scholarly journals Security of the Use of Cognitive Information Technologies of Decision-Making

2021 ◽  
pp. 112-131
Author(s):  
Nataliia Gennadevna Mironova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Technologies ◽  
Business Processes ◽  
Predictive Analytics ◽  
Unmanned Vehicles ◽  
Cognitive Information ◽  
Customer Profiling ◽  
Smart Technologies ◽  
Automation Tools

The article considers the intelligent automation of decision-making and management procedures that is being implemented in many areas of socio-economic practice, including financial and credit business processes, in trade and e-commerce (customer profiling, marketing micro-targeting), telecommunications, industry (technological control, robotics, neurocontrol, strategic planning and forecasting), intelligent automation also came to business management, to public administration. It is claimed that automation of personnel management is expanding (monitoring compliance with requirements, profiling and assessing KPIs, predicting conflicts and violations), unmanned vehicles and other neural network automation are used in medicine, the transport industry and agriculture; smart technologies come to education (in Moscow, a system of predictive analytics of the digital footprint of students is being tested to optimize and target educational services, help orientate in the future profession). The use of cognitive technologies in the creation of expert, advisory systems, decision support systems provides not only convenience and savings in time and effort, but gives rise to a variety of organizational, economic, ethical, social problems, giving rise to new risks. This study provides an overview of intelligent technologies that are used in social management, threats associated with the practical use of intelligent automation tools and decision support, ways and measures to reduce some of the risks associated with these threats.

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.

Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Serhat Simsek ◽  
Abdullah Albizri ◽  
Marina Johnson ◽  
Tyler Custis ◽  
Stephan Weikert
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Organizational Performance ◽  
Predictive Analytics ◽  
Design Science ◽  
Decision Support Tool ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Content Type ◽  
Support Tool

PurposePredictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.Design/methodology/approachThis study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.FindingsThere are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.Originality/valueThis paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

scholarly journals Decision and decision makers

2010 ◽  
Author(s):  
Anuta Porutiu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Policy Makers ◽  
Systemic Analysis ◽  
New Information ◽  
Enterprise Success

In the current economic context, decision making requires complex and multiple actions on the part of the policy makers, who are more challenged than in previous situations, due to the crisis that we are facing. Decision problems cannot be solved by focusing on manager’s own experience or intuition, but require constant adaptation of the methods used effectively in the past to new challenges. Thus, a systemic analysis and modeling of arising issues is required, resulting in the stringent use of Decision Support Systems (DSS), as a necessity in a competitive environment. DSS optimize the situation by getting a timely decision because the decision making process must acquire, process and interpret an even larger amount of data in the shortest possible time. A solution for this purpose is the artificial intelligence systems, in this case Decision Support Systems (DSS), used in a wider area due to expansion of all the new information technologies in decisionmaking processes. These substantial cyber innovations have led to a radical shift in the relationship between enterprise success and quality of decisions made by managers.

Fundamentals of designing intelligent decision support systems in nuclear power engineering

2017 ◽  
Author(s):  
Башлыков ◽  
Aleksandr Bashlykov ◽  
Еремеев ◽  
Aleksandr Eremeev
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Nuclear Power ◽  
Information Technologies ◽  
Power Plants ◽  
Support Systems ◽  
Applied Mathematics ◽  
Problem Area ◽  
Emergency Situations

The textbook is devoted to actual problems of using the achievements of modern information technologies, including methods of artificial intelligence, in decision-making in emergency situations at environmentally hazardous facilities, typical examples of which are nuclear power plants. The considered problem area of decision-making in emergency situations is a good example for showing the relevance of importance and complexity of the problem to the applied software and mathematical tools such as intelligent (expert) decision support systems for real-time. The book can be recommended as a textbook for students studying in the areas "Nuclear stations: design, operation and engineering", "Applied mathematics and informatics," Computer science and computer technology", "Automation of technological processes and productions ", as well as for students of other directions, post-graduate students, scientific and engineering staff engaged in the design of modern highly efficient decision support systems for managing complex technical (technological) objects and systems.

