A database perspective on knowledge discovery
Communications of the ACM
Nonmonotonic reasoning in LDL++
Logic-based artificial intelligence
Machine Learning
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Database System Implementation
Database System Implementation
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On Supporting Interactive Association Rule Mining
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Efficient online mining of large databases
International Journal of Business Information Systems
Designing an inductive data stream management system: the stream mill experience
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
An inductive database prototype based on virtual mining views
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
A constraint-based querying system for exploratory pattern discovery
Information Systems
On interactive pattern mining from relational databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
IQL: a proposal for an inductive query language
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Inductive databases and constraint-based data mining
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery
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Almost a decade ago, Imielinski and Mannila introduced the notion of Inductive Databases to manage KDD applications just as DBMSs successfully manage business applications. The goal is to follow one of the key DBMS paradigms: building optimizing compilers for ad hoc queries. During the past decade, several researchers proposed extensions to the popular relational query language, SQL, in order to express such mining queries. In this paper, we propose a completely different and new approach, which extends the DBMS itself, not the query language, and integrates the mining algorithms into the database query optimizer. To this end, we introduce virtual mining views, which can be queried as if they were traditional relational tables (or views). Every time the database system accesses one of these virtual mining views, a mining algorithm is triggered to materialize all tuples needed to answer the query. We show how this can be done effectively for the popular association rule and frequent set mining problems.