Towards data-driven decision support for organizational IT security audits

2018 ◽  
Vol 60 (4) ◽  
pp. 207-217 ◽  
Author(s):  
Michael Brunner ◽  
Christian Sillaber ◽  
Lukas Demetz ◽  
Markus Manhart ◽  
Ruth Breu
Keyword(s):  
Decision Support ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
It Security ◽  
Related Data ◽  
Security Controls ◽  
Audit Data ◽  
Data Driven Approach ◽  
Security Audits

Abstract As the IT landscape of organizations increasingly needs to comply with various laws and regulations, organizations manage a plethora of security-related data and have to verify the adequacy and effectiveness of their security controls through internal and external audits. Existing Governance, Risk and Compliance (GRC) approaches provide little support for auditors or are tailored to the needs of auditors and do not fully support required management activities of the auditee. To address this gap and move towards a holistic solution, a data-driven approach is proposed. Following the design science research paradigm, a data-driven approach for audit data management and analytics that addresses organizational needs as well as requirements for audit data analytics was developed. We contribute workflow support and associated data models to support auditing and security decision making processes. The evaluation shows the viability of the proposed IT artifact and its potential to reduce costs and complexity of security management processes and IT security audits. By developing a model and associated decision support workflows for the entire IT security audit lifecycle, we present a solution for both the auditee and the auditor. This is useful to developers of GRC tools, vendors, auditors and organizational decision makers.

scholarly journals Development of a Decision Support System Framework for Cultural Heritage Management

2021 ◽  
Vol 13 (13) ◽  
pp. 7070
Author(s):  
Eleonora Di Di Matteo ◽  
Paolo Roma ◽  
Santo Zafonte ◽  
Umberto Panniello ◽  
Lorenzo Abbate
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Cultural Heritage ◽  
Design Science ◽  
Demand Management ◽  
Science Research ◽  
Ease Of Use ◽  
Heritage Management ◽  
Space Management ◽  
Managerial Implications

Decision support systems (DSSs) have been traditionally identified as useful information technology tools in a variety of fields, including the context of cultural heritage. However, to the best of our knowledge, no prior study has developed a DSS framework that incorporates all the main decision areas simultaneously in the context of cultural heritage. We fill this gap by focusing on design-science research and specifically by developing a DSS framework whose features support all the main decision areas for the sustainable management of cultural assets in a comprehensive manner. The main decision-making areas considered in our study encompass demand management, segmentation and communication, pricing, space management, and services management. For these areas, we select appropriate decision-making supporting techniques and data management solutions. The development of our framework, in the form of a web-based system, results in an architectural solution that is able to satisfy critical requirements such as ease of use and response time. We present an application of the innovative DSS framework to a museum and discuss the main managerial implications and future improvements.

scholarly journals X-ETL: A Data-Driven Approach for Designing Star Schemas

2019 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Nawfal El Moukhi ◽  
Ikram El Azami ◽  
Abdelaaziz Mouloudi ◽  
Abdelali Elmounadi
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Data Driven ◽  
Critical Stage ◽  
Model Driven ◽  
New Methods ◽  
Star Models ◽  
Data Driven Approach ◽  
Data Source ◽  
Warehouse Design

The data warehouse design is currently recognized as the most important and complicated phase in any project of decision support system implementation. Its complexity is primarily due to the proliferation of data source types and the lack of a standardized and well-structured method, hence the increasing interest from researchers who have tried to develop new methods for the automation and standardization of this critical stage of the project. In this paper, the authors present the set of developed methods that follows the data-driven paradigm, and they propose a new data-driven method called X-ETL. This method aims to automating the data warehouse design by generating star models from relational data. This method is mainly based on a set of rules derived from the related works, the Model-Driven Architecture (MDA) and the XML language.

ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION—DATA-DRIVEN APPROACH

2013 ◽  
Vol 44 (2-3) ◽  
pp. 204-221 ◽  
Author(s):  
Krzysztof Brzostowski ◽  
Jarosław Drapała ◽  
Adam Grzech ◽  
Paweł Świątek
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Driven ◽  
Physical Effort ◽  
Plan Generation ◽  
Data Driven Approach

Data driven approach to risk management and decision support for dynamic positioning systems