Situated Service-Oriented Modeling

2013 ◽  
pp. 54-75
Author(s):  
Raafat George Saadé ◽  
Rustam Vahidov
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Cost Effectiveness ◽  
Business Processes ◽  
User Requirements ◽  
Service Oriented ◽  
E Learning ◽  
Ease Of Access ◽  
Access To Data

The emergence of e-services benefited the stakeholders due to ease of access to data, information and knowledge sources. Service-based applications have evolved into flexible and adaptable systems capable of coping with changes in user requirements and business processes. The shift from monolithic application silos towards service-oriented approaches is evident in the literature today. The benefits of service-oriented approaches include cost effectiveness, improved inter-operability, reusability, and flexibility. The benefits are not enjoyed without the threat of cognitively overloading managers in their decision making activities. Tools for effective management of information are necessary. Effective and efficient service-oriented applications need to operate within their situational boundaries. As such, decision support type tools require tight integration with the service-based approach. This study proposes an integrated Situated Service-Oriented Model and demonstrates its value via a case study of an e-learning service-based application used over a period of 15 months. Two designs were used; component-based and service-oriented. The significance of this study is in the tangible value of the model proposed and demonstrated by the e-learning case study.

Web Analytics

2016 ◽  
pp. 295-314
Author(s):  
Himani Singal ◽  
Shruti Kohli
Keyword(s):  
Decision Making ◽  
Business Performance ◽  
Business Processes ◽  
Predictive Analytics ◽  
Marketing Effort ◽  
Business Analytics ◽  
Ample Evidence ◽  
Analytical Skills ◽  
Football Match ◽  
Very High

There is a remarkable association between an organization's analytics intricacy and its competitive enactment. The biggest problem to adopting analytics is the lack of knowledge of using it to improve business performance. A website is believed and considered as ‘face' of the company. In present era, there are more than 200 million people who buy goods online across the globe. Business Analytics helps companies to find the most profitable customer and allows them to justify their marketing effort, especially when the competition is very high. Predictive analytics helps organizations to predict churn, default in loan payment, brand switch, insurance loss and even the outcome in a football match. There is ample evidence from the corporate world that the ability to make better decisions (by management executives) improves with analytical skills. This chapter will provide an in-depth knowledge of business analytic techniques and their applications in improving business processes and decision-making.

Decision-Making Support for the Correctness of Input Data at Runtime in Business Processes

2014 ◽  
Vol 23 (04) ◽  
pp. 1450003 ◽  
Author(s):  
María Teresa Gómez-López ◽  
Rafael M. Gasca ◽  
José Miguel Pérez-Álvarez
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Business Process ◽  
Process Model ◽  
Input Data ◽  
Business Processes ◽  
Business Process Model ◽  
Programming Paradigm ◽  
Business Process Execution ◽  
Data Constraints

In a business process, the information that flows between the activities can be introduced by those users who interact with the process. This introduced information could be incorrect due to a lack of knowledge or a mistake. For this reason and to make the business process execution consistent, we propose a Decision Support System (DSS) to inform the user about the possible and correct values that the input data can take. The DSS takes into account the business process model and the policy of the company. The policy concerning the input data and dataflow that the company manages can be represented by constraints (called Business Data Constraints (BDCs)). In order to ascertain all the possible values of the input data that permit the execution of the process following the defined goals, the DSS analyzes the business process model and the BDC, using the constraint programming paradigm.

scholarly journals An Object-Oriented Frameworks-based Architecture for Decision Support Systems

1998 ◽  
Vol 1 (1) ◽  
Author(s):  
Karin Becker ◽  
François Bodart
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Rapid Development ◽  
Object Oriented ◽  
Coarse Grained ◽  
Reusable Components

Reusability is considered to be the key for achieving productivity and quality in software, and much has been claimed about the particular contributions of the object-oriented paradigm towards the achievement of these goals. Object-oriented frameworks are coarse-grained reuse units, composed of a set of classes specifically designed to be refined and used as a group. In this paper, we discuss the nature of frameworks necessary to build a particular type of systems, namely Decision Support Systems (DSS), and their organization in a generic OO DSS multi-layer architecture. DSS are systems intended to improve the effectiveness of decision making, but information technologies can only have a major impact on decision making if techniques allowing the easy and rapid development of DSS are available. Much benefit is expected in terms of easiness and rapidity of development by constructing DSS from domain-oriented reusable components, as well as in terms of quality of DSS in this way developed. 

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.

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