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.     

Towards collaborative intelligent IoT eHealth: From device to fog,and cloud

The relationship between technology and healthcare due to the rise of intelligent Internet of Things (IoT), Artificial Intelligence (AI), and the rapid public embracement of medical-grade wearables has been dramatically transformed in the past few years. AI-powered IoT enabled disruptive changes and unique opportunities to the healthcare industry through personalized services, tailored content, improved availability and accessibility, and cost-effective delivery. Despite these exciting advancements in the transition from clinic-centric to patient-centric healthcare, many challenges still need to be tackled. The key to successfully unlock and enable this horizon shift is adopting hierarchical and collaborative architectures to provide a high level of quality in key attributes such as latency, availability, and real-time analytics. In this paper, we propose a holistic AI-driven IoT eHealth architecture based on the concept of Collaborative Machine Learning approach in which the intelligence is distributed across Device layer, Edge/Fog layer, and Cloud layer. This solution enables healthcare professionals to continuously monitor health-related data of subjects anywhere at any time and provide real-time actionable insights which ultimately improves the decision-making power. The feasibility of such architecture is investigated using a comprehensive ECG-based arrhythmia detection case study. This illustrative example discusses and addresses all important aspects of the proposed architecture from design implications such as corresponding overheads, energy consumption, latency, and performance, to mapping and deploying advanced machine learning techniques (e.g., Convolutional Neural Network) to such architecture.     

Harnessing Artificial Intelligence to Deliver Real-Time Intelligence and Business Process Improvements

As demand for data scientists in audit/Governance, risk management and compliance (GRC), and industry in general, outpaces supply, data science in a box—packaged analytics powered by artificial intelligence (AI) and guided machine learning—can bridge the gap to bring analytics to every major enterprise. Packaged analytics harness the power of AI and machine learning technologies to help operations, finance executives, and GRC professionals do their jobs better; optimize business processes; and deliver actionable insights for better decision making. This article will explore real-world case studies of how companies have used packaged analytics to achieve process improvements, better oversight over financial spend, and significant return on investment. It is a guide to internal auditors and their GRC counterparts on what is available and suggests they can partner or use the products independently and significantly contribute to their companies.     

Using resource-based view theory to assess the value of ERP commercial-packages in SMEs

This study explores the enterprise resource planning (ERP) variations in value on small and medium enterprises (SMEs) across four commercial-packages (Microsoft NAV, SAP All-in-one, ORACLE JDE, and SAGE X3). Grounded on the resource-based view (RBV) theory of the firm, we assess a research model linking three determinants; ERP use, collaboration, and analytics to explain the ERP value in three effects (individual productivity, management control, and customer satisfaction). Using a survey data set of 883 firms across European SMEs we test the theoretical model through structural equation modelling. This study provides empirical evidence on how European SMEs find value from the top four commercial-packaged ERPs. Whereas for Dynamics and ORACLE the most important factor is analytics system capability, for SAP and SAGE it is greater collaboration system capability. Furthermore, for SAP and ORACLE greater ERP use is perceived as an important factor, but not for Dynamics and SAGE. In addition, the study finds that both collaboration and analytics capabilities are the greatest differentiators to ERP value, which is consistent with the RBV. The finding provide guidance to business implementation strategies and to software development. The limitations and future work of the study are noted.     

A survey of recommender systems for energy efficiency in buildings: Principles,challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors’ knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.     

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.     

A secure and scalable storage system for aggregate data in IoT

In recent years, with the impressive rapid development of integrated circuit and networking technologies, computers, devices and networking have become highly pervasive, incurring the introduction, development and deployment of the Internet of Things (IoT). The tiny identifying devices and wearables in IoT have transformed daily life in human society, as they generate, process and store the amount of data increasing at exponential rate all over the world. Due to high demand on data mining and analytics activities in IoT, secure and scalable mass storage systems are highly demanded for aggregate data in efficient processing. In this paper, we propose such a secure and scalable IoT storage system based on revised secret sharing scheme with support of scalability, flexibility and reliability at both data and system levels. Shamir’s secret sharing scheme is applied to achieve data security without complex key management associated with traditional cryptographic algorithms. The original secret sharing scheme is revised to utilize all coefficients in polynomials for larger data capacity at data level. Flexible data insert and delete operations are supported. Moreover, a distributed IoT storage infrastructure is deployed to provide scalability and reliability at system level. Multiple IoT storage servers are aggregated for large storage capacity whereas individual servers can join and leave freely for flexibility at system level. Experimental results have demonstrated the feasibility and benefits of the proposed system as well as tangible performance gains.     

