Modeling the Process of Chance Discovery

Ohsawa, Yukio

doi:10.1007/978-3-662-06230-2_1

Cited by 51 publications

(24 citation statements)

References 10 publications

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“…However, using only data mining techniques is not enough for chance discovery, because its aim is different from prediction based on general rules. That is, the word 'chance' means information about an event or a situation that is significant for making decisions [3] , and such event or situation is usually a rare one. As the saying "a chance favors a prepared mind" indicates, chance discovery is possible if the context information brought by humans meets the information extracted from a data set.…”

Section: Roles Of Computer Systems In Chance Discovery Processmentioning

confidence: 99%

“…The annotated data can be extracted and used for generating a new graph, which focuses on the specific part of the original data set. As the chance discovery process is generally regarded as the double-helical model [3], which involves the repeated interaction between humans and computer systems, the graph generated from specific part of the data set will help users go into the further level of analysis.…”

Section: Smart Data: Scenario To Data Mappingmentioning

confidence: 99%

“…Section 2 discusses the smart data approach for chance discovery process, and the algorithm is proposed in Sect. 3. By the experiment in Sect.…”

Section: Introductionmentioning

confidence: 97%

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Scenario to Data Mapping for Chance Discovery Process

2006

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The mapping method between the graph generated by KeyGraph and the scenario drawn up by a user is proposed for supporting chance discovery process. Although KeyGraph is widely known as one of the effective tools that support the process of chance discovery, further improvement seems to be required, concerning the ambiguity involved in user's interpretation of the graph. The mapping found by the proposed algorithm is used for extracting the data referred to in the scenario and for annotating those in the original data file. The annotated data files are expected to be used for further data analysis as well as for supporting group discussion. Experiments are performed in terms of scenario similarity calculation based on the annotated data set. The results are compared with typical document retrieval method based on Vector space model (VSM), which shows the availability of annotated data in chance discovery process.

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Section: Roles Of Computer Systems In Chance Discovery Processmentioning

confidence: 99%

Section: Smart Data: Scenario To Data Mappingmentioning

confidence: 99%

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Scenario to Data Mapping for Chance Discovery Process

2006

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“…However, it has well known for its fruitful works on chance discovery. Ohsawa and McBurney [4] defined "chance is an event or a situation which can effectively contribute to user's decision making." KeyGraph has shown his powerful capacity on drawing scenarios, and visualizing the relations of events (or states, or components) in the target environment [2].…”

Section: Introductionmentioning

confidence: 99%

Discovering Hidden Blue Ocean Strategy with KeyGraph Machine

Hsu¹,

Lee²,

Chi³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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Researchers usually focused on using unstructured documents, such as news, scientific literatures, World Wide Web pages, business reports, as input documents of KeyGraph. But the unstructured documents might results in unclear keywords and meaningless keywords. Although traditional document preprocessing could dismiss some of the problems, generating complex and unreadable KeyGraph diagrams were not avoidance. In this research, we applied the concept of chance discovery and KeyGraph to discover hidden blue ocean strategy (BOS) for decision makers who not necessary familiar with the concerned domains. A preprocessing strategy, including develop an operational framework of BOS and design a sentence structure for representing BOS, was proposed to assure the necessary keywords be included in the required documents. Seventy-two documents concerning blue ocean firms were collected as sample cases for developing the operational framework. At last, three experiences were designed for confirming the performance of the proposed preprocessing strategy. Experimental results shown that the subjects were not consistently recognized BOS with traditional approach (used unstructured documents as input documents of KeyGraph). In contrast, the subjects could easily find explicit scenarios of BOS, and recognize a few of implicit scenarios appeared on KeyGraph diagrams with the proposed strategy.

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“…Chance discovery, as a field of investigation itself, is devoted to analyze the occurrence of events that might have important impact in their happening area. In this regard, the word "chance" has been defined as the information about an event or situation significant for making decisions, being chance discovery the discovery of a chance, not by chance [1]. This chance has been considered a rare opportunity -or risk -whose significance has been unnoticed, which may lead to an unexpected benefit.…”

Section: Introductionmentioning

confidence: 99%