On Data Mining and Knowledge

2010 ◽  
pp. 162-183
Author(s):  
Oliver Krone
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Decision Support ◽  
Information Systems ◽  
Organizational Studies ◽  
Enterprise Resources Planning ◽  
Key Characteristics ◽  
Resources Planning ◽  
Knowledge Claims ◽  
Logical Perspective

Understanding data mining (DM) as part of Information Systems (IS) this contribution investigates the question how this subordination is reasoned in a technological and business logical perspective. For this purpose general characteristics of Enterprise Resources Planning Applications (ERP) and Management Information Systems (MIS; including here Decision Support and Expert Systems) are presented. Based on this evaluation it is examined how knowledge and DM are becoming interdependent for Knowledge Management (KM) in organizations. Knowledge is defined along the Penrose’an dichotomy of information and knowledge in the context of resources and services. Validity of knowledge is analyzed from a methodological (quantitative versus qualitative methods) perspective, probing what key characteristics of both method strands are, and how those fit into the discipline of Organizational Studies. Unveiling a relationship between security and information in Penrose, an alternative account of security originating in Foucault is presented. In this security and knowledge become means for standardization of live in order to allow for continuation of an abstracted, socially generated object. Combining arguments about validity of knowledge claims with that of security, DM based knowledge and security are identified as means abstracting from a human core and attempting constraining variability. Against this background researchers and users of DM based knowledge are asked for awareness of the constructed character of IS, and how much of this constructed character is contained in DM based knowledge.

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.

Big data and knowledge management: a case of déjà vu or back to the future?

2017 ◽  
Vol 21 (1) ◽  
pp. 113-131 ◽  
Author(s):  
Xuemei Tian
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Information Systems ◽  
Theory And Practice ◽  
Organizational Studies ◽  
Content Type ◽  
Online Databases ◽  
Management Perspective ◽  
Big Data Technologies ◽  
Conceptual Study

Purpose Big data clearly represent an important advance in information systems theory, but to describe it as “revolutionary” is premature. Similar technological breakthroughs, from online databases to ERP, were clearly modulated by advances in the organizational domain, including matters of structure, strategy and culture and arguably big data will be similar. The purpose of this paper is to encourage discussion of the wider implications of big data for the theory and practice of knowledge management. Design/methodology/approach This is a conceptual study based on critical analysis of the relevant literatures including those of organizational studies and management, big data and knowledge management. Findings The literature of big data emphasizes the application of algorithms to pattern analysis and prediction, resulting in data-driven decision-making, with data being the creator of value in organizations and societies. This would appear to render obsolete previous depictions of the “data-information-knowledge” relationship and, in effect, spell the end of knowledge management. However, big data literature largely ignores the organizational dimension and, significantly, the importance of frameworks, strategies and cultures for big data. As all of these are present in the literature of knowledge management, it would seem that big data have a long way to go to catch up and qualify even as a sub-discipline. Indeed, on the evidence, big data may well have a future as a contributor to and/or an element of knowledge management. Even for this to happen, however, major advances are required across the spectrum of big data technologies. Research limitations/implications This is a position paper written as the precursor for an empirical study. Originality/value The paper offers a critical literature-based and knowledge management perspective on big data while pointing out the common thread that runs through decades of advances in information systems technologies.

scholarly journals Evaluasi Implementasi Enterprise Resources Planning pada Perusahaan Manufaktur dengan Model DeLone dan McLean

2020 ◽  
Vol 4 (2) ◽  
pp. 21
Author(s):  
Albert Gozali ◽  
Johanes Supranto
Keyword(s):  
Information Systems ◽  
User Satisfaction ◽  
Critical Ratio ◽  
Manufacturing Companies ◽  
Erp Implementation ◽  
P Values ◽  
Enterprise Resources Planning ◽  
Use Of Resources ◽  
Success Model ◽  
Resources Planning

This research is intended to examine factors that influence the success of ERP implementation in winning competitive advantage. ERP is a device system that enables companies to manage efficiently and effectively the use of resources and provide integrated solutions for the company's information processing needs. DeLone & McLane success model was actively used since it was first introduced in 1992 and Measurement with the DeLone & McLean model is one of the most developed models to test the information success model. The sample of this study is the users of companies that use SAP as one of their information systems, several 200 people from various manufacturing companies to evaluate information systems based on user satisfaction. The results of the analysis show that all hypotheses show significant results obtained from positive values on the critical ratio (cr) and also the value of p values smaller than 5% (significant effect on α < 5%).