Knowledge-based tool for planning of enterprise resources in ASEAN SMEs

Manufacturing has been identified as a key pillar of growth in many Southeast Asian (ASEAN) economies. However, in the last decade many countries have become keen competitors for foreign direct investments. Many countries are trying to improve their total business capabilities by encouraging computerisation of small and medium sized enterprises (SME). Manufacturing SMEs (M-SMEs) are tasked to adopt technologically advanced programmes. With an improving public education system and more literate work force, more SMEs are better positioned to tap into the knowledge-based economy. There is tremendous amount of knowledge intensive activities within the multi-flows of the M-SMEs.Although the concept of ERP systems and artificial intelligence (AI) techniques have been around for more than two decades, this has largely remained the domain of the larger companies. ASEAN M-SMEs have been slow to implement it. In this paper, the various strategic and operational requirements of regional M-SMEs are presented and a knowledge-based resources planning model making use of AI techniques is proposed. This improved AI model makes use of the large amount of accumulated knowledge typically found in the M-SMEs, especially those in the electronics and precision engineering sectors. This includes a case study of how an electronics precision engineering company adopted the proposed AI model.     

The Fourth Industrial Revolution and implications for innovative cluster policies

The Fourth Industrial Revolution has become a global buzz word since the World Economic Forum (WEF) adopted it as an annual issue in 2016. It is represented by hyper automation and hyper connectivity based on artificial intelligence (AI), big data, robotics, and Internet of things (IoT). AI, big data, and robotics can contribute to developing hyper automation that can increase productivity and intensify industrial production. Particularly, robots using AI can make decision by themselves as human being on complicated processes. Along with the hyper automation, the hyper connectivity increases not only at national, but also global level by using information and communication technologies (ICT). IoT is the core technology to create the hyper connectivity in Cyber Physical System (CPS) that connects technology, nature, and human being. Accordingly, a perfect convergence between ICT and manufacturing can be completed in the Fourth Industrial Revolution era and an extremely efficient flexible production system by spreading IoT in CPS will be established. Under such a condition, innovative clusters must play their traditional roles in cradles of technology innovation and commercialization. It must be difficult challenges for innovative clusters to meet their targets and to be adjusted by the changing new environment at the same time. This paper argues how the Fourth Industrial Revolution can change the global production chain and how core technologies function in industries. Furthermore, it focuses on how innovative clusters have to evolve to respond the Fourth Industrial Revolution. Last, but not least it also analyzes whether or not innovative clusters can play their roles as technology innovation hubs in the real world and CPS in the Fourth Industrial Revolution era.     

Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.     

RIoTBench: An IoT benchmark for distributed stream processing systems

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench , a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.     

IoT-enabled smart appliances under industry 4.0: A case study

Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.     

A novel lifecycle model for Web-based application development in small and medium enterprises

Software engineering’s lifecycle models have proven to be very important for traditional software development. However, can these models be applied to the development of Web-based applications as well? In recent years, Web-based applications have become more and more complicated and a lot of efforts have been placed on introducing new technologies such as J2EE, PhP, and.NET, etc., which have been universally accepted as the development technologies for Web-based applications. However, there is no universally accepted process model for the development of Web-based applications. Moreover, shaping the process model for small medium-sized enterprises (SMEs), which have limited resources, has been relatively neglected. Based on our previous work, this paper presents an expanded lifecycle process model for the development of Web-based applications in SMEs. It consists of three sets of processes, i.e., requirement processes, development processes, and evolution processes. Particularly, the post-delivery evolution processes are important to SMEs to develop and maintain quality web applications with limited resources and time.     

