Third-party System Research Articles

Streamlining Customer Communication through AI-powered Chatbots in Travel Agencies

Abstract: The Travels Management System (TMS) is a comprehensive solution designed to optimize travel operations for both travelers and administrators. It offers an intuitive interface accessible via web and mobile platforms, allowing for easy booking and reservation management. TMS simplifies expense tracking by consolidating all travel-related expenses in one place, enabling administrators to generate detailed reports to analyze travel expenditure and identify cost-saving opportunities. It also helps enforce travel policies by providing alerts and notifications when bookings fall outside established guidelines, ensuring adherence to company policies and budgetary constraints. TMS offers seamless integration with third-party systems, enabling automated data exchange and enhanced functionality. It also includes features for monitoring travel risks, such as weather alerts, political unrest, or health advisories, allowing administrators to proactively address potential risks and ensure traveler safety. TMS can be customized to meet the unique needs of different organizations, with scalable options to accommodate growing businesses and evolving travel requirements. Overall, TMS provides a comprehensive solution for optimizing travel operations, enhancing efficiency, and ensuring compliance with organizational policies, allowing businesses and individuals to focus on their core objectives while enjoying hassle-free travel experiences. IndexTerms– HTML, CSS, Javascript, MySQL, PHP.

The Most Important Types of Cyber Attacks that France is Expected to Face in the Future and the Cyber Security Measures it Must Implement to Protect Critical Infrastructure, Telecommunication Networks and Personal Data

As the digital landscape continues to evolve, nations like France find themselves on the frontline of an escalating cyber warfare scenario. This abstract provides an insightful overview of the most significant types of cyber attacks that France is expected to face in the future, offering a comprehensive analysis based on current trends and emerging technologies. The research delves into the realm of advanced persistent threats (APTs), examining the sophisticated techniques employed by state-sponsored actors and cybercriminal organizations to compromise critical infrastructure, sensitive data, and national security. It explores the evolving landscape of ransomware attacks, focusing on the potential impact on both public and private sectors, and the looming threat of extortion campaigns that could cripple essential services. Furthermore, the abstract sheds light on the rising concern of supply chain attacks, as interconnected global networks make businesses and government entities vulnerable to malicious actors seeking to exploit weaknesses in third-party systems. The research also explores the nuances of social engineering attacks, recognizing the human element as a critical vulnerability that threat actors may exploit through targeted phishing campaigns and manipulation tactics. In addition, the abstract touches upon the growing menace of Internet of Things (IoT) vulnerabilities, acknowledging the increased interconnectivity of devices and the potential for large-scale disruptions. It also considers the implications of emerging technologies such as artificial intelligence and quantum computing in shaping the future threat landscape. By examining these diverse aspects of cyber threats, this abstract aims to provide a forward-looking perspective on the challenges France is likely to encounter in the digital realm. As Cyber security becomes an integral component of national defense, understanding these anticipated threats is crucial for policymakers, security experts, and technology professionals seeking to fortify France’s resilience against the evolving nature of cyber attacks.

HyperFusion: Combating Phishing Attacks through URL, Hyperlink, and User Behavior Analysis

Abstract: Phishing attacks remain a persistent threat in cyberspace, demanding innovative solutions for timely detection. In this paper, HyperFusion, a pioneering approach that combines machine learning with user behavior analysis, is introduced to enhance the real-time identification of phishing websites. By integrating features derived from URL and hyperlink characteristics with dynamic user interaction patterns, HyperFusion achieves unprecedented accuracy without reliance on thirdparty systems. Traditional anti-phishing methods often falter against zero-hour attacks or novel phishing websites, underscoring the need for novel strategies. The methodology addresses this challenge by leveraging user behavior data alongside client-side information, thereby eliminating external dependencies and fortifying defense mechanisms. Additionally, a tailored dataset for rigorous experimentation is presented, facilitating a comprehensive evaluation of the approach. Experimental results demonstrate HyperFusion's superior efficacy, boasting a remarkable detection accuracy of 99.37% with the XG Boost technique.

Enabling the use of digital calibration certificates in industrial calibration management systems

Abstract. Metrology has been a slowly digitalizing field in which a significant part of the handling of data has been dependent on paper-based processes. Due to the need for the improved efficiency and reliability of these processes, digitalization has become a major topic of interest in the metrology community. Data formats such as the digital calibration certificate (DCC) have an essential role as an enabler of further digitalization, acting as the harmonized data format for calibration data. Naturally, introducing these data formats into industrial usage sets new requirements with respect to the calibration management and associated systems. Diversity of metrology also means that the systems need to be flexible and scalable to fulfill the needs of actors in the global calibration infrastructure with very different requirements. This paper presents a conceptual approach for enhancing the communications between the existing systems in a calibration ecosystem and enabling DCC-based data exchange with third-party systems using the Beamex calibration ecosystem as an example. Thus, the presented system architecture concept that introduces data and DCC exchange services would provide a solution to enable the use of DCCs on Beamex systems.

