Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue

Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research.The research reported in this paper was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and the AETHER-UA (PID2020-112540RB-C43) Projects from the Spanish Ministry of Science and Innovation

MADNESS OF THE CROWD - HOW BIG DATA CREATES EMOTIONAL MARKETS AND WHAT CAN BE DONE TO CONTROL BEHAVIOURAL RISK

In the recent years the term Big Data has been vividly discussed in management, the IS community and in the IT departments. Du to its potential for corporate performance and competitive advantage it has gained large attention up into the C-level-management. Observations on the possible negative consequnces of living in a data-driven world have mostly been limited to the perspective of an individual. For instance, concerns about data privacy have been vividly discussed when the growing hunger of governmental or private institutions for ever more and more personalized data was made public. This article starts with a critical reflection on the phenomena of Big Data, focusing on the consequnces for organizations and decision making. Next a case from the field of risk management is investigated in more detail using behavioural economics. Upon a series of experiments this paper sheds light on the possibility to create emotional markets using Big Data analytics in an un-reflected way. As a key takeaway this article should raise the awareness of behavioural risk. The presented work suggests extending the organizational risk framework by addressing behavioural risk

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Enabling data-driven decision-making for a Finnish SME: a data lake solution

In the era of big data, data-driven decision-making has become a key success factor for companies of all sizes. Technological development has made it possible to store, process and analyse vast amounts of data effectively. The availability of cloud computing services has lowered the costs of data analysis. Even small businesses have access to advanced technical solutions, such as data lakes and machine learning applications. Data-driven decision-making requires integrating relevant data from various sources. Data has to be extracted from distributed internal and external systems and stored into a centralised system that enables processing and analysing it for meaningful insights. Data can be structured, semi-structured or unstructured. Data lakes have emerged as a solution for storing vast amounts of data, including a growing amount of unstructured data, in a cost-effective manner. The rise of the SaaS model has led to companies abandoning on-premise software. This blurs the line between internal and external data as the company’s own data is actually maintained by a third-party. Most enterprise software targeted for small businesses are provided through the SaaS model. Small businesses are facing the challenge of adopting data-driven decision-making, while having limited visibility to their own data. In this thesis, we study how small businesses can take advantage of data-driven decision-making by leveraging cloud computing services. We found that the report- ing features of SaaS based business applications used by our case company, a sales oriented SME, were insufficient for detailed analysis. Data-driven decision-making required aggregating data from multiple systems, causing excessive manual labour. A cloud based data lake solution was found to be a cost-effective solution for creating a centralised repository and automated data integration. It enabled management to visualise customer and sales data and to assess the effectiveness of marketing efforts. Better skills at data analysis among the managers of the case company would have been detrimental to obtaining the full benefits of the solution

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

Next generation smart manufacturing and service systems using big data analytics

© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors

Understanding Continuous Use of Business Intelligence Systems: A Mixed Methods Investigation

Business intelligence (BI) systems play an important role in organizations’ decision-making processes. The existing literature has long focused on the continuous use of information systems (IS). However, the specificities of BI systems such as voluntary use, long-term return of investments, heterogeneity of their use cases, and innovative rather than routine use in such systems motivate our investigating continuous use in the specific context of BI systems. To theorize continuous use of BI systems, we investigate the influencing factors and their interactions. By means of an exploratory and confirmatory mixed-methods research design that comprises a literature review, a single-case study, and a survey, we integrate the identified factors and hypothesize their influence on the continuous use of BI systems in a research model. We test the research model following a partial least squares (PLS) approach to structural equation modeling (SEM). The paper makes two primary contributions: 1) it confirms certain well-established constructs and relations in the specific context of BI systems, which are generally theorized for the continuous use of IS, and 2) it introduces either new constructs or new relations through the given investigation in the context of BI systems. Future studies can test these new constructs and relations as potential input for theorizing general IS continuous use