Suchergebnisse

Chance discovery is to become aware of and to explain the significance of a chance, that is, a piece of information about events or situations that is significant for decision making. Sometimes a chance is rare and its significance is unnoticed. This paper proposes a method to merge three keys for chance discovery: (1) communication; (2) imagination and (3) data mining. Applied to the case of meal service, a visualised data mining method KeyGraph is used for discovering unnoticed demands underlying family consumption behaviour. The visualised relations between usual and unusual consumption patterns stimulate the awareness of and the communication among housewives talking in a room. This leads to the discovery of latent family consumption demands and to the proposal of serving meals that have unnoticed significant merits for their families.

KeyGraph, a document–indexing (keyword–extraction) algorithm, is applied for a new purpose: Extracting active faults with risks of near–future large earthquakes from earthquake–sequences. This paper presents KeyGraph as an extractor of causalities from an event–sequence. This validates KeyGraph as a tool for showing why and which active faults are risky, as well as for showing why and which words abstract a document. The risky faults that are empirically obtained by KeyGraph correspond closely to real earthquake occurrences and seismologists' risk estimation.

This book explores the employment of market mechanisms for data-interactive innovations. Based on the concept of innovators' marketplaces the book introduces a new concept of 'data jackets' to enable analysis of what kind of data exist, where they are located, and what kind of information they hold, even if the contents of data cannot be made publicly available. The book presents the concept of a marketplace for data in the case of data-interactive innovations. It introduces the marketplace as a platform for value-based exchange of data and - based on the idea of the innovators' marketplace - explains how data jackets can be utilized independently from the actual contents of the data. Specific chapters deepen the understanding of variables, constraints and intentions as constituent parts of data jackets, and the extension to variable quest, a process towards the design of data. A number of case studies showcases how the methods and processes presented can be employed in real-life contexts. Finally the authors present some extensions of the concept for web-based IMDJ and connections to business information system and an outlook.