Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

BackgroundCambodia has been investing in Village Malaria Workers (VMWs) to improve malaria case management in rural areas. This study assessed the quality of the VMWs’ services compared to those by a government-run health center from the perspective of community members. We focused on VMWs’ contribution to promote their action to control malaria. A community-based cross-sectional study was conducted in Kampot province in 2009. Interviews were conducted at every accessible household in a village with VMWs (n = 153) and a village with a health center (n = 159), using interviewer administered questionnaire. Preference of the interview was given to female household head. Multiple regression analyses were run to compare knowledge about malaria, preventive measures taken, and time before first malaria treatment between the two villages.FindingsThe villagers perceived the VMWs’ services equally as good as those provided by the health center. After controlling for confounding factors, the following indicators did not show any statistical significance between two villages: community members’ knowledge about malaria transmission (AOR = 0.60, 95% CI = 0.30-1.22) and government-recommended antimalarial (AOR = 0.55, 95% CI = 0.25-1.23), preventive measures taken (Beta = −0.191, p = 0.315), and time before the first treatment (Beta = 0.053, p = 0.721). However, knowledge about malaria symptoms was significantly lower in the village with VMWs than the village with a health center (AOR = 0.40, 95% CI = 0.19-0.83).ConclusionsVMWs played an equivalent role as the health center in promoting malaria knowledge, action, and effective case management. Although VMWs need to enhance community knowledge about malaria symptoms, the current government policy on VMWs is reasonable and should be expanded to other malaria endemic villages.

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BACKGROUND Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is an ontology. OBJECTIVE This paper aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients and disparate heterogeneous infectious diseases disease surveillance. METHODS The (IoT-MIDO) was developed using the Basic Formal Ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. RESULTS We demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: 1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; 2) clinical patient management of infectious diseases; 3) epidemic risk analysis for timely response at the public health level; 4) infectious disease surveillance; and 5) transforming patient information into surveillance information. CONCLUSIONS The development of the IoT-MIDO was driven by competency questions. Being able to answer all of the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance and epidemic risk analysis. The novelty and uniqueness of the ontological model lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems to support public health organisations and practitioners.

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