MaCuDE IS Task Force Phase II Report: Views of Industry Leaders on Big Data Analytics and AI

2023 | Communications of the Association for Information Systems | Citations: 2

Authors: Lyytinen, Kalle; Topi, Heikki; Tang, Jing

Abstract: This paper represents the Phase II report of the Management Curriculum for the ... Expand

Abstract: This paper represents the Phase II report of the Management Curriculum for the Digital Era (MaCuDE) disciplinary task force on information systems (IS). Aligned with the current work of the AIS (Association for Information Systems) and ACM (Association for Computing Machinery), we focus on the current and future industry driven educational needs and requirements posed by big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and related innovations. In this report, we probe and report on the views of industry leaders regarding BDA/AI education needs. We conducted 18 rich semi-structured interviews with a representative sample of industry leaders situated around key changes and issues related to workforce demands in digital transformation and associated educational needs. We performed a grounded theory based analysis of key themes in reported education needs. We note the shifting meaning of AI and BDA phenomena and identify three main organizational level needs for the digital eracapability improvement and transformation, decision-making strategies and tactics, and changes in operations or products -and connect them to three individual professional competencies necessary to deliver them: fundamental environmental competencies, data information and content, and system design competencies. Based on the analysis we outline several novel competency-based IS curriculum recommendations for the master's and undergraduate level IS education.Architecting for cloud: "And so the thing I noticed, a lot of these companies, when their data is growing so big and their database costs are running so high, they think about going towards Open Source Software, Open Source databases to store all the data and, hence, that creates a demand on the workforce to actually know how to use those. "Understanding data structures, architectures, and governance: "So that's his role, and the key requirement for that role again is Data Architecture, data Engineering and Data Management" Building and managing machine learning models: "Second, of course, is strong competency in the use of Machine Learning packages or models. This field is evolving aggressively." "For developing or programming our own AI solutions, our employees have no deep knowledge of mathematical models and how to build mature and value-creating AI applications." IS management and operations: "The talent sort of wars out there are really, really tough, and finding people who understand that, (how to) generate the data, use the data, think about the data, manipulate it are really, really important. I agree. It used to be you had to have people with Project Management. That was critical and that was everyone competed for that, and now it's that plus the data, the Machine Learning."Based on the data collected in this study, we cannot offer a clear answer to the questions of how BDA and AI affect areas that do not belong to the two main areas of the study. Given, however, how quickly BDA and AI have grown and how substantial related resource requirements are in organizations, it is clear that they already have an impact on several competency needs such as programming language selection, selection of BDA and AI tools based on cloud-based solutions, data architecture, data governance, service repertoires available on platform environments, and special characteristic of BDA and AI projects and how they affect education for project management needs and competencies. Summary of Findings Related to Individual CompetenciesGenerally, we observe a strong alignment and continuity between the recent information systems curriculum recommendations and the findings of this study. The data highlights the continued importance of IS core topics covered in all past curricula, such as data management, systems development, IT infrastructure and architecture (including security), and IS (project) management.The results also highlight also multiple areas that need more emphasis and careful design decisions when schools plan and implement their BDA and AI based programs: Integration of business and technology competencies in ways that allow seamless communication with both business and technology experts  A stronger emphasis on multiple life cycle models and concepts related to data management and data science than in the recent model curricula focused on system life cycles  Heightened focus on new dimensions of data analytics competencies (including communication-related elements such a storytelling and visualization)  The growing importance of ethics as a foundation for ensuring privacy and security of BDA and AI models  Need for more skills in statistics  Understanding the novel requirements that AI solutions set up for IT infrastructure  The importance of competencies in AI systems development and deployment. Collapse

Topics: artificial intelligence big data analytical information system data management decision making

Methods: qualitative interview machine learning meta learning field study personal interview

AI applications of data sharing in agriculture 4.0: A framework for role-based data access control

2021 | International Journal of Information Management | Citations: 0

Authors: Spanaki, Konstantina; Karafili, Erisa; Despoudi, Stella

Abstract: Industry 4.0 and the associated IoT and data applications are evolving rapidly a ... Expand

Abstract: Industry 4.0 and the associated IoT and data applications are evolving rapidly and expand in various fields. Industry 4.0 also manifests in the farming sector, where the wave of Agriculture 4.0 provides multiple oppor­ tunities for farmers, consumers and the associated stakeholders. Our study presents the concept of Data Sharing Agreements (DSAs) as an essential path and a template for AI applications of data management among various actors. The approach we introduce adopts design science principles and develops role-based access control based on AI techniques. The application is presented through a smart farm scenario while we incrementally explore the data sharing challenges in Agriculture 4.0. Data management and sharing practices should enforce defined contextual policies for access control. The approach could inform policymaking decisions for role-based data management, specifically the data-sharing agreements in the context of Industry 4.0 in broad terms and Agri­ culture 4.0 in specific. Collapse

