Query: Big data analytics information systems

title: Utilizing big data analytics for information systems research: challenges, promises and guidelines
abstract: This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g. predictive modelling, natural language processing) Following the phases of a typical research process, this article is set out to dissect BDA's challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.

title: An Accounting Information Systems Perspective on Data Analytics and Big Data
abstract: Forty six academics and practitioners participated in the second Journal of Information Systems Conference to discuss data analytics and Big Data from an accounting information systems perspective. The panels discussed the evolving role of technology in accounting, privacy within the domain of Big Data, and people and Big Data. Throughout all three panels, several topics emerged that impact all areas of accounting developing enhanced analytical and data handling skills; evaluating privacy, security requirements, and risks; thinking creatively; and assessing the threat of automation to the accounting profession. Other topics were specific to a segment of the profession, such as the growing demand for privacy compliance audits and the curriculum adjustments necessary to develop data analytic skills. This commentary synthesizes and expands the discussions of the conference panels and suggests potential areas for future research.

title: Big Data Analytics: A Field of Opportunities for Information Systems and Technology Researchers
abstract: In recent years, the information systems research community has been experiencing an exciting time discovering and coming to grips with many unexplored or untapped opportunities for research that lie at the intersection of the disciplines of information systems and technology, big data, analytics, and data science. In this article, some of the critical aspects of the big data problem are discussed, and their importance as well as relevance to information systems research is shown. Also proposed are a number of research problems in this area that interested information systems scholars can address to delve more deeply into the field as well as to generate further active interest of the subject within the information systems community.

title: Big Data analytics and Computational Intelligence for Cyber Physical Systems: Recent trends and state of the art applications
abstract: Big data is fuelling the digital revolution in an increasingly knowledge driven and connected society by offering big data analytics and computational intelligence based solutions to reduce the complexity and cognitive burden on accessing and processing large volumes of data. In this paper, we discuss the importance of big data analytics and computational intelligence techniques applied to data produced from the myriad of pervasively connected machines and personalized devices offering embedded and distributed information processing capabilities. We provide a comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data. We discuss a number of exemplar application areas that generate big data and can hence benefit from its effective processing. State of the art research and novel applications in health care, intelligent transportation and social network sentiment analysis, are presented and discussed in the context of Big data, Cyber Physical Systems (CPS) and Computational Intelligence (CI) We present a data modelling methodology, which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial Temporal State Machine (HSTSM) The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analysing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas. A conceptual cyber physical architecture, which can accommodate and benefit from the proposed methodology, is further presented.

title: A Proposed Concentration Curriculum Design for Big Data Analytics for Information Systems Students.
abstract: Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design emphasizes expanded learning of business, mathematical and statistical, and presentation skills, in projects of teams, in addition to skills in technology. This paper will be beneficial to educators considering improvement of the curriculum for Big Data Analytics and to students desiring a more contemporary program.

title: Incorporating Big Data Analytics into Enterprise Information Systems
abstract: Big data analytics has received widespread attention for enterprise development and enterprise information systems (EIS) However, how can it enhance the development of EIS? How can it be incorporated into EIS? Both are still big issues. This paper addresses these two issues by proposing an ontology of a big data analytics. This paper also examines incorporation of big data analytics into EIS through proposing BABES: a model for incorporating big data analytics services into EIS. The proposed approach in this paper might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence as well as intelligent agents.

title: Towards a Design Model for Interdisciplinary Information Systems curriculum Development, as exemplified by Big Data Analytics Education
abstract: The need for interdisciplinary programs for problem based education increases. However, there is a substantial shortage of skilled boundary spanning students, as prominently evidenced in the field of big data analytics. To solve this problem, this paper suggests a generic interdisciplinary IS curriculum process design model. The model considers education of students with deep knowledge in more than one discipline who possess the right skills that are needed by their future employers. It is applicable to various disciplines and potential combinations of these disciplines. Based on skills representing the consensus of corresponding communities and professionals, the model allows the definition of university specific constraints which are considered in the curriculum design. Besides the process design model we have implemented a prototype for a curriculum generator which supports the generation of concrete curricula. Through our approach that is based on the design science research paradigm, we formalize the curriculum development process which allows the fast and easy design of interdisciplinary programs.

title: Information systems innovation adoption in higher education: Big data and analytics
abstract: Much as the term analytics is not new in the research field. However, analytics and big data are still not fully understood, nor yet been thoroughly and empirically researched by institutions of higher education (HE) more so, in the South African context. Many organizations and businesses are welcoming and adopting such an information system innovation. However, there has been a limited interest in the adoption big data and analytics in institutions of HE. The lack of interest in big data and analytics from HE institutions has caused institutions to function with substantial delays in analysing readily evident data. Furthermore, the lack of adequate theoretical frameworks for IS adoption in HE institutions has steered the many failures to produce meaningful, accessible and timely information for decision making. Therefore this calls for the development of a conceptualized framework for the adoption of big data and analytics in institutions of HE.

title: Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates
abstract: Meanings of risk in criminal justice assessment continue to evolve, making it critical to understand how particular compositions of risk are mediated, resisted and re configured by experts and practitioners. Criminal justice organizations are working with computer scientists, software engineers and private companies that are skilled in big data analytics to produce new ways of thinking about and managing risk. Little is known, however, about how criminal justice systems, social justice organizations and individuals are shaping, challenging and redefining conventional actuarial risk episteme(s) through the use of big data technologies. The use of such analytics is shifting organizational risk practices, challenging social science methods of assessing risk, producing new knowledge about risk and consequently new forms of algorithmic governance. This article explores how big data reconfigure risk by producing a new form of algorithmic risk a form of risk which is posited as different from the social science (psychologically) informed risk techniques already in use in many justice sectors. It also shows that new experts are entering the risk game, including technologists who make data public and accessible to a range of stakeholders. Finally, it demonstrates that big data analytics can be used to produce forms of usable knowledge that constitute types of 'information activism' This form of activism produces alternative risk narratives, which are focused on 'criminogenic structures' or 'criminogenic policy'

title: Big data analytics architecture design An application in manufacturing systems
abstract: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re architecting approach incorportaitng careful and thorough evaluation of goals for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases.  We propose an approach for goal obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution.  The approach employs goal oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. Next, it combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems.  The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal oriented modelling for exploring goals and obstacles in integrating systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper connected manufacturing collaboration system to a new big data architecture.