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Sustainable Logistics and Strategic Transportation Planning

The tactical organization of resources is a vital component to any industry in modern society. Effectively managing the flow of materials through various networks ensures that the requirements of customers are met. Sustainable Logistics and Strategic Transportation Planning is a pivotal reference source for the latest research on the management of logistics through the lens of sustainability, as well as for emerging procedures that are particularly critical to the transportation sector. Highlighting international perspectives, conceptual frameworks, and targeted investigations, this book is ideally designed for policy makers, professionals, researchers, and upper-level students interested in logistics and transport systems.

Like discussed in the theoretical foundation, logistics management covers a more
operational view compared to SCM. Therefore, SCM-oriented measures are then
removed or adjusted to a logistics management view. Here, the approach of
Bhagwat & Sharma (2007) is used who developed further the idea of
Gunasekaran et al. (2001) by using the original four BSC perspectives introduced
by Kaplan & Norton (1996). The Customer perspective incorporates performance
indicators ...

Knowledge discovery and data mining

challenges and realities

"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.

xii Preface As data mining evolves into an exciting research area spanning
multiple disciplines such as machine learning, artificial intelligence,
bioinformatics, medicine and business intelligence, the need to apply data
mining techniques to ...

Research and Trends in Data Mining Technologies and Applications

Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.

This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges.

Web Data Management Practices

Emerging Techniques and Technologies

"This book provides an understanding of major issues, current practices and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends. The most important aspects are discussed: Web data mining, content management on the Web, Web applications and Web services"--Provided by publisher.

"This book provides an understanding of major issues, current practices and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends.

Data Mining Applications for Empowering Knowledge Societies

Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

Biological Data Mining in Protein Interaction Networks

"The goal of this book is to disseminate research results and best practices from cross-disciplinary researchers and practitioners interested in, and working on bioinformatics, data mining, and proteomics"--Provided by publisher.

In this tutorial chapter, the author reviews basics about frequent pattern mining
algorithms, including itemset mining, association rule mining, and graph mining.
These algorithms can find frequently appearing substructures in discrete data.

Data Mining in Public and Private Sectors: Organizational and Government Applications

Organizational and Government Applications

The need for both organizations and government agencies to generate, collect, and utilize data in public and private sector activities is rapidly increasing, placing importance on the growth of data mining applications and tools. Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management. This innovative publication provides relevant theoretical frameworks and the latest empirical research findings useful to governmental agencies, practicing managers, and academicians.

Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management.

Organizational Data Mining

Leveraging Enterprise Data Resources for Optimal Performance

Mountains of business data are piling up in organizations every day. These organizations collect data from multiple sources, both internal and external. These sources include legacy systems, customer relationship management and enterprise resource planning applications, online and e-commerce systems, government organizations and business suppliers and partners. A recent study from the University of California at Berkeley found the amount of data organizations collect and store in enterprise databases doubles every year, and slightly more than half of this data will consist of "reference information," which is the kind of information strategic business applications and decision support systems demand (Kestelyn, 2002). Terabyte-sized (1,000 megabytes) databases are commonplace in organizations today, and this enormous growth will make petabyte-sized databases (1,000 terabytes) a reality within the next few years (Whiting, 2002). By 2004 the Gartner Group estimates worldwide data volumes will be 30 times those of 1999, which translates into more data having been produced in the last 30 years than during the previous 5,000 (Wurman, 1989).

Data mining has quickly emerged as a tool that can allow organizations to exploit
their information assets. In this chapter, we suggest how this tool can be used to
support strategic decision-making. Starting with an interpretive perspective of ...

Managing Data Mining Technologies in Organizations

Techniques and Applications

Portals present unique strategic challenges in the academic environment. Their conceptualization and design requires the input of campus constituents who seldom interact and whose interests are often opposite. The implementation of a portal requires a coordination of applications and databases controlled by different campus units at a level that may never before have been attempted at the institution. Building a portal is as much about constructing intra-campus bridges as it is about user interfaces and content. Designing Portals: Opportunities and Challenges discusses the current status of portals in higher education by providing insight into the role portals play in an institution's business and educational strategy, by taking the reader through the processes of conceptualization, design, and implementation of the portals (in different stages of development) at major universities and by offering insight from three producers of portal software systems in use at institutions of higher learning and elsewhere.

Chapter. 5. A. Proposed. Process. for. Performing. Data. Mining. Projects. KarimK.
Hirji IBM Canada Ltd., Canada ... The enthusiasm surrounding data mining at
large continues to grow; however, atthe same time, there are claims that data ...

Data Mining

Opportunities and Challenges

Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

This chapter presents an approach to mining free-text documents for structure
that is qualitative in nature. It complements the statistical and machine-learning
approaches, insomuch as the structural organization of information in documents
is ...