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Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.

Abstract Intensive conventional farm management, characterized by high agrochemicals input, could alter the composition of microbial communities with potential negative effects on both functional traits and the ecosystem services provided. In this study, we investigated the gut microbial composition of a high ecological relevance carabid Pseudoophonus rufipes, sampled in two fields subjected to conventional and organic management practices. Carabids’ gut microbiota was analyzed via qPCR and NGS. Profound differences between the microbial composition of organic and conventional samples were detected: the abundance of Tenericutes and Proteobacteria was significant higher in organic and conventional samples, respectively. Spiroplasmataceae and Bifidobacteriaceae families were significantly more abundant in samples from organic management, while Enterococcaceae, Morganellaceae and Yersiniaceae were more abundant in samples from conventional management. The diverse gut microbial composition of insects between the two management systems is related to the pressure of environmental stressors and it may representing an important bioindication of ecological functions and services provided by a carabid species.

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework's applicability using an actual usage scenario and left the implementation of the system for future work.

Many companies migrate to microservices because they help deliver value to customers quickly and continuously. However, like any architectural style, microservices are prone to architectural technical debt (ATD), which can be costly if the debts are not timely identified, avoided, or removed. During the early stages of migration, microservice-specific ATDs (MS-ATDs) may accumulate. For example, practitioners may decide to continue using poorly defined APIs in microservices while attempting to maintain compatibility with old functionalities. The riskiest MS-ATDs must be prioritized. Nevertheless, there is limited research regarding the prioritization of MS-ATDs in companies migrating to microservices. This study aims to identify, during migration, which MS-ATDs occur, are the most severe, and are the most challenging to solve. In addition, we propose a way to prioritize these debts. We conducted a multiple exploratory case study of three large companies that were early in the migration process to microservices. We interviewed 47 practitioners with several roles to identify the debts in their contexts. We report the MS-ATDs detected during migration, the MS-ATDs that practitioners estimate to occur in the future, and the MS-ATDs that practitioners report as difficult to solve. We discuss the results in the context of the companies involved in this study. In addition, we used a risk assessment approach to propose a way for prioritizing MS-ATDs. Practitioners from other organizations and researchers may use this approach to provide rankings to help identify and prioritize which MS-ATDs should be avoided or solved in their contexts.

Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.

Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development methods and types of DSLs vary for different applications and objectives. In this study, we conducted a systematic literature review to overview DSL methods and their intended applications for VA systems. Moreover, the review outlines the benefits and limitations of each of these methods. The aim is to provide decision support for both the research and development communities to choose the most compatible approach for their application. We think the communication of this research delivers a broad figure of previous relevant research and assists with the transfer and adaptation of the results to other domains.