Pattern Management

Knowledge intensive applications rely on the usage of knowledge artifacts, called patterns, to represent in a compact and semantically rich way huge quantities of heterogeneous raw data. Due to pattern characteristics of patterns, specific systems are required for pattern management in order to model, store, retrieve and manipulate patterns in an efficient and effective way. Several theoretical and industrial approaches (relying on standard proposals, metadata management and business intelligence solutions) have already been proposed for pattern management. However, no critical comparison of the existing approaches has been proposed so far. The aim of this chapter is to provide such a comparison. In particular, specific issues concerning pattern management systems, pattern models and pattern languages are discussed. Several parameters are also identified that will be used in evaluating the effectiveness of theoretical and industrial proposals. The chapter is concluded with a discussion concerning additional issues in the context of pattern management.

AUTOMATION OF MAKING REPORTS IN THE AREA OF MANAGEMENT SYSTEMS' CERTIFICATION VIA ORACLE BUSINESS INTELLIGENCE

Theoretical & Applied Science ◽

10.15863/tas.2017.03.47.22 ◽

2017 ◽

Vol 47 (03) ◽

pp. 121-127

Author(s):

Oleg Yurievich Sabinin ◽

◽

Ekaterina Sergeevna Sheikina ◽

Keyword(s):

Business Intelligence ◽

Management Systems

Knowledge Management Systems for Knowledge-Intensive Processes: Design Approach and an Illustrative Example

2010 43rd Hawaii International Conference on System Sciences ◽

10.1109/hicss.2010.248 ◽

2010 ◽

Cited By ~ 10

Author(s):

Surendra Sarnikar ◽

Amit Deokar

Keyword(s):

Knowledge Management ◽

Design Approach ◽

Management Systems ◽

Stages of Information Systems in E-Government for Knowledge Management

Applied Technology Integration in Governmental Organizations ◽

10.4018/978-1-60960-162-1.ch017 ◽

2011 ◽

pp. 270-280

Author(s):

Petter Gottschalk

Keyword(s):

Knowledge Management ◽

Information Systems ◽

Police Officers ◽

Growth Models ◽

Knowledge Work ◽

Management Systems ◽

Financial Crime ◽

Knowledge Intensive ◽

Direction Information

A stage model for knowledge management systems in policing financial crime is developed in this chapter. Stages of growth models enable identification of organizational maturity and direction. Information technology to support knowledge work of police officers is improving. For example, new information systems supporting police investigations are evolving. Police investigation is an information-rich and knowledge-intensive practice. Its success depends on turning information into evidence. This chapter presents an organizing framework for knowledge management systems in policing financial crime. Future case studies will empirically have to illustrate and validate the stage hypothesis developed in this paper.

Toward Developing Human Resource Management Systems for Knowledge-Intensive Teamwork

Research in Personnel and Human Resources Management ◽

10.1016/s0742-7301(06)25002-3 ◽

2006 ◽

pp. 27-70 ◽

Cited By ~ 77

Author(s):

Susan E. Jackson ◽

Chih-Hsun Chuang ◽

Erika E. Harden ◽

Yuan Jiang

Keyword(s):

Resource Management ◽

Human Resource Management ◽

Human Resource ◽

Management Systems ◽

Data Science Techniques in Knowledge-Intensive Business Processes

International Journal of Data Analytics ◽

10.4018/ijda.2020010104 ◽

2020 ◽

Vol 1 (1) ◽

pp. 52-67

Author(s):

Matthias Lederer ◽

Joanna Riedl

Keyword(s):

Fuzzy Logic ◽

Business Intelligence ◽

Data Science ◽

Business Processes ◽

Due Diligence ◽

Fuzzy Logic Systems ◽

Investment Bank ◽

Front Office ◽

Management Techniques ◽

The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

Data Mining and Business Intelligence

Business Intelligence in the Digital Economy ◽

10.4018/978-1-59140-206-0.ch009 ◽

2011 ◽

pp. 141-190 ◽

Cited By ~ 2

Author(s):

Jeffrey Hsu

Keyword(s):

Data Mining ◽

Business Intelligence ◽

Business Environment ◽

Full Potential ◽

Raw Data ◽

Useful Knowledge ◽

Business Customers

Most businesses generate, are surrounded by, and are even overwhelmed by data — much of it never used to its full potential for gaining insights into one’s own business, customers, competition, and overall business environment. By using a technique known as data mining, it is possible to extract critical and useful patterns, associations, relationships, and, ultimately, useful knowledge from the raw data available to businesses. This chapter explores data mining and its benefits and capabilities as a key tool for obtaining vital business intelligence information. The chapter includes an overview of data mining, followed by its evolution, methods, technologies, applications, and future.

Development of Web-based knowledge management systems

Human Systems Management ◽

10.3233/hsm-2002-21301 ◽

2002 ◽

Vol 21 (3) ◽

pp. 153-158

Author(s):

David C. Chou ◽

Binshan Lin

Keyword(s):

Information Technology ◽

Knowledge Management ◽

Technology Integration ◽

Business Intelligence ◽

Knowledge Management System ◽

Management Systems ◽

Web Based ◽

Complex Process ◽

Operation And Management

Knowledge management is a complex process that collects, stores, and distributes business intelligence for corporate operation and management. This paper discusses the implication of knowledge management, its process, bottlenecks, information technology integration, and development of a Web-based knowledge management system.

Applied Metadata Management Systems for Post-Production

9th Conference and Exhibition of the SMPTE Australia Section ◽

10.5594/m001212 ◽

1999 ◽

Author(s):

Garry Claridge

Keyword(s):

Management Systems ◽

Metadata Management ◽

Post Production

Does Knowledge Management Systems Moderate Turnover Effects?

International Journal of Knowledge Management ◽

10.4018/ijkm.2013100101 ◽

2013 ◽

Vol 9 (4) ◽

pp. 1-16

Author(s):

Davi Nakano ◽

Renato de Oliveira Moraes ◽

Ana Paula Pereira de Moraes Ress

Keyword(s):

Knowledge Management ◽

Organizational Performance ◽

Time Lag ◽

Management Systems ◽

Negative Effects ◽

Knowledge Assets ◽

Software Development Teams ◽

Knowledge Intensive ◽

And Performance

Knowledge assets are key to innovative capability, but are perishable and may decay over time. Knowledge Management Systems (KMS) can prevent knowledge decay and maintain and enhance performance and innovation. This paper investigates if the use of a KMS mitigates employee turnover negative effects on organizational performance. Data on turnover and project performance from two software development teams from the same corporation were collected and compared. One team adopted and uses a KMS to support development, while the other did not implement a KMS. Paired t-tests were performed and confirmed that KMS usage moderate turnover impact on organizational performance. There is also evidence that, when KMS are not used, turnover and performance are correlated with a time lag. From a practical stance, results indicate that knowledge intensive firms can avoid knowledge assets loss by implementing a KMS.