Topic
Knowledge extraction
About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.
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TL;DR: Results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability.
83 citations
TL;DR: A novel architecture to support the co-operative decision process by utilizing event-driven and task-driven data mining agents, along with user assistant agents and a knowledge manager agent is described.
Abstract: The integration of data mining techniques with decision support systems to assist in dealing with information overload has received increased attention and importance over recent years. However, challenges remain regarding practical deployment and implementation of such integration, due to the increased complexity of decision making, system co-ordination and knowledge communication. It is the purpose of this paper to outline the issues necessary to be addressed in a practical decision support system that integrates data mining techniques. The paper will describe a novel architecture to support the co-operative decision process by utilizing event-driven and task-driven data mining agents, along with user assistant agents and a knowledge manager agent. An internet-based prototype for supporting marketing planning decisions is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based decision support system architecture.
83 citations
Proceedings Article•
10 Nov 1997TL;DR: A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses and provides a user-friendly, interactive data mining environment with good performance.
Abstract: A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. By incorporating several interesting data mining techniques, including OLAP and attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance.
83 citations
TL;DR: This comprehensive review of the literature (over 250 references) deals with high-throughput experimentation in heterogeneous catalysis, with special focus on advanced methods for knowledge discovery such as high- throughput kinetic modeling and QSAR.
83 citations
TL;DR: It is argued that it is critically important to OLAP graph structured data and a novel Graph OLAP framework is proposed, and a discovery-driven multi-dimensional analysis model is proposed to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes.
Abstract: Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases, to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, graphs have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle graph structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP graphs? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze graph data from different perspectives and with multiple granularities. In this paper, we argue that it is critically important to OLAP graph structured data and propose a novel Graph OLAP framework. According to this framework, given a graph dataset with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the graphs can be generalized/specialized dynamically, offering multiple, versatile views of the data. The contributions of this work are three-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the Graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “network” properties of the graph dataset are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting and informative OLAP of graphs, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven Graph OLAP.
83 citations