A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response

An intrusion detection system (IDS) perform postcompromise detection of security breaches whenever preventive measures such as firewalls do not avert an attack. However, these systems raise a vast number of alerts that must be analyzed and triaged by security analysts. This process is largely manual, tedious, and time-consuming. Alert correlation is a technique that reduces the number of intrusion alerts by aggregating alerts that are similar in some way. However, the correlation is performed outside the IDS through third-party systems and tools, after the IDS has already generated a high volume of alerts. These third-party systems add to the complexity of security operations. In this paper, we build on the highly researched area of alert and event correlation by developing a novel hierarchical event correlation model that promises to reduce the number of alerts issued by an intrusion detection system. This is achieved by correlating the events before the IDS classifies them. The proposed model takes the best features from similarity and graph-based correlation techniques to deliver an ensemble capability not possible by either approach separately. Further, we propose a correlation process for events rather than alerts as is the case in the current art. We further develop our own correlation and clustering algorithm which is tailor-made to the correlation and clustering of network event data. The model is implemented as a proof of concept with experiments run on standard intrusion detection sets. The correlation achieves an 87% data reduction through aggregation, producing nearly 21,000 clusters in about 30 s.

LEAF: A Federated Learning-Aware Privacy-Preserving Framework for Healthcare Ecosystem

Over the last decades, the healthcare industry has been revolutionized like anything, especially after the Covid-19 surge. Various artificial intelligence approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimum robustness to unseen or untrained data. So, there is a requirement for new techniques which can counterfeit the issues mentioned earlier. Federated learning (FL) can help to make specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making using FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% more than that of the traditional centralized and FL-based systems advantages without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4-5 times.

Enhancing Online Food Delivery Systems through Comprehensive Text Analytics and Strategic Data Integration

Addressing challenges in the online food delivery system involves employing various data analytics techniques. Text Analytics, encompassing web analytics, social media analytics, stream analytics, and geospatial analytics, plays a pivotal role in managing and extracting valuable insights. The use of third-party systems by many companies to meet the demand for online food delivery presents issues related to control. Furthermore, information overload and poorly organized data contribute to observed problems. This research proposes effective data integration as a solution, facilitating strategic analytics for optimal system performance. Proper data sorting enables adaptive planning and priority shifts tailored to customer satisfaction. The framework of data integration is crucial in illustrating the comprehensive analysis of online food delivery systems. The report also delves into the challenges associated with implementing text analytics.

The Stackelberg duel between Central Bank Digital Currencies and private payment titans in China

This study presents an in-depth analysis of the rapidly evolving mobile payment market, focusing on the competition between Central Bank Digital Currencies (CBDCs), as exemplified by China's Digital Currency Electronic Payment (DCEP), and well-established private digital payment platforms. This research builds on economic theories, including Metcalfe's Law and the Capital Asset Pricing Model (CAPM). It employs the Stackelberg game model as a strategic framework to dissect these payment systems' interactions and potential equilibrium outcomes. A notable innovation of this study is the application of advanced simulation techniques, as it uses Matlab2018a to model and predict variations in profit margins contingent on the size of the user base for each payment method. This approach yields three insightful and nonintuitive results that deepen our understanding of market dynamics. First, the study underscores the indispensable role of government support, particularly in the nascent stages of DCEP implementation, as a catalyst for competitiveness against entrenched third-party systems. Without proactive and substantial backing from the government, the DCEP might struggle to gain a foothold in a market dominated by private entities. Second, this study highlights the critical interconnectedness among the various user groups. It posits that for DCEP to effectively penetrate the market and challenge the dominance of third-party platforms, it must strategically align itself within the existing networks of these platforms. This aspect emphasises the importance of understanding and leveraging the complex web of relationships and dependencies within the mobile payment ecosystem. Third, the study identifies mutually beneficial cooperative strategies between the DCEP and third-party platforms. Drawing on the concept of Nash equilibrium, this suggests that collaboration, rather than competition, could lead to optimal outcomes for both parties. This cooperative stance can manifest in various forms, such as shared technological infrastructure, joint ventures, and cross-platform compatibility, ultimately enhancing the value proposition for end users. These findings have significant implications for policymakers, financial regulators, and market strategists, as they suggest that a balanced approach that harmonises the strengths of state-led digital currencies and private payment solutions is crucial for the sustainable development of the digital finance sector. This study also provides strategic insights for CBDCs and private payment platforms, guiding them to make informed decisions in a market increasingly characterised by technological innovation, regulatory shifts, and evolving consumer preferences.

