Middleware Solutions Research Articles

Developing Scalable APIs for Data Synchronization in Salesforce Environments

In the modern business landscape, Salesforce has become an essential platform for managing customer relationships, driving sales, and fostering business growth. However, as organizations grow and their data volumes increase, the need for efficient and scalable data synchronization across various systems becomes paramount. Developing scalable APIs for data synchronization in Salesforce environments presents a significant challenge due to the complexity of integrating multiple data sources, ensuring real-time updates, and maintaining data integrity across diverse systems. This paper explores the design and implementation of scalable APIs specifically tailored for data synchronization within Salesforce environments. It delves into the architectural considerations necessary for creating robust, high-performance APIs capable of handling large volumes of data while ensuring consistency and reliability. The discussion begins with an overview of Salesforce’s ecosystem, highlighting the importance of data synchronization in maintaining seamless operations across various departments and systems. A critical component of this study is the examination of different API design patterns that support scalability, such as RESTful services, GraphQL, and event-driven architectures. Each approach is analyzed for its suitability in handling the unique challenges of Salesforce data synchronization, such as managing API limits, optimizing data transfer, and ensuring that data synchronization processes do not disrupt ongoing business activities. The paper also explores the role of middleware solutions, like Enterprise Service Bus (ESB) and API gateways, in facilitating seamless data flow between Salesforce and other systems.

DS-PACK: Tool assembly for the end-to-end support of controlled access human data sharing

The EU General Data Protection Regulation (GDPR) requirements have prompted a shift from centralised controlled access genome-phenome archives to federated models for sharing sensitive human data. In a data-sharing federation, a central node facilitates data discovery; meanwhile, distributed nodes are responsible for handling data access requests, concluding agreements with data users and providing secure access to the data. Research institutions that want to become part of such federations often lack the resources to set up the required controlled access processes. The DS-PACK tool assembly is a reusable, open-source middleware solution that semi-automates controlled access processes end-to-end, from data submission to access. Data protection principles are engraved into all components of the DS-PACK assembly. DS-PACK centralises access control management and distributes access control enforcement with support for data access via cloud-based applications. DS-PACK is in production use at the ELIXIR Luxembourg data hosting platform, combined with an operational model including legal facilitation and data stewardship.

Unlocking Integration Efficiency with Entire-X

EntireX stands as a robust middleware solution, pivotal in facilitating seamless integration and communication between disparate systems and platforms within an enterprise architecture. This abstract delves into the core principles and functionalities of EntireX, elucidating its significance in optimizing integration processes and enhancing operational efficiency

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

Design and Implementation of Data Service Middleware in Earthquake Precursor Instrument Platform

With the gradual development of the earthquake signal early warning system, more and more earthquake precursor signal observation instruments and equipment have been added to the earthquake signal early warning system, which has also caused various compatibility issues. This article designs a data service middleware solution based on B/S architecture for the interaction between earthquake precursor signal observation equipment and remote access terminals on the earthquake precursor instrument platform. Combining embedded technology, it achieves compatible and universal deployment of heterogeneous terminals, and achieves the ideal function of data service middleware. At the same time, in the data service middleware system, real-time data access, presentation, and log upload and download of earthquake precursor instruments are implemented, as well as remote editing of basic information such as instrument IP addresses and station positions, as well as instrument status monitoring and other functions.

Reducing Specimen Rejection Rates Using Concentration-Dependent Hemolysis Rejection Thresholds.

