):75–76
Gibbons PB, Matias Y (1999) Synopsis data structure for massive data sets. In: DIMACS: series in discrete mathematics and theoretical computer science: special issue on external memory Algorithms Visualiz, vol A
Gupta SK, Bhatnagar V, Wasan SK (2000) User-centric mining of association rules. In: Workshop on data mining, decision support, meta learning and ILP, PKDD’2000
Gupta SK, Bhatnagar V, Wasan SK (2001) A proposal for data mining management system. In: Workshop on data mining and knowledge management, ICDM
Gupta SK, Bhatnagar V, Wasan SK, Kumar N (2003) Knowledge discovery in evolving databases. Available on request
Gupta SK, Bhatnagar V, Wasan SK, Somayajulu D (2000) Intension mining: a new paradigm in knowledge discovery. Technical Report IITD/CSE/TR2000/001, Indian Institute of Technology, Hauz Khas, New Delhi, India
Gupta SK, Suresh P, Bhatnagar V (2002) Decision tree classifications using knowledge concentrates. In: SPIE conference on data mining, Orlando, FL
Han J, Fu Yongjian, Wang W, Chiang J (1996) DBMiner: a system for data mining in large relational databases. In: Proceedings of international conference on data mining and knowledge discovery, Portland, OR, August 1996, pp 250–255
Imielinski T, Mannila H (1996) A database perspective on knowledge discovery. Commun ACM, pp 58–64
Klosgen W, Zytkow J (eds) (1999) Handbook of data mining and knowledge discovery. Oxford University Press
Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of international conference on knowledge discovery and data mining (KDD-98)
Macintosh (1998) A Knowledge Management. http://www.aiai.ed.ac.uk/∼alm/kamlnks.html
Maheshwari S (2000) Incremental clustering in intension mining framework. Thesis, Indian Institute of Technology, New Delhi, Hauz Khas, India
Matheus CJ, Chan PK, Piatetsky-Sahpiro G (1993) System for knowledge discovery in databases. IEEE Trans Knowl Data Eng 5(6):903–913
Meo R (1999) A new approach for the discovery of frequent itemsets. In: Proceedings of first international conference on data warehousing and knowledge discovery
Psaila G (1998) Integration of data mining techniques and relational databasaes. Thesis, Politecnico de Torino, Italy
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Reinartz T (1999) Focusing solutions for data mining. Lecture notes in artificial intelligence, vol 1623. Springer, Berlin Heidelberg New York
Silberschatz A, Korth HF, Sudershan (2001) Database system concepts, 4th edn. McGraw-Hill
Silicon Graphics Inc. MineSet. Data mining package. http://www.sgi.com/software/mineset/
Srikant R, Quest synthetic data generation code. http://www.almaden.ibm.com/software/quest/Resources
Stodder D (2000) After the goldrush: data mining in the new economy. Invited Talk, KDD—2000
Study Group on DBMS (1978) The ANSI/X3/SPARC/DBMS framework: report of the study group on database management systems, information systems
Suresh P (2001) Classification in intension mining framework. Thesis, Indian Institute of Technology, New Delhi, Hauz Khas, India
Taylor RW, Frank RL (1976) Codasyl database management system. ACM Comput Surv 8(1):67–103
University of California at Irvine (1998) UCI machine learning repository. http://www.ics.uci.edu/∼mlearn/MLSummary
Virmani A (1998) Second generation data mining: concepts and implementation. Thesis, Rutgers University, NJ
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of international conference on management of data (SIGMOD), pp 103–114
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gupta, S., Bhatnagar, V. & Wasan, S. Architecture for knowledge discovery and knowledge management. Knowl Inf Syst 7, 310–336 (2005). https://doi.org/10.1007/s10115-004-0153-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-004-0153-x