Academic literature on the topic 'Email-Driven Processes'

This paper explores the transformative impact of AI-driven automation on administrative processes, emphasizing the dual objectives of enhancing efficiency and accuracy. Through an in-depth examination of various applications, from document management to dynamic task prioritization, the study showcases how artificial intelligence can revolutionize traditional workflows. Special attention is given to the integration of natural language processing for email triage, virtual assistants for administrative support, and facial recognition for secure access control. The implementation of predictive analytics, sentiment analysis, and predictive maintenance further contributes to the paper’s focus on predictive decision-making and improved resource allocation. The abstract underscores the pivotal role of AI in meeting contemporary administrative challenges, offering solutions that streamline tasks, reduce errors, and optimize resource utilization. Additionally, the paper addresses ethical considerations associated with AI implementation and highlights the need for a balanced approach that aligns technological advancements with organizational goals. In essence, thisresearch provides a comprehensive overview of how AI can be harnessed to reshape administrative landscapes, fostering heightened efficiency and accuracy in contemporary workplaces.

In this article, we examine how the Putin government is attempting to respond and adapt to the YouTube phenomenon and the vibrant oppositional online visual culture on Runet. We show how these processes are giving rise to new forms of state propaganda, shaped and driven above all by the quest for high-ranking search-engine results and the concomitant desire to appeal to the perceived new sensibilities of the Internet generation through the commissioning and production of “viral videos.” We focus in particular on the videos created by Iurii Degtiarev, a pioneer in the development of this genre, whose works we explore in light of the “Kremlingate” email leaks, which offer inside information on the strategies and aims being pursued on the online visual front of the campaign to manage the Russian mediascape, and Degtiarev's own reflections on this subject. Examining the output of young creatives patronized by the Kremlin offers a “bottom-up” view to supplement studies of the Russian ideological and media landscape as shaped by “political technologists” such as Vladislav Surkov and Gleb Pavlovskii.

Abstract: Floods pose significant threats to communities worldwide, necessitating the development of efficient monitoring and alerting systems. This abstract presents an IoT-based Flood Monitoring and Alerting System that leverages the power of LoRaWAN (Long Range Wide Area Network) technology to provide real-time flood detection, monitoring, and timely alerts. The system consists of strategically placed water level sensors that use ultrasonic or pressure-based technology to accurately measure water levels. The sensor data is wirelessly transmitted to a central gateway through the LoRaWAN network, known for its long-range and low-power capabilities. A cloud-based platform processes and analyzes the collected data, identifying abnormal water level patterns and potential flood conditions. In case of a flood, the system triggers alerts via mobile notifications, email, and SMS to relevant authorities and stakeholders. Users can access real-time flood information, historical data, and visualizations through a user-friendly web or mobile interface. The system offers cost-effectiveness, extended range, low power consumption, early flood detection, timely response, data-driven decision-making, and has a brighter potential for future enhancements

Message Oriented Middleware (MOM) is an enabling technology for modern event-driven applications that are typically based on publish/subscribe communication (Eugster, 2003). Enterprises typically contain hundreds of applications operating in environments with diverse databases and operating systems. Integration of these applications is required to coordinate the business process. Unfortunately, this is no easy task. Enterprise Integration, according to the authors in (Brosey et al, 2001), "aims to connect and combines people, processes, systems, and technologies to ensure that the right people and the right processes have the right information and the right resources at the right time”. Communication between different applications can be achieved by using synchronous and asynchronous communication tools. In synchronous communication, both parties involved must be online (for example, a telephone call), whereas in asynchronous communication, only one member needs to be online (email). Middleware is software that helps two applications communicate with one another. Remote Procedure Calls (RPC) and Object Request Brokers (ORB) are two types of synchronous middleware—when they send a request they must wait for an immediate reply. This can decrease an application’s performance when there is no need for synchronous communication. Even though asynchronous distributed messaging using message oriented middleware is widely used in industry, there is not enough work done in evaluating the performance of various open source Message oriented middleware. The objective of this work was to benchmark and evaluate three different open source MOM’s performance in publish/subscribe and point-to-point domains, and provide a functional comparison and qualitative study from developers perspective.

