Knowledge Warehouse for Decision Support in Critical Business Processes: Conceptual Modeling and Requirements Elicitation

2010 ◽  
pp. 131-148 ◽  
Author(s):  
Meira Levy ◽  
Nava Pliskin ◽  
Gilad Ravid
Keyword(s):  
Decision Support ◽  
Business Processes ◽  
Conceptual Modeling ◽  
Requirements Elicitation

NBIC-TECHNOLOGIES FOR A DIGITAL ECONOMY

2021 ◽  
Author(s):  
О.Н. МАСЛОВ
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Simulation Modeling ◽  
Information Technologies ◽  
Business Processes ◽  
Digital Economy ◽  
Biological Information ◽  
Role Of Information ◽  
New Formation

Дается обоснование необходимости ускоренного внедрения NBIC-технологий (нанотехнологии, информацион -ные, биологические и когнитивные технологии) в отечественное производство на стадии его перехода к цифровой экономике. Рассматривается проблема формирования системы генерации и реализации инновационных знаний; показана ключевая роль информационных технологий (реинжиниринг бизнес-процессов, имитационное моделирование, системы поддержки реше -ний и др.). Отмечена важность подготовки кадров новой формации, способных использовать достижения NBIC-технологий в интересах современного производства. The paper discusses the need for accelerated implementation of NBIC-technologies (according to the first letters of their names: nanotechnology, biological, information, and cognitive technologies) in domestic production at the stage of its transition to the digital economy. The problem of forming a system for generating and implementing innovative knowledge is considered. The key role of information technologies (business processes reengineering, simulation modeling, decision support systems, etc.) in its solution is shown. The importance of training personnel of a new formation, capable of using the achievements of NBIC-technologies in the interests of modern production, is noted.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Databased Decision Support for the Design of Business Processes in Manufacturing Companies

2021 ◽  
pp. 565-573
Author(s):  
M. Schopen ◽  
L. Geesmann ◽  
S. Schmitz ◽  
A. Gützlaff ◽  
G. Schuh
Keyword(s):  
Decision Support ◽  
Business Processes ◽  
Manufacturing Companies

Design of a Decision Support System for a Home Telehealth Application

2013 ◽  
Vol 4 (3) ◽  
pp. 68-79 ◽  
Author(s):  
Mas S. Mohktar ◽  
Kezhang Lin ◽  
Stephen J. Redmond ◽  
Jim Basilakis ◽  
Nigel H. Lovell
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Open Source Software ◽  
Business Processes ◽  
Rule Engine ◽  
Clinical Support ◽  
Tier System ◽  
Home Telehealth ◽  
Data Layer

A decision support system (DSS) that has been designed to manage patients using a home telehealth system is presented. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs business processes and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.

Modeling and Specification of Collaborative Business Processes with a MDA Approach and a UML Profile

2010 ◽  
pp. 238-268
Author(s):  
Pablo David Villarreal ◽  
Enrique Salomone ◽  
Omar Chiotti
Keyword(s):  
Business Processes ◽  
Conceptual Modeling ◽  
Automatic Generation ◽  
Uml Profile ◽  
Analysis And Design ◽  
Model Driven ◽  
Collaborative Processes ◽  
Interaction Protocols ◽  
Business To Business

This chapter describes the application of MDA (model driven architecture) and UML for the modeling and specification of collaborative business processes, with the purpose of enabling enterprises to establish business-to-business collaborations. The proposed MDA approach provides the components and techniques required for the development of collaborative processes from their conceptual modeling to the specifications of these processes and the partners’ interfaces in a B2B standard. As part of this MDA approach, a UML profile is provided that extends the semantics of UML2 to support the analysis and design of collaborative processes. This UML profile is based on the use of interaction protocols to model collaborative processes. The application of this UML profile in a case study is presented. Also, an overview is provided about the automatic generation of B2B specifications from conceptual models of collaborative processes. In particular, the generation of B2B specifications based on ebXML is described.

Sign in / Sign up

Export Citation Format

Share Document