A database perspective on knowledge discovery
Communications of the ACM
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Gigascope: high performance network monitoring with an SQL interface
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
The 3W Model and Algebra for Unified Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Experiences with a Logic-Based Knowledge Discovery Support Environment
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Issues in data stream management
ACM SIGMOD Record
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The deductive database system ℒ𝒟ℒ++
Theory and Practice of Logic Programming
An Adaptive Learning Approach for Noisy Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Sampling algorithms in a stream operator
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A native extension of SQL for mining data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Distributed operation in the Borealis stream processing engine
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
Building data mining solutions with OLE DB for DM and XML for analysis
ACM SIGMOD Record
Unifying the Processing of XML Streams and Relational Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query languages and data models for database sequences and data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Expressive power of an algebra for data mining
ACM Transactions on Database Systems (TODS)
Designing an inductive data stream management system: the stream mill experience
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
A Logic-Based Approach to Mining Inductive Databases
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
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Among data-intensive applications that are beyond the reach of traditional Data Base Management Systems (DBMS), data mining stands out because of practical importance and the complexity of the research problems that must be solved before the vision of Inductive DBMS can become a reality. In this paper, we first discuss technical developments that have occurred since the very notion of Inductive DBMS emerged as a result of the seminal papers authored by Imielinski and Mannila a decade ago. The research progress achieved since then can be subdivided into three main problem subareas as follows: (i) language (ii) optimization, and (iii) representation. We discuss the problems in these three areas and the different approaches to Inductive DBMS that are made possible by recent technical advances. Then, we pursue a language-centric solution, and introduce simple SQL extensions that have proven very effective at supporting data mining. Finally, we turn our attention to the related problem of supporting data stream mining using Data Stream Management Systems (DSMS) and introduce the notion of Inductive DSMS. In addition to continuous query languages, DSMS provide support for synopses, sampling, load shedding, and other built-in functions that are needed for data stream mining. Moreover, we show that Inductive DSMS can be achieved by generalizing DSMS to assure that their continuous query languages support efficiently data stream mining applications. Thus, DSMS extended with inductive capabilities will provide a uniquely supportive environment for data stream mining applications.