Current industry trends in enterprise architectures indicate movement from Service-Oriented Architecture (SOA) to Microservices By understanding the key differences between these two approaches and their features, we can design a more effective Microservice architecture by avoiding SOA pitfalls To do this, we must know why this shift is happening and how key SOA functionality is addressed by key features of the Microservice-based system Unfortunately, Microservices do not address all SOA shortcomings In addition, Microservices introduce new challenges This work provides a detailed analysis of the differences between these two architectures and their features Next, we describe both research and industry perspectives on the strengths and weaknesses of both architectural directions Finally, we perform a systematic mapping study related to Microservice research, identifying interest and challenges in multiple categories from a range of recent research

Contextual understanding of microservice architecture: current and future directions

We analyze how artificial intelligence changes a significant part of the energy sector, the oil and gas industry. We focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to tackle. Basing on the analysis of AI application possibilities and the review of existing applications, we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the industry. We investigate AI approaches and algorithms, as well as the role and availability of data in the segment. Further, we discuss the main non-technical challenges that prevent the intensive application of artificial intelligence in the oil and gas industry, related to data, people, and new forms of collaboration. We also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future (in 5, 10, and 20 years).

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.

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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices‐based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

A microservice architecture is expected to provide a better modularization and management of small and autonomous services. Other expected benefits include increased availability and time to market. There is a growing interest of both industry and academia on streamlining the migration of existing systems to a microservice architecture. However, the success of this migration is largely dependent on the use of appropriate criteria for extracting microservices from a code base. Recent studies indicate the selection and decomposition of microservices represent the main challenge along the migration. Academic techniques tend to support the extraction of microservices with either one or two conventional criteria, namely coupling and cohesion. There is limited knowledge on the criteria actually considered as useful by practitioners. Thus, we have performed an exploratory online survey with 15 specialists experienced on migrating systems to a microservices architecture. In particular, we question the relative usefulness of seven possible criteria for supporting decision-making along microservice extraction. The participants were also questioned about tools they have used, their limitations, and whether the decisions on extracted microservices were considered unsuccessful. Overall, the survey results suggest academic techniques do not totally satisfy the needs of practitioners. Practitioners often need to consider simultaneously at least four dominant criteria as well as their trade-offs to support their decisions. Most practitioners consider existing tooling support insufficient or even irrelevant to support their microservice extraction decisions.

Analysis of the criteria adopted in industry to extract microservices

In order to achieve economies of scale, a Software as a Service (SaaS) should be configurable, multi-tenant efficient, and scalable. But building SaaS with these characteristics comes at a price of having more complex services. Some works in the literature integrate software product line engineering and service-oriented architecture to tackle the complexity of building multi-tenant SaaS. Most of these works focused on centralized approaches that rely on middleware or platforms, but they do not investigate the use of decentralized architectural style. Microservices architecture is an architectural style that relies on small, decentralized, and autonomous services that work together. Thus, this paper investigates the integrated use of microservices architecture and software produt line techniques to develop multi-tenant SaaS. We conducted an empirical study that analyzes the behavior of software reuse during the evolution of a multi-tenant SaaS. This empirical study showed an average software reuse of 62% of lines of code among tenants. We also provide lessons we learned during the the re-engineering and maintenance of such multi-tenant SaaS.

Using Microservices and Software Product Line Engineering to Support Reuse of Evolving Multi-tenant SaaS

Sketching is viewed as an efficient way to design the user interface. However, there are few tools that help designers go from sketching the user interface to simulating its behavior with endusers during early evaluation. We have developed a pen-based tool called UISKEI, which goes beyond allowing designers to define simple navigation between user interface snapshots, into allowing them to define more complex interactive behavior with conditional changes in the state of the user interface. We have conducted two studies on how UISKEI compares to similar prototyping techniques: paper and pencil; widget-based (Balsamiq) and other sketch-based user interface prototyping tools (DENIM and SketchiXML). The studies revealed that UISKEI's sketch-based definition of the user interface behavior for later simulation is better than the analogous mechanisms provided by the other tools.

UISKEI: a sketch-based prototyping tool for defining and evaluating user interface behavior

Effective and efficient strategies are needed to extract unknown and unexpected information from data of unprecedentedly large size, high dimensionality, and complexity [7]. Only a combination of data analysis and visualization techniques can handle these complex and dynamic data [4]. Visual analytics applications aim to integrate the best of both sides.

History Viewer: Displaying User Interaction History in Visual Analytics Applications

Sensor data is detected from at least one sensor selected and installed for detecting operating conditions of at least one equipment. The sensor data includes an operating condition of at least one equipment. The sensor is selected dependent on indications of a user restriction and a predictive model. A sensor data signal is generated dependent on the sensor data. The sensor data signal is transmitted to a network device for collecting the sensor data and transforming the collected sensor data into a formatted transmission signal having a format for transmission over a network to a network server. The network server receives the formatted transmission signal for performing on-demand service of at least one of analysis, reporting and visualization dependent on the operating condition.

On-Demand Remote Predictive Monitoring For Industrial Equipment Analysis and Cost Forecast

Visual analytics applications (VAApps) rely heavily on visual representations and notations to communicate information and support user interaction. The visual representations themselves are the main communication form used by designers, reflecting their interpretation of what data aspects should be highlighted and how users can explore the data. Ultimately, the combination of representation and interactivity guides the user's data analysis in VAApp. To keep the VAApp development centered in their users, the design team must pay special attention to the visual representations and their underlying interaction mechanisms. Our team is developing HistoryViewer, a VAApp itself for users to explore log data obtained from interactions with other ("source") VAApps (VAAppsrc). During the development of HistoryViewer and the instrumentation of a VAAppsrc, we noticed unanticipated benefits for the development of the VAAppsrc itself. This paper presents our HistoryViewer system, proposes an architecture for VAApps integrated with our log model, discussing some ancillary benefits of such instrumentation. Finally, it reports two empirical studies conducted to gather potential users' opinions about HistoryViewer, which revealed positive attitudes towards the system and pointed to opportunities for improvement.

Vinícius C. V. B. Segura

Papers

Using Microservices and Software Product Line Engineering to Support Reuse of Evolving Multi-tenant SaaS

UISKEI: a sketch-based prototyping tool for defining and evaluating user interface behavior

History Viewer: Displaying User Interaction History in Visual Analytics Applications

On-Demand Remote Predictive Monitoring For Industrial Equipment Analysis and Cost Forecast

HistoryViewer: Instrumenting a Visual Analytics Application to Support Revisiting a Session of Interactive Data Analysis