Using middleware solutions, it is possible to implement concentration-dependent analyte-specific hemolysis rejection limits. This makes day-to-day reporting of clinical specimens more efficient and potentially lowers sample rejection rates compared to a "one-size-fits-all" approach (i.e., solely based on a single cutoff provided in the package insert). Hemolysis interference studies were performed at multiple analyte concentrations for three frequently ordered tests. For each assay, concentration-dependent hemolysis rejection limits were designed based on the total allowable error (TAE) for the analyte as well as the clinical significance of such incurred inaccuracy at the respective concentrations. In general, the following rationale was used: if the interference exceeds 10% (or package insert cutoffs), a comment is placed on the result. If the interference exceeds the TAE, the result will not be reported. Reduction in specimen rejection rates were estimated by comparing the incurred specimen rejection rates when package inserts' vs concentration-dependent hemolysis interference limits were applied to a data set in our institute during a three-month period. Concentration-dependent analyte-specific hemolysis rejection thresholds were designed for three commonly ordered assays that are especially susceptible to hemolysis interference. It is estimated that these novel thresholds for aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and direct bilirubin (DBIL) reduced specimen rejection rates from 9.3% to 1.3%, 31.4% to 4.8%, and 19.9% to 7.1%, respectively. Concentration-dependent analyte-specific hemolysis rejection thresholds for three commonly ordered assays can reduce rejection rates without significantly compromising the quality of test results.

Versatile information provisioning in a configure-to-order production environment; a case study

Problems or challenges at operational/tactical levels in configure-to-order production environments are more often than not caused by inadequate information provisioning. Additionally, incomplete, uncertain, and ambiguous information hampers adequate decision making and control. This is especially the case in configure-to-order environments, where there is a high variability in processes. Consequently, standard software solutions often fail to allow for the required flexibility, adaptability, and agility. Moreover, in industry, software solutions (be it PLM, ERP, IoT, …, MES or CAQ solutions), oftentimes tend to become dominant factors in primary processes, thus further stifling the anticipated flexibility. If, however, these software solutions – here conjointly referred to as Digital Infrastructure – are regarded as subcontractors in a digital twinning approach, the primary targets in production/assembly can remain the driver in the overall process. This paper depicts how this digital twinning approach can be used to establish versatile and adaptive information provisioning. To ensure that the approach is not merely a theoretical discourse, the approach is depicted based on a case study. This case study, embedded in a Dutch manufacturing company for special needs bicycles, is rooted in e.g., the seasonal fluctuation in the market, on the broad product portfolio, but foremost on the ability to strengthen customisation as the unique selling point in that portfolio. For this reason, a use case focuses especially on one of the most demanding parts of the production/assembly-line: the welding robot digital twin for frame welding. On the one hand, this twin encounters all product variants and exceptions; moreover, this twinning approach will aim to provide decision support for upscaling of the production facilities. Next to this specific application, the paper also addresses the middleware solution that is under development to integrate individual applications in the bigger company context and strategy.

Low Latency Reliable Data Sharing Mechanism for UAV Swarm Missions

The use of Unmanned Aerial Vehicle (UAV) swarms is increasing in many commercial applications as well as military applications (such as reconnaissance missions, search and rescue missions). Autonomous UAV swarm systems rely on multi-node interhost communication, which is used in coordination for complex tasks. Reliability and low latency in data transfer play an important role in the maintenance of UAV coordination for these tasks. In these applications, the control of UAVs is performed by autonomous software and any failure in data reception may have catastrophic consequences. On the other hand, there are lots of factors that affect communication link performance such as path loss, interference, etc. in communication technology (WIFI, 5G, etc.), transport layer protocol, network topology, and so on. Therefore, the necessity of reliable and low latency data sharing mechanisms among UAVs comes into prominence gradually. This paper examines available middleware solutions, transport layer protocols, and data serialization formats. Based on evaluation results, this research proposes a middleware concept for mobile wireless networks like UAV swarm systems.

SOME/IP Intrusion Detection System Using Machine Learning

With the development of in-vehicle network technologies, Automotive Ethernet is being applied to modern vehicles. Scalable service-Oriented MiddlewarE over IP (SOME/IP) is an automotive middleware solution that is used for communications of the infotainment domain as well as that of other domains in the vehicle. However, since SOME/IP lacks security, it is vulnerable to a variety of network-based attacks. In this paper, we introduce a new type of intrusion detection system (IDS) leveraging on SOME/IP packet's header information and packet reception time to deal with SOME/IP related network attacks.