Topics: artificial intelligence data management access control role-based access control industry 4.0

Methods: design science case study

Theories: theory of abduction

Pirasa: strategic protocol selection for e-commerce agents

2019 | Electronic Markets | Citations: 0

Authors: Hopkins, Jack; Kafali, Özgür; Alrayes, Bedour; Stathis, Kostas

Abstract: We present Pirasa: an agent-based simulation environment for studying how autono ... Expand

Abstract: We present Pirasa: an agent-based simulation environment for studying how autonomous agents can best interact with each other to exchange goods in e-commerce marketplaces. A marketplace in Pirasa enables agents to enact buyer or seller roles and select from sales, auction, and negotiation protocols to achieve the individual goals of their users. An agent’s strategy to maximize its utility in the marketplace is guided by its user’s preferences and constraints such as ‘maximum price’ and ‘deadline’, as well as an agent’s personality attributes, e.g., how ‘eager’ or ‘late’ the agent can be for exchanging goods and whether the agent is a ‘spender’ or ‘saver’ in an exchange. To guide the agent’s actions selected by a strategy, we use the notion of electronic contracts formulated as regulatory norms. In this context, we present how Pirasa is organized with regards to seller processes for goods submission, the inclusion of buyer preferences, and the management of transactions through specialized broker agents. Using randomized simulations, we demonstrate how a buyer agent can strategically select the most suitable protocol to satisfy its user’s preferences, goals and constraints in dynamically changing market settings. The generated simulation data can be leveraged by researchers to analyze agent behaviors, and develop additional strategies. Collapse

Topics: electronic commerce personality social norm Microsoft Windows usability

Methods: simulation experiment agent based simulation design artifact design science

Future paradigms of automated processing of business documents

2018 | International Journal of Information Management | Citations: 1

Authors: Cristani, Matteo; Bertolaso, Andrea; Scannapieco, Simone; Tomazzoli, Claudio

Abstract: In this paper we summarize the results obtained so far in the communities intere ... Expand

Abstract: In this paper we summarize the results obtained so far in the communities interested in the development of automated processing techniques as applied to business documents, and devise a few evolutions that are demanded by the current stage of either those techniques by themselves or by collateral sector advancements. It emerges a clear picture of a field that has put an enormous effort in solving problems that changed a lot during the last 30 years, and is now rapidly evolving to incorporate document processing into workflow management systems on one side and to include features derived by the introduction of cloud computing technologies on the other side. We propose an architectural schema for business document processing that comes from the two above evolution lines. Collapse

Topics: logic programming language

Methods: survey qualitative content analysis computational algorithm conceptual modelling archival research

Taxo-Semantics: Assessing similarity between multi-word expressions for extending e-catalogs

2017 | Decision Support Systems | Citations: 0

Authors: Angermann, Heiko; Pervez, Zeeshan; Ramzan, Naeem

Abstract: Taxonomies, also named directories, are utilized in e-catalogs to classify goods ... Expand

Abstract: Taxonomies, also named directories, are utilized in e-catalogs to classify goods in a hierarchical manner with the help of concepts. If there is a need to create new concepts when modifying the taxonomy, the semantic similarity between the provided concepts has to be assessed properly. Existing semantic similarity assessment techniques lack in a comprehensive support for e-commerce, as those are not supporting multi-word expressions, multilingualism, the import/export to relational databases, and supervised user-involvement. This paper proposes Taxo-Semantics, a decision support system that is based on the progress in taxonomy matching to match each expression against various sources of background knowledge. The similarity assessment is based on providing three different matching strategies: a lexical-based strategy named Taxo-Semantics-Label, the strategy Taxo-Semantics-Bk, which is using different sources of background knowledge, and the strategy Taxo-Semantics-User that is providing user-involvement. The proposed system includes a translating service to analyze non-English concepts with the help of the WordNet lexicon, can parse taxonomies of relational databases, supports user-involvement to match single sequences with WordNet, and is capable to analyze each sequence as (sub)-taxonomy. The three proposed matching strategies significantly outperformed existing techniques. Taxo-Semantics-Label could improve the accuracy result by more than 7% as compared to state-of-the-art lexical techniques. Taxo-Semantics-Bk could improve the accuracy compared to structure-based techniques by more than 8%. And, Taxo-Semantics-User could additionally increase the accuracy by on average 23%. Collapse