In our planned study, we shall empirically study the assessment of cited papers within the framework of the anchoring-and-adjustment heuristic. We are interested in the question whether citation decisions are (mainly) driven by the quality of cited references. The design of our study is oriented towards the study by Teplitskiy, Duede [10]. We shall undertake a survey of corresponding authors with an available email address in the Web of Science database. The authors are asked to assess the quality of papers that they cited in previous papers. Some authors will be assigned to three treatment groups that receive further information alongside the cited paper: citation information, information on the publishing journal (journal impact factor), or a numerical access code to enter the survey. The control group will not receive any further numerical information. In the statistical analyses, we estimate how (strongly) the quality assessments of the cited papers are adjusted by the respondents to the anchor value (citation, journal, or access code). Thus, we are interested in whether possible adjustments in the assessments can not only be produced by quality-related information (citation or journal), but also by numbers that are not related to quality, i.e. the access code. The results of the study may have important implications for quality assessments of papers by researchers and the role of numbers, citations, and journal metrics in assessment processes.

This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.

This study applied a hermeneutic phenomenological approach to better understand pharmacy workplace wellbeing and resilience using respondents’ written comments along with a blend of the researchers’ understanding of the phenomenon and the published literature. Our goal was to apply this understanding to recommendations for the pharmacy workforce and corresponding future research. Data were obtained from the 2021 APhA/NASPA National State-Based Pharmacy Workplace Survey, launched in the United States in April 2021. Promotion of the online survey to pharmacy personnel was accomplished through social media, email, and online periodicals. Responses continued to be received through the end of 2021. A data file containing 6973 responses was downloaded on 7 January 2022 for analysis. Usable responses were from those who wrote an in-depth comment detailing stories and experiences related to pharmacy workplace and resilience. There were 614 respondents who wrote such comments. The findings revealed that business models driven by mechanized assembly line processes, business metrics that supersede patient outcomes, and reduction of pharmacy personnel’s professional judgement have contributed to the decline in the experience of providing patient care in today’s health systems. The portrait of respondents’ lived experiences regarding pharmacy workplace wellbeing and resilience was beyond the individual level and revealed the need for systems change. We propose several areas for expanded inquiry in this domain: (1) shared trauma, (2) professional responsibility and autonomy, (3) learned subjection, (4) moral injury and moral distress, (5) sociocultural effects, and (6) health systems change.

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

Salesforce Analytics is a strong business intelligence (BI) solution that turns raw data into actionable insights. Today's data-driven world requires fast, reliable data analysis for business choices. Salesforce Analytics' broad range of solutions helps organizations use data for better decision-making, operational efficiency, and strategic planning. Integration of Salesforce Analytics into company processes has several benefits. First, it gives organizations real-time knowledge to adapt quickly to market developments and client requests. Organizations may track KPIs and trends using customisable dashboards, automatic reporting, and predictive analytics. Real-time visibility empowers decision-makers to prevent concerns from becoming major ones. Salesforce Analytics' unified data access and analysis platform improves departmental cooperation. Team members may easily exchange insights and reports, breaking down silos and promoting data-driven culture. On-demand report creation and sharing guarantees that all stakeholders have the same information, resulting in better aligned and informed decision-making. Customer relationship management is another important Salesforce Analytics function. The software analyzes touchpoint data to help companies understand their consumers. Businesses may detect client preferences, forecast behavior, and tailor marketing by using this data, improving customer happiness and loyalty. Salesforce's AI-powered analytics help foresee client wants and personalize offerings. Salesforce Analytics also improves forecasting and planning. Organizations may forecast future performance better by evaluating previous data and patterns. This skill is crucial in sales forecasting, inventory management, and financial planning, where precise projections ensure operational efficiency and profitability. Another benefit of Salesforce Analytics is third-party data integration. To get a complete picture of their operations, businesses may mix data from social media, email marketing, and e-commerce platforms. Integration improves strategic choices and corporate results by enabling more complete analysis. The platform's versatility lets analytics tools be customized to match corporate demands, boosting its BI solution value. Data security is crucial in the digital era, and Salesforce Analytics takes it seriously. The platform protects critical company data using encryption, access restrictions, and industry standards. Businesses may use their data confidently because of this security assurance. Finally, Salesforce Analytics improves business intelligence by offering real-time insights, collaboration, customer knowledge, and precise forecasting and planning. In today's fast-paced corporate world, its ability to interact with third-party data sources and secure data analysis makes it a useful tool. Salesforce Analytics will allow data-driven decision-making and strategic planning to help companies accomplish their objectives and expand sustainably as they navigate the contemporary market.