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC).

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.

Serialized Barcode Printing Solution for Drug Traceability: An Architectural Design and Approach

Medicine serialization seems to be a key to both counterfeiting and the complex supply chain of the pharmaceutical industry, including recalls and returns. Though there are countries that use serialization as a legal necessity, it is still a developing topic within the industry. Serialization is not just for anti-counterfeit technology but also to use supply chain and stock management more proficiently. The design of the Barcode is a fundamental requirement as per Healthcare Distribution Alliance (HDA) guidelines for serialized products. Encoding Barcodes on pharmaceutical drugs with critical product attributes is necessary for tracking and validating product sources. Since the serialization guidelines are regulated, small-scale pharmaceutical companies are struggling to comply with regulations due to heavy investments. Printing Serialized labels with barcodes required specialized third-party systems and software. The cost of third-party systems that integrate with ERP and Serialized systems for exchanging data is very expensive. On the other hand, the operational and sustainability costs of these systems will be an additional year-to-year cost for the company.
 Aim: This Study investigated the feasibility of designing a barcode printing solution for drug traceability.
 Material and Method: A case study has been carried out at a USA-based generic pharmaceutical company to print serialized labels using their enterprise Resource planning (ERP) integrated with the packaging line.
 Result: This dynamic solution for serialized label printing saves the company a significant number of resources and money, as they do not need to buy any specialized third-party system to print the label or send back the product to the packaging line.
 Conclusion: In this paper, we implement and validate a dynamic solution to print 2D data matrix and 1D Linear barcodes successfully within an ERP system by eliminating the need for a specialized third-party printing system.

Knowledge representation of the state of a cloud-native application

Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices.

Improving the registration stability of cone-beam computed tomography with the Sphere-Mask Optical Positioning System: a feasibility study.

Cone-beam computed tomography (CBCT) is an important tool for patient positioning in radiotherapy due to its outstanding advantages. However, the CBCT registration shows errors due to the limitations of the automatic registration algorithm and the nonuniqueness of manual verification results. The purpose of this study was to verify the feasibility of using the Sphere-Mask Optical Positioning System (S-M_OPS) to improve the registration stability of CBCT through clinical trials. From November 2021 to February 2022, 28 patients who received intensity-modulated radiotherapy and site verification with CBCT were included in this study. S-M_OPS was used as an independent third-party system to supervise the CBCT registration result in real time. The supervision error was calculated based on the CBCT registration result and using the S-M_OPS registration result as the standard. For the head and neck, patients with a supervision error ≥3 or ≤-3 mm in 1 direction were selected. For the thorax, abdomen, pelvis, or other body parts, patients with a supervision error ≥5 or ≤-5 mm in 1 direction were selected. Then, re-registration was performed for all patients (selected and unselected). The registration errors of CBCT and S-M_OPS were calculated based on the re-registration results as the standard. For selected patients with large supervision errors, CBCT registration errors (mean ± standard deviation) in the latitudinal (LAT; left/right), vertical (VRT; superior/inferior), and longitudinal (LNG; anterior/posterior) directions were 0.90±3.20, -1.70±0.98, and 7.30±2.14 mm, respectively. The S-M_OPS registration errors were 0.40±0.14, 0.32±0.66, and 0.24±1.12 mm in the LAT, VRT, and LNG directions, respectively. For all patients, CBCT registration errors in the LAT, VRT, and LNG directions were 0.39±2.69, -0.82±1.47, and 2.39±2.93 mm, respectively. The S-M_OPS registration errors were -0.25±1.33, 0.55±1.27, and 0.36±1.34 mm for all patients in the LAT, VRT, and LNG directions, respectively. This study shows that S-M_OPS registration offers comparable accuracy to CBCT for daily registration. S-M_OPS, as an independent third-party tool, can prevent large errors in CBCT registration, thereby improving the accuracy and stability of CBCT registration.

