Ebook: Mining for Strategic Competitive Intelligence: Foundations and Applications

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.

---

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.

---

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.

Content:
Front Matter....Pages 1-14
Introduction....Pages 1-4
Front Matter....Pages 5-5
Research Foundations....Pages 7-49
Competitive Intelligence Capturing Systems....Pages 51-62
Research Topics and Applications....Pages 63-93
Conclusion....Pages 95-97
Back Matter....Pages 99-105
Front Matter....Pages 107-107
Towards Automated Reputation and Brand Monitoring on the Web....Pages 109-119
Mining and Exploring Customer Feedback Using Language Models and Treemaps....Pages 121-134
Content Extraction from News Pages Using Particle Swarm Optimization....Pages 135-149
Distilling Informative Content from HTML News Pages Using Machine Learning Classifiers....Pages 151-166
Automatic Computation of Semantic Proximity Using Taxonomic Knowledge....Pages 167-187
Leveraging Sources of Collective Wisdom on the Web for Discovering Technology Synergies....Pages 189-206

---

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.

Content:
Front Matter....Pages 1-14
Introduction....Pages 1-4
Front Matter....Pages 5-5
Research Foundations....Pages 7-49
Competitive Intelligence Capturing Systems....Pages 51-62
Research Topics and Applications....Pages 63-93
Conclusion....Pages 95-97
Back Matter....Pages 99-105
Front Matter....Pages 107-107
Towards Automated Reputation and Brand Monitoring on the Web....Pages 109-119
Mining and Exploring Customer Feedback Using Language Models and Treemaps....Pages 121-134
Content Extraction from News Pages Using Particle Swarm Optimization....Pages 135-149
Distilling Informative Content from HTML News Pages Using Machine Learning Classifiers....Pages 151-166
Automatic Computation of Semantic Proximity Using Taxonomic Knowledge....Pages 167-187
Leveraging Sources of Collective Wisdom on the Web for Discovering Technology Synergies....Pages 189-206
....