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

Strategic Utilization of Data Mining

2005 ◽  
pp. 2638-2642
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Strategic Utilization of Data Mining

2008 ◽  
pp. 1689-1695
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Data Mining in Animal Husbandry Decision Support System

2014 ◽  
Vol 926-930 ◽  
pp. 2678-2681
Author(s):  
Ai De Jiang ◽  
Hong Yu Duan ◽  
Tai Yu Liu ◽  
Shu Yan Wu
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support System ◽  
Support System ◽  
Management Information Systems ◽  
Animal Husbandry ◽  
Management Information ◽  
Analysis And Design ◽  
Prototype Development

This paper based on the actual needs of the current animal husbandry information management, with reference to the actual development of the situation and the various existing information decision support system for animal husbandry management, animal husbandry production in the existing management information systems, management information systems and other prairie feed business management information system as the basic data sources in order to achieve integrated management of farm animal husbandry information and data mining objectives by using data mining techniques to construct a set of B / S structure of decision support systems. Considering some of today's more popular data mining methods, combined with the actual situation of animal husbandry decision support system to determine the practicality of the system using a prototype development methodology and life cycle the Combination. This development based on a detailed design plan, dividing each module to determine the functional modules. Each functional module with independent design principles, gives details of systems analysis and design features of each module, developed based on data mining animal husbandry decision support system.

scholarly journals Blueprint of Enterprise Architecture on Project Management Information Systems using TOGAF in ERP Provider Company

2020 ◽  
Vol 8 (6) ◽  
pp. 3852-3857
Keyword(s):  
Project Management ◽  
Information Systems ◽  
Enterprise Architecture ◽  
Management Information Systems ◽  
Management Information ◽  
Enterprise Information ◽  
Enterprise Resources Planning ◽  
Information System Architecture ◽  
Management Project ◽  
Resources Planning

XYZ company is a leading Enterprise Resources Planning (ERP) solution provider company from Singapore that develops innovative software to help businesses automate their daily operations to achieve optimal levels and productivity. To realize its vision of becoming a leading Enterprise Resources Planning (ERP) solution provider in South East Asia, a solution to the current problems is needed, namely that the project management is not optimal so that some projects are not completed according to the timeline due to poor communication. This study aims to build an Enterprise Architecture using The Open Group Architecture Forum framework (TOGAF) that supports the design, planning, implementation and governance of an enterprise information technology architecture, so that this framework will help the company to improve the quality of project management information systems. This study implements 4 phases of TOGAF, namely Architecture Vision, Business Architecture, Information System Architecture, and Technology Architecture to build an application management project called Internal Po. In this study, TOGAF framework will help the company's business to achieve its vision

Data Mining and Decision Support for Business and Science

2011 ◽  
pp. 1798-1805
Author(s):  
Auroop R. Ganguly ◽  
Amar Gupta ◽  
Shiraj Khan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Geographical Information Systems ◽  
Information Technologies ◽  
Scientific Data ◽  
Knowledge Discovery In Databases ◽  
Relational Systems ◽  
Available Information

Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise-scale data warehouses every day. Acquisition, storage, and management of business information are commonplace and often automated. Recent advances in remote or other sensor technologies have led to the development of scientific data repositories. Database technologies, ranging from relational systems to extensions like spatial, temporal, time series, text, or media, as well as specialized tools like geographical information systems (GIS) or online analytical processing (OLAP), have transformed the design of enterprise-scale business or large scientific applications. The question increasingly faced by the scientific or business decision-maker is not how one can get more information or design better information systems but what to make of the information and systems already in place. The challenge is to be able to utilize the available information, to gain a better understanding of the past, and to predict or influence the future through better decision making. Researchers in data mining technologies (DMT) and decision support systems (DSS) are responding to this challenge. Broadly defined, data mining (DM) relies on scalable statistics, artificial intelligence, machine learning, or knowledge discovery in databases (KDD). DSS utilize available information and DMT to provide a decision-making tool usually relying on human-computer interaction. Together, DMT and DSS represent the spectrum of analytical information technologies (AIT) and provide a unifying platform for an optimal combination of data dictated and human-driven analytics.

Data Mining and Decision Support for Business and Science

2011 ◽  
pp. 233-238 ◽  
Author(s):  
Auroop R. Ganguly ◽  
Amar Gupta ◽  
Shiraj Khan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Geographical Information Systems ◽  
Information Technologies ◽  
Scientific Data ◽  
Knowledge Discovery In Databases ◽  
Relational Systems ◽  
Available Information

Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise-scale data warehouses every day. Acquisition, storage, and management of business information are commonplace and often automated. Recent advances in remote or other sensor technologies have led to the development of scientific data repositories. Database technologies, ranging from relational systems to extensions like spatial, temporal, time series, text, or media, as well as specialized tools like geographical information systems (GIS) or online analytical processing (OLAP), have transformed the design of enterprise-scale business or large scientific applications. The question increasingly faced by the scientific or business decision-maker is not how one can get more information or design better information systems but what to make of the information and systems already in place. The challenge is to be able to utilize the available information, to gain a better understanding of the past, and to predict or influence the future through better decision making. Researchers in data mining technologies (DMT) and decision support systems (DSS) are responding to this challenge. Broadly defined, data mining (DM) relies on scalable statistics, artificial intelligence, machine learning, or knowledge discovery in databases (KDD). DSS utilize available information and DMT to provide a decision-making tool usually relying on human-computer interaction. Together, DMT and DSS represent the spectrum of analytical information technologies (AIT) and provide a unifying platform for an optimal combination of data dictated and human-driven analytics.

Sign in / Sign up

Export Citation Format

Share Document