2020 ◽  
Vol 201 ◽  
pp. 106964 ◽  
Author(s):  
Tarannom Parhizkar ◽  
Sandra Hogenboom ◽  
Jan Erik Vinnem ◽  
Ingrid Bouwer Utne
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Data Driven ◽  
Dynamic Positioning ◽  
Positioning Systems ◽  
Data Driven Approach

scholarly journals Personas Design for Conversational Systems in Education

Informatics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 46 ◽  
Author(s):  
Fatima Ali Amer Jid Almahri ◽  
David Bell ◽  
Mahir Arzoky
Keyword(s):  
Machine Learning ◽  
Student Engagement ◽  
Interaction Analysis ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
Gap Statistic ◽  
Optimal Value ◽  
Persona Development ◽  
Future Work

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) while using a novel conversational system (chatbots). The principal research methodology for this study is design science research (DSR), which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This paper focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, specifically the K-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the K-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of K. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically K-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations. It will cover building SEFMs, building tailored interaction models for these personas and then evaluating them using chatbot technology.

scholarly journals Design science research for decision support systems development: recent publication trends in the premier IS journals

2016 ◽  
Vol 20 ◽  
Author(s):  
Shah J Miah ◽  
Michael McGrath ◽  
Don Kerr
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Science Studies ◽  
Support Systems ◽  
Design Science ◽  
Contemporary Literature ◽  
User Involvement ◽  
Science Research ◽  
Design Science Research ◽  
Journal Ranking

This paper presents a contemporary literature review of design science research (DSR) studies in the domain of decision support systems (DSS) development. The latest studies in the DSS design domain claim that DSR methodologies are the most popular design approach, but many details are still yet to be revealed for supporting this claim. In particular, it is important to thoroughly investigate the trends in either the form or deeper insights in use of DSR in this field. The aim of this study is to analyse the existing DSS design science studies to reveal insights into the use of DSR, so that we can outline research agenda for a special issue, based on findings of analysis. We selected articles (from 2005 to 2014) that were published in seven selected premier IS journals (ranked as A* in the ABDC journal ranking). The selected 57 sample articles are representative of DSS design studies that used DSR in theorising, designing, implementing, and evaluating DSS solutions. We discuss the theoretical positions of DSR for DSS development through six categories: DSS artefacts, DSR methods, DSR views, user involvement, DSS design innovations and problem domains. The findings indicate that new studies are needed to fill the knowledge gap in DSS design science, for more solid theoretical basis in near future.

Applications of Machine Learning in Education

2021 ◽  
pp. 72-113
Author(s):  
Fatima Ali Amer Jid Almahri ◽  
David Bell ◽  
Mahir Arzoky
Keyword(s):  
Machine Learning ◽  
Student Engagement ◽  
Interaction Analysis ◽  
Design Science ◽  
Science Research ◽  
Data Driven ◽  
Persona Development ◽  
Applications Of Machine Learning ◽  
Future Work ◽  
Is Design

This research aims to explore how to enhance student engagement in higher education institutions using novel chatbots. This study's principal research methodology is design science research, which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This chapter focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, precisely a k-means clustering technique. Data analysis is conducted using two datasets. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically k-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations.

scholarly journals Improving Business Intelligence Traceability and Accountability

2014 ◽  
Vol 25 (3) ◽  
pp. 28-47 ◽  
Author(s):  
Chin-Hoong Chee ◽  
William Yeoh ◽  
Shijia Gao ◽  
Gregory Richards
Keyword(s):  
Data Quality ◽  
Business Intelligence ◽  
Design Science ◽  
Science Research ◽  
Business Process Modelling ◽  
Integrated Framework ◽  
Related Data ◽  
Quality Dimensions ◽  
Product Map ◽  
Validation Stage

A Business Intelligence (BI) system provides users with multi-dimensional information (a so-called ‘BI product') to support decision-making. However, existing BI systems overlook the lineage metadata which supports individual data quality dimensions such as data believability and ease of understanding. Using a design science research paradigm, this paper proposes and develops an integrated framework (known as BI Product and Metacontent Map - ‘BIP-Map') to facilitate the traceability and accountability of BI products. Specifically, the business workflow layer of the integrated framework is modelled using business process modelling notation, and an information product map is used to model the second layer's information manufacturing process, whilst the third layer represents the metacontent detail of the data validation stage, from source system through to ETL, to the data warehousing stage. Also, the authors develop a BIP-Map informed prototype in collaboration with an online job advertising firm, the framework then being validated by key BI stakeholders of the firm. The integrated framework addresses individual-related data quality issues and builds user confidence by enhancing the traceability and accountability of a BI product.

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