Internet of Things Middleware: Evaluating the Costs of Service-Based Solutions Under Different Application Scenarios

The rapid evolution of technology has paved the way for the many applications of the Internet of Things (IoT). As IoT systems become increasingly complex, new technologies that simplify the development of the IoT are emerging, for example, IoT cloud platforms have gained popularity as tools that reduce development time and cut costs. Recent surveys indicate that only a few IoT projects have had successful outcomes. Among the main reasons for this, there is the lack of knowledge regarding IoT design and architecture. Numerous research studies have tried to address the problem of choosing the right cloud platform based on specific requirements, but none of the reviewed studies has proposed a cost-benefit analysis of the current available cloud solutions for the IoT. Here, the IoT has been studied from a decision-maker’s perspective, giving a brief but concise introduction to modern IoT architecture and IoT middleware and evaluating the costs of four popular IoT cloud middleware solutions under different project requirements and design scenarios.

DIFFUSE: A DIstributed and decentralized platForm enabling Function composition in Serverless Environments

Serverless computing is an emerging proposition in the cloud offering landscape that promotes a higher level of abstraction, further decoupling software operations from the underlying hardware. Often recognized as an economically driven computational approach, the serverless model relies on the execution of short-lived stateless functions, enabling a fine-grained accounting and control of resources. In this context, function composition represents an appealing feature, allowing the composition of two or more functions to create tailored processing pipelines, incentivizing modularity and reusability of functions, while paving the way to application-specific run-time optimizations. This work presents DIFFUSE: a DIstributed and decentralized platForm enabling Function composition in Serverless Environments. DIFFUSE embodies an innovative infrastructural support, enabling the efficient and transparent composition of functions by relying on pluggable middleware support, serving as a conveyor of messages among the platform components. Broadening the deployment spectrum of our proposal, we assess different middleware solutions, each presenting distinct delivery profiles, evidencing the tradeoffs that emerge.

A Framework for Rapid Robotic Application Development for Citizen Developers

It is common knowledge among computer scientists and software engineers that ”building robotics systems is hard”: it includes applied and specialized knowledge from various scientific fields, such as mechanical, electrical and computer engineering, computer science and physics, among others. To expedite the development of robots, a significant number of robotics-oriented middleware solutions and frameworks exist that provide high-level functionality for the implementation of the in-robot software stack, such as ready-to-use algorithms and sensor/actuator drivers. While the aforementioned focus is on the implementation of the core functionalities and control layer of robots, these specialized tools still require extensive training, while not providing the envisaged freedom in design choices. In this paper, we discuss most of the robotics software development methodologies and frameworks, analyze the way robotics applications are built and propose a new resource-oriented architecture towards the rapid development of robot-agnostic applications. The contribution of our work is a methodology and a model-based middleware that can be used to provide remote robot-agnostic interfaces. Such interfaces may support robotics application development from citizen developers by reducing hand-coding and technical knowledge requirements. This way, non-robotics experts will be able to integrate and use robotics in a wide range of application domains, such as healthcare, home assistance, home automation and cyber–physical systems in general.

