Information Exploration Shootout or "Benchmarks for Information Exploration"

Abstract

Many now recognize that mining for information and knowledge from large databases and documents will be the next fundamental impact in database systems, knowledge discovery, and visualization. This is considered an important area for major cost savings and potential revenue, and it has immediate applications in decision systems, intelligence, information management, business, and communicationin the form of both on-line services and the World Wide Web. Data mining now draws from fields including databases, statistics, information technology, data visualization, and artificial intelligence, especially machine learning and knowledge-based systems. There is a clear sense that, to achieve the next increase in knowledge exploitation, individual data exploration approaches must work together. The ProblemHow does one discover information and knowledge in datasets -e.g., databases, archives, document collections, television news reports, the Web? What process do analysts and other data explorers use in discovering non-trivial patterns? How do, or should, knowledge discovery, statistics, and visualization work together to support the human exploration process [1, 2, 3]? What are the procedures for using visualization and analytic agents, in context with the human operator, to achieve timely, computationally responsive discoveries in data?

Immune system approaches to intrusion detection – a review

“…To generate the self profiles they use the Information Exploration Shootout dataset [15]. This data contains five specified attacks.…”

Immune System Approaches to Intrusion Detection - A Review

“…The authors compare their results with the negative selection based system described by Hofmeyr [18]. To generate the self profiles they use the Information Exploration Shootout dataset [15]. This data contains five specified attacks.…”