Topics: database system knowledge representation participatory design decision support system Prolog

Methods: machine learning WordNet design methodology experiment computational algorithm

On the Discovery of Declarative Control Flows for Artful Processes

2015 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ciccio, Claudio Di; Mecella, Massimo

Abstract: Artful processes are those processes in which the experience, intuition, and kno ... Expand

Abstract: Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the “knowledge workers,” such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented. Collapse

Topics: electronic mail Java knowledge base open source operating system

Methods: computational algorithm business process modeling event log case study experiment

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

2015 | Decision Support Systems | Citations: 1

Authors: Pinto, Tiago; Barreto, João; Praça, Isabel; Sousa, Tiago M.; Vale, Zita; Solteiro Pires, E. J.

Abstract: The energy sector has suffered a significant restructuring that has increased th ... Expand

Abstract: The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach. Collapse

Topics: decision support artificial intelligence database system Prolog logic programming language

Methods: meta learning simulation case study computational algorithm k-means clustering

Arguing over Goals for Negotiation: Adopting an Assumption-Based Argumentation Decision Support System

2014 | Group Decision and Negotiation | Citations: 0

Authors: Morge, Maxime; Mancarella, Paolo

Abstract: Computational models of argumentation has been put forward as a promising approa ... Expand

Abstract: Computational models of argumentation has been put forward as a promising approach to support decision making. In this context several recent works have proposed argumentation-based frameworks for decision making. In this paper we describe an application based on an argumentation-based mechanism for decision-making to concede. Adopting the assumption-based approach of argumentation, we propose an argumentation framework in which preferences are attached to goals. Arguments are defined as tree-like structures. Our framework is equipped with a computational counterpart for solving a decision problem, modeling the intuition that high-ranked goals are preferred to low-ranked goals which can be withdrawn. In this way, our framework suggests some decisions and provides an interactive and intelligible explanation of this choice. Our implementation, called MARGO, has been used for service selection within the ArguGRID project. We illustrate our approach with an industrial application, and illustrate the operation of the system with a running example. Collapse

Topics: decision making Prolog procurement management logic programming language resource allocation

Methods: chatbot literature study

Theories: game theory

That is your evidence?: Classifying stance in online political debate

2012 | Decision Support Systems | Citations: 0

Authors: Walker, Marilyn A.; Anand, Pranav; Abbott, Rob; Tree, Jean E. Fox; Martell, Craig; King, Joseph

Abstract: A growing body of work has highlighted the challenges of identifying the stance ... Expand

Abstract: A growing body of work has highlighted the challenges of identifying the stance that a speaker holds towards a particular topic, a task that involves identifying a holistic subjective disposition. We examine stance classification on a corpus of 4731 posts from the debate website ConvinceMe.net, for 14 topics ranging from the playful to the ideological. We show that ideological debates feature a greater share of rebuttal posts, and that rebuttal posts are significantly harder to classify for stance, for both humans and trained classifiers. We also demonstrate that the number of subjective expressions varies across debates, a fact correlated with the performance of systems sensitive to sentiment-bearing terms. We present results for classifying stance on a per topic basis that range from 60% to 75%, as compared to unigram baselines that vary between 47% and 66%. Our results suggest that features and methods that take into account the dialogic context of such posts improve accuracy. Collapse

Topics: climate change website decision support system logic programming language

Methods: experiment naïve bayes support vector machine computational algorithm part of speech tagging

Constraint-centric workflow change analytics

2011 | Decision Support Systems | Citations: 0

Authors: Wang, Harry Jiannan; Zhao, J. Leon

Abstract: In a globalized economic environment with volatile business requirements, contin ... Expand

Abstract: In a globalized economic environment with volatile business requirements, continuous process improvement needs to be done regularly in various organizations. However, maintaining the consistency of workflow models under frequent changes is a significant challenge in the management of corporate information services. Unfortunately, few formal approaches are found in the literature for managing workflow changes systematically. In this paper, we propose an analytical framework for workflow change management through formal modeling of workflow constraints, leading to an approach called Constraint-centric Workflow Change Analytics (CWCA). A core component of CWCA is the formal definition and analysis of workflow change anomalies. We operationalize CWCA by developing a change anomaly detection algorithm and validate it in the context of procurement management. A prototype system based on an open-source rule engine is presented to provide a proof-of-concept implementation of CWCA. Collapse

Topics: procurement management predicate logic change management business process management system open source

Methods: computational algorithm business process modeling design artifact anomaly detection reference modelling