An exploratory study of information technology evaluation and benefits management practices of SMEs in the construction industry

While the number of articles on IT evaluation and benefits management has been substantial, limited attention has been given to these topics in small and medium-sized enterprises (SMEs), particularly the construction industry. This paper presents findings from a questionnaire survey that sought to examine the approaches used by 126 construction organisations to evaluate and justify their IT investments, as well as the benefits and costs that they have experienced due to IT implementation. The analysis of their responses identified three key findings. Firstly, different organisation types significantly differ in the amount they invest in IT and their firm size (in terms of turnover and number of employees) does not influence investment levels in IT. Secondly, the evaluation process adopted by construction SMEs is used as for both control and learning. Thirdly, a major barrier to justifying IT investments was attributed to having no strategic vision. While organisations experienced no significant differences in the tactical and operational benefits incurred after the adoption of IT, differences were found with respect to the strategic benefits. If construction SMEs are to leverage the benefits of IT, then this should form an integral part of their business strategy. Considering this, recommendations for IT evaluation for construction SMEs that are also pertinent for SMEs operating in other industry sectors, are presented.     

The impact of AMT practices on firm performance in manufacturing SMEs

This research deals with the use of advanced manufacturing technology (AMT) in small and medium-sized enterprises (SMEs). The main purpose of this study is to find out the types of AMT to adopt to improve the manufacturing parameters that have significant impact on firm performance. In the research firstly, the current situation in AMT use in SMEs is examined. Secondly, the relationship between manufacturing parameters and firm performance is investigated. Finally, the relationship between the types of AMT and manufacturing parameters is investigated. The sampling pool includes 102 manufacturing SMEs that introduced AMTs. Our findings show that local area network, computer-aided design, and computer-aided manufacturing technologies are the most commonly used and automated storage, robotics, and wide area network technologies are the least commonly used AMTs in SMEs. In addition, the statistical association between manufacturing parameters and firm performance indicate that product design performance, fixture utilization, setup and production planning performance have positive impact, while capacity utilization and finished product inventory need have negative impact on firm performance.     

A framework of pavement management system based on IoT and big data

With the expansion of urban road network, the importance of road maintenance is increasing, which guarantee the operation efficiency of transportation infrastructure. Compare to road construction, road maintenance is more sophisticated. Even though invested a lot of manpower and financial resources, management departments are troubled by insufficient and lagging road maintenance. The development and widely application of technologies including IoT, Big Data and Artificial Intelligence(AI) in various industries offer possible solutions to this issue. However, how to utilize these technologies is a challenge for related administration department because of their weak technical force. To address the problem, a pavement management system(PMC) is developed in this research. Combined with IoT and big data, the PMS provide an overall management structure of road maintenance. Composed of three subsections: Pavement detection and 3D modeling, Data analysis and Decision support, the PMS offer an automated and intelligent solution to related administrative departments and firms. Besides, two road maintenance related firms, one is a road maintenance company and the other is a technical firm that offer smart solutions to road maintenance, are selected as cases to illustrate how PMS are applied to support the daily operations and road maintenance.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Shifting intra‐ and inter‐organizational innovation processes towards digital business: An empirical analysis of SMEs

The literature suggests that increasing investments in information and communication technologies (ICTs), knowledge exchange and sharing help SMEs tackle the current global and dynamic environment. Given that much of the useful knowledge resides outside the enterprises’ boundaries, these technological tools foster the gathering of big data and information. Despite these premises, few studies have considered the role of ICTs and big data in intra‐ and inter‐organizational ties and the consequent effects on enterprises’ innovation performance. The paper investigates whether ICTs oriented to intra‐organizational (in‐house research and development [R&D]) and inter‐organizational (open innovation) processes improve SMEs’ innovation performance. Therefore, via structural equation modelling (SEM), the study analyses a sample of 239 knowledge‐intensive SMEs located in Italy. The noteworthy results are that ICTs oriented to intra‐ and inter‐organizational innovation processes improve both these processes in generating new products and/or services. On this basis, managerial and academic implications are provided, along with avenues for further research.