InCulture: A Collaborative Platform for Intangible Cultural Heritage Narratives

Intangible Culture Heritage (ICH) is defined as the collection of oral traditions and expressions such as epics, fairy tales, stories, arts, social practices, rituals and celebrations, events, knowledge, and practices related to nature and the universe, traditional medicine, folk medicine, traditional handcrafts, as well as personal experiences related to important historical events or cultural activities that shaped the historical and local identity. Under the UNESCO Convention for the Safeguarding of the ICH, nations are committed to developing inventories of ICH and working with local communities, groups, and individuals to preserve these traditions. In this paper, a platform is introduced that facilitates the collection of intangible ICH data, the formation of story-based narratives, and their presentation to the public via a web and mobile application, which offers Augmented Reality (AR) experiences. The platform aims to support the formation of user communities sharing common interests and to provide them with the appropriate tools for collecting pieces of ICH. Collected ICH resources and created narratives are modeled using semantic web technologies so that information can be perceived by third-party systems too. Furthermore, towards the dissemination of the platform, a real-world use case took place on the island of Rhodes focusing on the recent history of the island between 1912 and 1948 (WWII). The platform was implemented to support the goals of the project InCulture, funded by the EPAnEK Greek national co-funded operational program “Competitiveness Entrepreneurship and Innovation”.

Review of forecasting methods to support photovoltaic predictive maintenance

Predictive maintenance models are thought to be a reliable alternative to costly on-site maintenance techniques in the solar photovoltaic industry. They provide the owners with a third-party system to objectively diagnose and prevent failures in the system or any of its components. These models depend on data acquisition and analysis to compare the estimated performance with the actual energy production of the system, to conclude on the systems' health status. Ambient temperature, cell temperature, and solar irradiance forecasting are crucial parameters in predictive maintenance models’ formulation. These parameters represent the input variables to the model and define its efficiency to a great extent. Nonetheless, the current approaches to predict weather-related parameters have a high dependency on data availability and, in some cases, sensibility to the location where they are being measured. Forecasting climate parameters using neighboring weather stations seems to be a promising option to tackle the cost of on-site sensing and data availability, however, the scarcity of applications of specialized forecasting methods to predict solar irradiance and temperature (cell and ambient) for such applications represents an obstacle. This document will present a review of the state-of-the-art comparing the techniques to forecast solar irradiance, and ambient and cell temperature, and their relationship with predictive maintenance models, by acquiring data from weather stations. This review evaluates the suitability of the models for solar photovoltaic applications, taking on their forecasting horizon, computational cost, and improvement opportunities to propose a framework for future work in the photovoltaic industry.

Hybrid Integration Model in Industry 4.0 for Lean Management (HIM)

In many large organizations, there is an Enterprise Resource Planning (ERP) system that needs to be integrated with a third-party system to provide real-time services to their customers. To achieve this, these organizations’(ERP) systems should have inbound (IB) and outbound (OB) integration capability through APIs. In this paper, we have proposed and implemented a Hybrid Integration Model (HIM) in Industry 4.0 for Lean Management. This research was implemented in an Airline Travel Company (ATC) in which an In-house ERP system is real-time-integrated (In-bound) with Global Distribution System (GDS) through API’s i-e, soap &rest-based web services for online ticketing as well other Miscellaneous facilities i-e (Auto SMS Email) using Horizontal Integration Model in Industry 4.0 and also integrated (Outbound) with 3rd party General Ledger (GL) system. Integrating an ERP system with GL is an automated business process that requires implementing API’s and developing automatic routines to move the data between ERP & GL Systems as per user requirements. Our proposed model is fully automated where no human intervention is involved while transferring data from ERP to GL and following all standards recommended by industry 4.0 for Lean Management.

Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches.

Design and automation of specific geometric quality controls for cone-radiosurgery treatments

PurposeThe Varian TrueBeam STx linac can be equipped with BrainLAB stereotactic cones and ExacTrac imaging system for SRS treatments. However, these two third-party systems lack integration in a self-performance diagnosis tool dedicated to the SRS platform. The aim of this work was to design and automate essential geometric tests considering the complete set of cones with diameter range from 4 to 15 mm. MethodsEPID-based tests were focused on the cone alignment, the radiation isocentricity and the isocenters congruence. Images acquired with or without the BrainLAB pointer were analysed using the Hough transform and morphological filtering operations, after assessment of the algorithm accuracy using simulated images. The new Machine QA program was experimented over one year. ResultsA subpixel resolution of 0.02 mm was found for the circular-field center detection algorithm. The tests results did not depend on the pointer location. The maximum deviations reported were in accordance with the AAPM SRS guidelines. The accurate localization of the linac radiation isocenter allowed for guidance of the ExacTrac calibration. A misalignment reaching 0.2 mm was measured for all cones but one, highlighting the benefit of systematizing this control before each patient SRS treatment. ConclusionAn effective in-house QA program dedicated to SRS cones was developed to supplement the standard machine performance check on our mixed SRS platform. Specific geometric tests even include the smallest 4-mm cone, which could be of great interest for future clinical indications such as the radiosurgery of functional disorders.