Editorial

It is with great pleasure that I welcome you to our first regular issue in 2022, which contains 5 very relevant and new articles on various topics in computer science. Looking back over the past year, we had a successful relaunch of the J.UCS platform hosted by Pensoft Publishers Ltd. on their ARPHA Publishing Platform. Thanks to the combined efforts of the Pensoft team and the J.UCS publishing team, we are listed and indexed in more than 40 indexing services worldwide, including DOAJ, Web of Science, and Scopus. Increased visibility and social media presence have also continued to increase page views and article downloads, as well as the number of articles submitted and special issue proposals. We are also very proud to report that the journal's Impact Factor continued to improve. The Web of Science Impact Factor increased to 1.139 and the Scopus Science Score to 2.0. We are proud to present a total of 12 issues with 64 articles on new aspects of various topics in computer science; more precisely, 40 articles were published in 6 special issues and 24 articles in 6 regular issues. These great achievements were only possible through the commitment and interest of the community, the valuable support of the Editorial Board, and the support of the members of the J.UCS Consortium. In particular, I would like to thank Dr. Ulrike Krießmann from the Library of the Graz University of Technology, Prof. Klaus Tochtermann from the ZBW, Prof. Christian Eckhardt from California Polytechnic State University, and Prof. Krzysztof Pietroszek from the American University in Washington DC for their generous support in offering an open-content journal without charging authors for their articles. I would also like to thank the J.UCS team, Johanna Zeisberg for taking care of the publication process, Aleksandar Bobic for his social media support, and Alexander Nussbaumer for his technical support, as well as Pensoft Publishers Ltd. for hosting our journal. I look forward to continuing to work with our editors, editorial team and technical support to maintain the success of J.UCS. I would be very grateful for suggestions and feedback on how we can make J.UCS even better and develop it further in the future. In this regular issue, I am very pleased to introduce 5 accepted articles from 5 different countries. Roberto Cavicchioli, Riccardo Martoglia and Micaela Verucchi from Italy report on an innovative framework aiming to facilitate the design of advanced Big Data analytics workflows for smart cities. Sébastien Martinez, Christophe Gransart, Olivier Stienne, Virginie Deniau, and Philippe Bon from France focus in their article on aspects of dynamic software updating, specifically they present SoREn - Security REconfigurable Engine – in the context of moving vehicles. In their article, Rodolfo Medeiros, Sílvio Fernandes, and Paulo G. G. Queiroz from Brazil perform a systematic review of the literature to bring together middleware solutions for the Internet of Things, identify the requirements and communication protocols used, and finally point out some gaps and directions for future research in IoT middleware development. Canan Tastimur and Erhan Akin form Turkey focus on the challenging problem of classifying highly similar objects by exploring the Siamese Convolution Neural Network, a similarity measurement-based network, and applying it to classify different types of screws, nuts, and bolts. In a research collaboration between Argentina and Brazil, Florencia Vega, Guillermo Rodríguez, Fabio Rocha and Rodrigo Pereira dos Santos present and discuss Scrum Watch, a tool for monitoring the performance of Scrum-based work teams.

Middleware for the Internet of Things: a systematic literature review

The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.

Integrating SAP SD with Third-Party Applications for Enhanced EDI and IDOC Communication

This research paper explores the integration of SAP Sales and Distribution (SD) module with third-party applications to enhance Electronic Data Interchange (EDI) and Intermediate Document (IDOC) communication. As businesses increasingly rely on seamless data exchange for efficient operations, the ability to integrate SAP SD with external systems becomes crucial for optimizing supply chain processes. The study examines current challenges faced by organizations in achieving effective EDI and IDOC communication, including data inconsistency, interoperability issues, and delays in processing transactions. Through a comprehensive analysis of integration methodologies, including middleware solutions and direct API connections, the paper identifies best practices for facilitating real-time data exchange between SAP SD and third-party applications. Case studies of successful implementations demonstrate the positive impact on order processing speed, accuracy, and overall customer satisfaction. Additionally, the research highlights the role of emerging technologies, such as cloud computing and machine learning, in streamlining integration efforts and enhancing the reliability of EDI transactions. By providing actionable insights and practical recommendations, this study aims to equip organizations with the knowledge needed to leverage SAP SD integration effectively, thereby improving operational efficiency and fostering stronger partnerships with suppliers and customers. Ultimately, the findings underscore the importance of robust EDI and IDOC communication in achieving a competitive advantage in today’s dynamic business environment.

A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications.

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

OWebSync: Seamless Synchronization of Distributed Web Clients

Many enterprise software services are adopting a fully web-based architecture for both internal line-of-business applications and for online customer-facing applications. Although wireless connections are becoming more ubiquitous and faster, mobile employees and customers are often offline due to expected or unexpected network disruptions. Nevertheless, continuous operation of the software is expected. This article presents OWebSync: a web-based middleware for data synchronization in interactive groupware with fast resynchronization of offline clients and continuous, interactive synchronization of online clients. To automatically resolve conflicts, OWebSync implements a fine-grained data synchronization model and leverages state-based Conflict-free Replicated Data Types. This middleware uses Merkle-trees embedded in the tree-structured data and virtual Merkle-tree levels to achieve the required interactive performance. Our comparative evaluation with available operation-based and delta-state-based middleware solutions shows that OWebSync is especially better in operating in and recovering from offline settings and network disruptions. In addition, OWebSync scales more efficiently over time, as it does not store version vectors or other meta-data for all past clients.

