Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies

The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies     

Progresson Sustainable City Assessment Index System for SDGs

Urban sustainability has become the world’s most important urban issue.The evaluation index system is an effective tool to objectively diagnosethe current status and problems of sustainable development.The existing urban sustainability evaluation index systems are mostly based on traditional statistical data and they have different emphases.Due to the suitability of index and availability of data,etc,These index systems are hardly used for comparative evaluation amongdifferent cities.With the interpretation of the sustainable city connotation by the Sustainable Development Goals11,and the use of multi-source data such as remote sensing and network big data,it is possible to achieve higher resolution for urban sustainability assessment under a unified standard.Based on this,this work analyzes the evolution of the concept of urban sustainable development and identifies the key areas of the connotation of sustainable city construction.This work also summarizes the research progress of sustainability evaluation indicators and analyzes typical index system.Based on SDG11,this study establish an open city sustainability index system,with combining traditional statistical data and multi-source data,such as remote sensing data and network big data.The framework of the evaluation index system aims to provide reference for the sustainability evaluation of cities in China under the framework of the United Nations.     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.     

Recent advances in social multimedia big data mining and applications

In the past decade, social media contributes significantly to the arrival of the Big Data era. Big Data has not only provided new solutions for social media mining and applications, but brought about a paradigm shift to many fields of data analytics. This special issue solicits recent related attempts in the multimedia community. We believe that the enclosed papers in this special issue provide a unique opportunity for multidisciplinary works connecting both the social media and big data contexts to multimedia computing.     

Big data analytics enhanced healthcare systems: a review

There is increased interest in deploying big data technology in the healthcare industry to manage massive collections of heterogeneous health datasets such as electronic health records and sensor data, which are increasing in volume and variety due to the commoditization of digital devices such as mobile phones and wireless sensors. The modern healthcare system requires an overhaul of traditional healthcare software/hardware paradigms, which are ill-equipped to cope with the volume and diversity of the modern health data and must be augmented with new “big data” computing and analysis capabilities. For researchers, there is an opportunity in healthcare data analytics to study this vast amount of data, find patterns and trends within data and provide a solution for improving healthcare, thereby reducing costs, democratizing health access, and saving valuable human lives. In this paper, we present a comprehensive survey of different big data analytics integrated healthcare systems and describe the various applicable healthcare data analytics algorithms, techniques, and tools that may be deployed in wireless, cloud, Internet of Things settings. Finally, the contribution is given in formation of a convergence point of all these platforms in form of SmartHealth that could result in contributing to unified standard learning healthcare system for future.

     

Optimizing the energy efficiency of chiller systems in the semiconductor industry through big data analytics and an empirical study

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed and automaticity in manufacturing processes. The semiconductor industry is a fundamental driver of this transformation; moreover, due to the highly complex and energy-consuming nature of the semiconductor manufacturing process, semiconductor fabrication facilities (fabs) can also benefit greatly from incorporating big data analytics to improve production and energy efficiency. This paper developed a big data analytics framework, along with an empirical study conducted in collaboration with a semiconductor manufacturer in Taiwan, to optimize the energy efficiency of chiller systems in semiconductor fabs. Chiller systems are one of the most energy-consuming systems within a typical modern fab. The developed big data analytics framework allows production managers to ensure that chiller systems operate at an optimized level of energy efficiency under dynamically changing conditions, while fulfilling the chilling demands. Compared to the commonly-used heuristics previously employed at the fab to tune chiller system parameters, by the utilization of big data analytics, it is shown that fabs can achieve substantial energy savings, greater than 12%. The developed framework and the lessons learned from the empirical study are not only generalizable but also useful for practitioners who are interested in applying big data analytics to optimize the performance of other equipment systems in fabs.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Server-Edge dualized closed-loop data analytics system for cyber-physical system application

In this research, we propose a system architecture of the server-edge dualized closed-loop data analytics system for cyber-physical system (CPS) application. We define six essential components for the data analytics system for CPS application: (1) the cyber model, (2) the data analytics module, (3) the data analysis model execution module, (4) the decision making module, (5) the system control module, and (6) the visualization module. We then propose an architecture of dualized closed-loop data analytics with server and edge-computing devices. The proposed dualized architecture of the data analytics system has advantages in handling the three issues of applying data analytics systems to the manufacturing context: (1) the system overload issue of the data analytics module due to large volumes of data, (2) the automation issue in the sequences of data analysis model generation, data analysis module execution, and system control, and (3) the real-time issue of data analysis model execution. In particular, a PMML-based data analysis model information parsing structure is proposed to deal with the automation issue. A case study that applies the proposed server-edge dualized closed-loop data analytics system for CPS application to the die-casting factory in Korea is introduced.     

Utilizing big data analytics for information systems research: challenges,promises and guidelines

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.     

Preface

It is our great honor to announce the publication of this special section on AI and big data analytics in biology and medicine in the Journal of Computing Science and Technology (JCST). As more and more modern biological and medical data are produced,artificial intelligence (AI) and big data analytics are playing an increasingly important role in helping to draw meaningful and logical conclusions about biology and medicine.Understanding biological and medical data will help answer important life questions on Earth,find solutions to global health problems,and even help solve tough problems such as drug design and disease diagnosis.The information obtained from biology and medicine is not only very detailed,but also has unique properties such as low quality data,big data sizes,different complex formats,high dimensions,many duplications and much noise,and so on.They all require special skills or unique tools for analysis and interpretation.Thus,a lot of studies using AI and big data analytics on biological and medical data are becoming very popular and hot topics in the computer science research field.     

System architecture for blockchain based transparency of supply chain social sustainability

Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.     

Manufacturing big data ecosystem: A systematic literature review

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.     

A survey on big data-driven digital phenotyping of mental health

The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.     

A systematic literature review about integrating dependability attributes,performability and sustainability in the implantation of cooling subsystems in data center

In recent years, data centers (DCs) have evolved a lot, and this change is related to the advent of cloud computing, e-commerce, services aimed at social networks, and big data. Such architectures demand high availability, reliability, and performance at satisfactory service levels; requirements are often neglected at the expense of high costs. In addition, the use of techniques capable of promoting greater environmental sustainability is most often forgotten in the design phase of such architectures. Approaches to perform an integrated assessment of dependability attributes for DCs, in general, are not trivial. Thus, this work presents the dependability attributes (availability and reliability), performability, and sustainability parameters that need special attention in implementing a cooling subsystem in DCs. That is one of the most cost generators for these infrastructures. In this study, we use the hypothetical-deductive method through a quantitative and qualitative approach; as for the procedure, it is bibliographical research through the review of scientific studies, and the research objectives are exploratory in nature. The results show that among all the papers selected and analyzed in this systematic literature review (SLR), none have jointly addressed performability, dependability, and sustainability in cooling systems for DCs. The main results of this work are presented through research questions, as they bring evidence of gaps to be addressed in the area. The four research questions point out challenges in implementing cooling systems in DCs and present the techniques and/or methods most used to propose or analyze data center cooling infrastructures; addressing the essential sustainability requirements for cooling subsystems, and finally, presenting open questions that can be investigated in the area of sustainable cooling in DCs regarding the data center’s cooling and the difficulty of incorporating dependability attributes in the environmental context. In addition to these results, the present study actively contributes to the concept of a “green data center” for the companies, which ranges from the choice of renewable energy sources to more efficient information technology equipment. Hence, we show the relevance and originality of this SLR and its results.

     

Establishment-level occupational safety analytics: Challenges and opportunities

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the “big” data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.