Creating Superior Knowledge Discovery Solutions

2011 ◽  
pp. 795-799 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Decision Support ◽  
Knowledge Discovery ◽  
High Performance ◽  
New Technologies ◽  
Relevant Information ◽  
Customer Relationship ◽  
Business Strategies ◽  
Competitive Edge

The information age has made information communication technology (ICT) a necessity for conducting business. This in turn has led to the exponential increase in the electronic capture of data and its storage in vast data warehouses. In order to respond quickly to fast changing markets, organizations must maximize these raw data and information resources. Specifically, they need to transform them into germane knowledge to aid superior decision-making (Wickramasinghe & von Lubitz, 2006). To do this effectively not only involves the analysis of the data and information but also requires the use of sophisticated tools to enable such analyses to occur. Knowledge discovery technologies represent a spectrum of new technologies that facilitate the analysis of data to find relationships from the data to finding reasons behind observable patterns (i.e., transform the data into relevant information and germane knowledge). Such new discoveries can have a profound impact on decision making in general and the designing of business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning, and predictive forecasting, these knowledge discovery technologies are becoming competitive necessities for providing a high performance and feature rich intelligent application servers for intelligent enterprises. Knowledge management (KM) tools and technologies are the systems that integrate various legacy systems, databases, ERP systems, and data warehouse to help facilitate an organization’s knowledge discovery process. Integrating all of these with advanced decision support and online real time events enables an organization to understand customers better and devise business strategies accordingly. Creating a competitive edge is the goal of all organizations employing knowledge discovery for decision support (Thorne & Smith, 2000). The following provides a synopsis of the major tools and critical considerations required to enable an organization to successfully effect appropriate knowledge sharing, knowledge distribution, knowledge creation, as well as knowledge capture and codification processes and hence embrace effective knowledge management (KM) techniques and advanced knowledge discovery.

Knowledge Discovery Solutions for Intelligent Enterprises

2005 ◽  
pp. 1747-1751
Author(s):  
Nilmini Wickramasinghe ◽  
Sushil K. Sharma
Keyword(s):  
Knowledge Discovery ◽  
High Performance ◽  
New Technologies ◽  
Customer Relationship ◽  
Business Strategies ◽  
Technology Knowledge ◽  
Demand Planning ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
New Knowledge

The exponential increase in information—primarily due to the electronic capture of data and its storage in vast data warehouses—has created a demand for analyzing the vast amount of data generated by today’s organizations so that enterprises can respond quickly to fast changing markets. These applications not only involve the analysis of the data but also require sophisticated tools for analysis. Knowledge discovery technologies are the new technologies that help to analyze data and find relationships from data to finding reasons behind observable patterns. Such new discoveries can have profound impact on designing business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning and predictive forecasting, the knowledge discovery technologies have become necessities to providing high performance and feature rich intelligent application servers for intelligent enterprises. The new knowledge based economy entirely depends upon information technology, knowledge sharing, as well as intellectual capital and knowledge management.

Design for Additive Manufacturing in the Cloud Platform

2017 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Additive Manufacturing ◽  
Full Advantage ◽  
New Technologies ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Cloud Platform ◽  
Am Processes

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.

Formal Knowledge Engineering Methods for Knowledge Discovery

2002 ◽  
Vol 01 (02) ◽  
pp. 141-154
Author(s):  
Satheesh Ramachandran
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Life Cycle ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Knowledge Engineering ◽  
The Other ◽  
Full Potential ◽  
Formal Knowledge

This paper presents a framework for the integrated use of formal knowledge engineering methods and data mining based knowledge discovery methods. Knowledge is a key enterprise asset, and organizations are adopting both knowledge engineering and knowledge discovery paradigms for better knowledge management and enhanced decision support capability. Although there exists a useful interdependence between these endeavors, not much effort has been focused on using the full potential of one for the other. This paper presents a framework for the integrated use of established formal knowledge engineering methods and knowledge discovery processes with the ultimate intent of better managing the enterprise knowledge life cycle. It provides a brief overview of the knowledge discovery processes, and introduces a class of formal knowledge engineering methods and the perceived role of these methods in supporting the integration between the two worlds of knowledge discovery and knowledge engineering.

scholarly journals Communication and Decision Support Systems

2021 ◽  
Vol 11 (4) ◽  
pp. 397-407
Author(s):  
Luigi Palestini
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Communication Systems ◽  
New Technologies ◽  
Support Systems ◽  
Just In Time ◽  
Strategic Decisions ◽  
Voice Data ◽  
The Impact

In emergencies, assessment and communication activities are particularly important for the support of the top decision-making bodies, to evaluate “just in time” the best actions to be taken. The multiple problems to be solved require specific skills in different areas. Upon the occurrence of a calamity, the authorities must answer questions such as “is a given place safe from the threat (e.g., an oncoming flood)?”, that’s why today knowledge of tools that can support decision-making is increasingly necessary: the so-called Decision Support Systems (DSS), software that allow users to improve situation assessment, helping all those who must make strategic decisions. Hand in hand with the growing interest in DSS there is an increasing use of communication systems based on IT. First responders know that to face an emergency everything must be prepared and planned, also communication. In fact, DSS and voice/data transmission systems are often integrated into a single system, as proposed by the European projects FIRE IN and IN PREP, because managing information is crucial for carrying out rescue activities in the best possible way. This work describes the impact of new technologies on rescue and emergency management in Italy and Europe, highlighting the challenges associated with their use.