COMPARE

Data analysis often involves comparing subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques that exploit the semantics of COMPARE to significantly improve the performance of such queries. We have implemented these extensions inside Microsoft SQL Server, a commercial DBMS engine. Our extensive evaluation on synthetic and real-world datasets shows that COMPARE results in a significant speedup over existing approaches, including physical plans generated by today's database systems, user-defined functions (UDFs), as well as middleware solutions that compare subsets outside the databases.

A Service-Oriented Real-Time Communication Scheme for AUTOSAR Adaptive Using OPC UA and Time-Sensitive Networking.

The transportation industry is facing major challenges that come along with innovative trends like autonomous driving. Due to the growing amount of network participants, smart sensors, and mixed-critical data, scalability and interoperability have become key factors of cost-efficient vehicle engineering. One solution to overcome these challenges is the AUTOSAR Adaptive software platform. Its service-oriented communication methodology allows a standardized data exchange that is not bound to a specific middleware protocol. OPC UA is a communication standard that is well-established in modern industrial automation. In addition to its Client–Server communication pattern, the newly released Publish–Subscribe (PubSub) architecture promotes scalability. PubSub is designed to work in conjunction with Time-Sensitive Networking (TSN), a collection of standards that add real-time aspects to standard Ethernet networks. TSN allows services with different requirements to share a single physical network. In this paper, we specify an integration approach of AUTOSAR Adaptive, OPC UA, and TSN. It combines the benefits of these three technologies to provide deterministic high-speed communication. Our main contribution is the architecture for the binding between Adaptive Platform and OPC UA. With a prototypical implementation, we prove that a combination of OPC UA Client–Server and PubSub qualifies as a middleware solution for service-oriented communication in AUTOSAR.

A Survey on Object Detection for the Internet of Multimedia Things (IoMT) using Deep Learning and Event-based Middleware: Approaches, Challenges, and Future Directions

An enormous amount of sensing devices (scalar or multimedia) collect and generate information (in the form of events) over the Internet of Things (IoT). Present research on IoT mainly focus on the processing of scalar sensor data events and barely considers the challenges posed by multimedia based events. In this paper, we systematically review the existing solutions available for the Internet of Multimedia Things (IoMT) by analyzing sensing, networking, service, and application-level services provided by IoT. We present state-of-the-art event-based middleware methods and their suitability for multimedia event processing methods. We observe that existing IoT event-based middleware solutions focus on structured (scalar) events and possess only domain-specific characteristics for unstructured (multimedia) events. A case study for object detection is also presented to demonstrate the requirements associated with the processing of multimedia events within smart cities, even with common image recognition based applications. In order to validate the existing issues in the detection of objects, we also presented an evaluation of object detection models using existing datasets. At the end of each section, we shed light on trends, gaps, and possible solutions based on our analysis, experiments, and review of the existing research. Finally, we summarize the challenges and future research directions for the generalized multimedia event processing (by taking detection of each and every object as an example) based on applications using IoMT. Our experiments demonstrate that existing models are very slow to respond to any unseen class, and existing rich datasets do not have a sufficient number of classes to meet the requirements of real-time applications of smart cities. We show that although there is a significantly large technical literature on IoT, and research on IoMT is also quite actively growing, there have not been much research efforts directed towards the processing of multimedia events. As an example, although deep learning techniques have been shown to achieve impressive performance in applications like image recognition, the methods are deficient in detecting new (previously unseen) objects for multimedia based applications in smart cities. In light of these facts, it becomes imperative to conduct research on bringing together the abilities of event-based middleware for IoMT, and low response-time based online training and adaptation techniques.