Data Mining and Knowledge Discovery An International Journal

Special Issue on

Scalable High-Performance Computing for KDD

Guest editors: Paul Stolorz and Ron Musick 

CALL FOR PAPERS

Traditional computational techniques and computer architectures are routinely overwhelmed by sheer volume and complexity of information generated from data-gathering instruments, computational and experimental methodologies, and business operations. The fundamental problem of extracting knowledge and insight from massive databases and datasets is shared across a wide range of fields in business, academia and government. The new field of Data Mining and Knowledge Discovery in Databases (KDD) has arisen as an interdisciplinary response to this situation, merging ideas drawn from disciplines such as statistics, pattern recognition, machine learning, databases, visualization and high performance computing.

This special issue of Data Mining and Knowledge Discovery is devoted to the challenge of applying data mining and knowledge discovery methods to large, complex datasets. Implementation of data mining ideas in high-performance computing environments is crucial for coping with large-scale data. In particular, parallel and distributed systems are needed to ensure system scalability as datasets grow inexorably in size and scope. These environments include dedicated massively parallel supercomputers, super-servers built from clusters of commodity workstations and high-speed network interfaces, and heterogeneous networks distributed over regional, national and global scales. High-performance and parallel computing holds the promise of scaling to large data sets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow. In addition, it promises to render the KDD process much more interactive by allowing fast response times for difficult search and model fitting problems.

Data Mining and Knowledge Discovery, published by Kluwer Academic publishers, is the flagship publication in the rapidly growing area of KDD. In this special issue we solicit the most dramatic new developments in high performance large-scale KDD applications, highlighting the promise of the technology and identifying the main challenges for the future. Technically innovative papers that describe new theoretical developments, or tackle the application of practical data mining approaches to real problems and datasets on parallel and distributed architectures, are solicited. Topics of interest include, but are not limited to, the intersection of KDD with the following fields:

Parallel implementations of datamining & KDD methods:

• Classification and regression: e.g. decision trees, neural nets
• Pattern recognition
• Belief nets and other Bayesian approaches
• Genetic programming
• Association rules
• Statistical inference
• Similarity detection and measurement
• Clustering and density estimation
• Change-detection
• Text retrieval
• Content-based indexing
• Data visualization
• Trend Analysis

Integration of KDD techniques with scalable I/O systems:

• Data warehouses & federated databases
• Parallel file systems
• High-performance network interfaces
• Intelligent data layout
• Out-of-core algorithms
• Parallel relational querying
• High performance storage systems
• Hierarchical and distributed storage

Methods to control complexity:

• Random sampling
• Anytime algorithms applied to datamining techniques
• New complex data-type algorithms (eg. not based on feature vectors)
• Domain simplification techniques
• Inference error/confidence characterization

Parallel, clustered and/or distributed applications:

• Datamining on commodity-based clusters and networks
• Web-oriented datamining
• Novel applications and case studies
• Knowledge discovery systems and tools

SUBMISSION INSTRUCTIONS

Electronic submissions are STRONGLY ENCOURAGED. Postscript copies of papers may be emailed to dmkdpar@aig.jpl.nasa.gov. Latex style files and related instructions can be obtained at the web site http://www.research.microsoft.com/research/datamine .

IMPORTANT DATES

 SUBMISSION DEADLINE: May 8, 1997

ACCEPTANCE NOTIFICATION: June 20, 1997

Enquiries about the submission process and scope of the special issue may be sent to dmkdpar@aig.jpl.nasa.gov.

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