Understanding Decision-Making in Data Warehousing and Related Decision Support Systems

2011 ◽  
pp. 228-250
Author(s):  
John D. Wells ◽  
Traci J. Hess
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Financial Services ◽  
Support Systems ◽  
Data Warehousing ◽  
Performance Model ◽  
Customer Relationship ◽  
Decision Performance

Many businesses have made or are making significant investments in data warehouses that reportedly support a myriad of decision support systems (DSS). Due to the newness of data warehousing and related DSS (DW-DSS), the nature of the decision support provided to DW-DSS users and the related impact on decision performance have not been investigated in an applied setting. An explanatory case study was undertaken at a financial services organization that implemented a particular type of DW-DSS, a Customer Relationship Management (CRM) system. The DSS-decision performance model has provided some theoretical guidance for this exploration. The case study results show that the decision-making support provided by these systems is limited and that an extended version of the DSS-decision performance model may better describe the factors that influence individual decision-making performance.

Research of Decision Support System Based on Knowledge Management and Web Technology

2013 ◽  
Vol 321-324 ◽  
pp. 2357-2360
Author(s):  
Zhi Dan Wu
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Web Technology ◽  
Decision Making Process ◽  
New Model ◽  
Model Based ◽  
S Model

This paper gives a new model of decision support system based on knowledge management and web technology. Firstly,we analysis the existing problem of the efficiency and expandability of the current system.Then, we study the knowledge management and the B/S model based on web technology.Based on this, a new model of decision support system is presented in this paper. The system is designed in four layers structure and on three library.Finally, the paper describes the structure of the system and gives the detailed steps of decision-making process.

scholarly journals Decision support system for promotion of Faculty of Business and Economics Mendel University

2012 ◽  
Vol 60 (2) ◽  
pp. 443-448
Author(s):  
Pavel Turčínek ◽  
Arnošt Motyčka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Secondary School ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Discovery In Databases ◽  
Optimum Number ◽  
Product Knowledge ◽  
Study Programs ◽  
Decision Making Processes

Decreasing number of secondary school graduates means that, for college, it becomes more difficult to fulfill guide number of newly admitted students. In order to maintain an optimum number of registered students, the Faculty of Business and Economics decided to support activities which increase the interest of its accredited programs.Potential students should be treated as customers to whom we want to offer a product – knowledge, skills and competencies. Promoting study programs PEF MENDELU is handled by PR department in collaboration with several students.Availability of resources for promotion is limited. It is crucial to deciding how to deal with these sources. By creating a system for monitoring and decision support, we provide all interested collaborators tool to improve decision-making processes.The system itself will be built on the tools of Business Intelligence (BI) that can observe consumer trends, identify customer segments and other important information. The BI emphasizes the use of OLAP technology for data processing. In the collected data about students is hidden a large amount of information that can be obtained using techniques such as knowledge discovery in databases.This article aims to describe the methodology for solving problems and show the application, which result in support of decision-making processes in the propagation PEF MENDELU, which should also lead to the efficiency of spending on this activity.

scholarly journals Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey (Preprint)

2020 ◽  
Author(s):  
Thomas Ploug ◽  
Anna Sundby ◽  
Thomas B Moeslund ◽  
Søren Holm
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Public Policy ◽  
Decision Making ◽  
Decision Support ◽  
Health Care Professionals ◽  
High Performance ◽  
Fractional Factorial ◽  
Final Decision ◽  
System Use

BACKGROUND Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public’s interests in such features of AI. OBJECTIVE This study elicited the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. METHODS We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents’ views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. RESULTS Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents’ trust in health and technology, and respondents’ fears and hopes regarding AI, do not play a significant role in the majority of cases. CONCLUSIONS The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.

scholarly journals Railway supply chain excellence through the mediator role of business intelligence: Knowledge management approach towards information system

2022 ◽  
Vol 10 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Mailasan Jayakrishnan ◽  
Abdul Karim Mohamad ◽  
Mokhtar Mohd Yusof
Keyword(s):  
Decision Making ◽  
Information System ◽  
Knowledge Management ◽  
Supply Chain ◽  
Decision Support ◽  
Support System ◽  
Business Intelligence ◽  
Decision Making Process ◽  
Supply Chain Performance ◽  
Performance Diagnostics

The features of a holistic view in an organization create the data value of the Business Intelligence (BI) and Knowledge Management (KM) in viewing the big picture of organizational performance diagnostics framework. This research focuses on the specific features of railway supply chain performance in viewing the decision-making process and creating better knowledge formation. The intention of the study is to structure supplier performance using BI-KM framework development to determine holistic perspective factors. The outcomes indicate that BI and KM significantly increased the railway supply chain and significantly increased the information system. This BI-KM framework relates the current analytic characteristics in designing the railway supply chain towards information system in determining the strategic theme of the decision-making process of the decision support system together with system features, characteristics of data, the content of the themes, and the effect of the decision-making process and for executive strategic performance diagnostics tool that provides effective strategic decision making in supply chain performance. The quantitative research method uses SmartPLS software version 3.2.8 for empirical analysis through distributing survey questionnaires to 320 railway suppliers in Malaysia. Using a model-driven development framework, to measure the implementation success of the decision support system, the study is conducted in the railway supplier focusing on strategic management that helps to make the decision and facilitate the organizational success.

scholarly journals A development framework for decision support systems in high-performance sport

2020 ◽  
Vol 19 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Xavier Schelling ◽  
Sam Robertson
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
High Performance ◽  
Injury Risk ◽  
Support Systems ◽  
System Development ◽  
Process Efficiency ◽  
Cognitive Limitations ◽  
Development Framework

AbstractDecision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.

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