Application of Heterogeneous Network Oriented to NoSQL Database in Optimal Postevaluation Indexes of Construction Projects

With the advent of the Internet Web 2.0 era, storage devices used to store website data are developing at an ever-increasing high-growth rate and a diversified trend. The focus on the structured data storage model has reduced the responsiveness of traditional relational databases to data changes. NoSQL database is scalable, has a powerful and flexible data model and a large amount of data, and has an increasing application potential in the memory field. Heterogeneous networks are composed of third-party computers, network equipment, and systems. Network types are usually used for other protocols to support other functions and applications. The research on heterogeneous networks can be traced back to the BARWAN project that started in 1995 at the University of California, Berkeley. The project leader RHKatz merged multiple types of nested networks for the first time to form heterogeneous network requirements for various future terminal services. Construction engineering refers to an engineering entity formed by installing pipelines and equipment that support the construction of various houses and ancillary facilities. “House construction” refers to projects with roofs, beams, columns, walls, and foundations that can form internal spaces to meet people’s needs in production, living, learning, and public activities. Among them, the engineering evaluation index is a statistical index used to evaluate and compare the quality and effects of social and economic activities through the use of equipment, such as capital turnover rate and employee labor efficiency. It is the exchange of corporate performance evaluation content and the expression of corporate performance evaluation content.

Developing a national science & technology funding registry in Portugal

Funding information is one of the pillars of the research information ecosystem. Despite this, it is still very challenging to associate scientific outputs to all the entities that financially support a project and all the stakeholders involved. To tackle such limitation, PTCRIS-a national integrated research information management ecosystem-developed a new infrastructure to support the management of funding attributed to the scientific triad in the Portuguese scientific ecosystem: organizations, people and projects. The SciPROJ database is the new national database that aggregates funding records (national and international) that support science and technology activities carried out in Portugal.The SciPROJ data comes mostly from third-party systems, namely the funding entities issuing the records. Aligned with the best practices, the metadata at SciPROJ is recorded using the CERIF data model. The data profile for sharing SciPROJ's information follows OpenAIRE guidelines. A REST API service has been developed to support sharing and reuse of SciPROJ's information. Regardless of other uses, SciPROJ is currently integrated with the national curriculum management system-CIÊNCIAVITAE-for the automatic import of funding-and with Dimensions, a platform that offers services based on research information integration. Furthermore, it is currently undergoing SciPROJ's integration with OpenAIRE infrastructure, Clarivate and HEI CRIS systems, thereby, consolidating the purpose of promoting sharing and reuse of data. Besides contributing to interoperability, SciPROJ will also be the basis of added value services, such as, services to monitor compliance with Open Access policies, discovery services (Research Portal) and a business intelligence system that aims to support decision and strategic planning in the context of scientific research information.

Full Text Search Setup on a Website

ntroduction. When implementing the search for text fragments on the site, approaches are used that are different in complexity and performance. There is also a sequence of related tasks: choosing a text indexing option, sending a text for indexing, selecting texts for indexing specifically from the CMS database, choosing a search engine, and others. These approaches do not always provide satisfactory search results. Purpose. The purpose of the article is to the description of existing solutions for full-text search on a website, their advantages, and disadvantages. Development of a full-text search algorithm using the Elasticsearch system. Methods. Analysis of approaches to the implementation of full-text search on a website, varying in complexity and performance. Identification of flaws and vulnerabilities in more primitive approaches and the development of more advanced and complex algorithms that eliminate the identified deficiencies. Step-by-step implementation of full-text search using third-party systems. Results. A method for implementing full-text search using Elasticsearch is described. The advantage of the new approach is the asynchronous sending of the page content and its address to a specific service responsible for communication with Elasticsearch. This allows you not to block the normal work with the CMS and not depend on the availability of the indexing service. The approach described in the article is flexible and adaptable for various website architectures. Asynchronous processing of indexing requests ensures high query execution speed and system fault tolerance. Conclusions. The article discusses various approaches to implementing full-text search on a website, their advantages and disadvantages. Based on the analysis, a more flexible and universal approach to the implementation of a full-text search system has been developed. A solution is proposed with step-by-step implementation and setup of advanced full-text search using Elasticsearch.