Abstract Background Fatigue, pain and urgency are among the most commonly reported and burdensome symptoms of inflammatory bowel disease (IBD). A disconnect between symptoms and inflammation has been documented and medical management does not always adequately resolve symptoms. Extensive research shows the relationship between IBD symptoms and psychosocial factors. This poster describes the development of a facilitator-supported, theory-driven, tailored web-based intervention for fatigue, pain and urgency in IBD. Methods The Medical Research Council (MRC) guidance and the person-based approach were used to guide intervention development. Literature reviews of psychosocial factors related to fatigue, pain and urgency in IBD and trials of behavioural interventions were used to create a cognitive-behavioural model of symptom perpetuation and impact. The model was tested and refined in large cross-sectional and qualitative studies to understand patients’ experiences of these symptoms and intervention needs. The refined model was mapped onto an intervention logic model to define the psychosocial processes to target in intervention techniques. Patient feedback on the logic model and session content was obtained. Usability of the website was assessed using think-aloud methods and survey data were collected on session content, design and functionality. Results 87 people with IBD and 68 IBD nurses participated in Patient and Public Involvement activities for intervention development. Five interviews were carried out to develop guiding principles and two focus groups provided feedback on a logic model and session plan. 54 people with IBD and 45 IBD nurses completed an initial discovery online survey. Results indicated preferences to receive facilitator support via email/online-messages rather than telephone. Five focus groups included 68 IBD-nurses to assess barriers/facilitators in supporting the intervention. Desirable functionalities included diagrams/aids, email reminders and links to external resources. 31 people with IBD were included in feasibility and acceptability testing. The final intervention includes 8 core sessions with tasks and 4 symptom-specific sessions, and facilitator support of one 30-minute call and in-site messaging. Core to all sessions is understanding and ‘breaking’ personal ‘vicious cycles’ of symptom interference. Conclusion We have used a person-based approach and systematic application of theory, evidence and stakeholder involvement to guide intervention development. BOOST is the first web-based intervention with the primary aim of targeting fatigue, pain and urgency and improving the quality of life of people with IBD. This is now being tested in a large randomised controlled trial.

La gestion des processus métier (BPM) est cruciale pour toute organisation cherchant à améliorer constamment ses opérations. Cela implique plusieurs étapes : conception, modélisation, exécution, surveillance, optimisation et automatisation. Un élément central du BPM est l'analyse des processus, qui consiste à examiner les traces d'exécution pour identifier les inefficacités et les déviations par rapport aux processus prévus. Cette analyse se concentre particulièrement sur la prédiction des processus futurs et sur la vérification de leur conformité. Dans cette thèse, nous nous penchons sur les défis spécifiques à l'analyse des processus métier lorsqu'ils sont pilotés par courriel. Il est essentiel de maîtriser ces pratiques pour rationaliser les opérations et maximiser la productivité. La vérification de conformité garantit que les processus réels respectent les modèles prédéfinis, assurant ainsi le respect des normes et standards. Par ailleurs, la prédiction des processus permet d'anticiper le comportement futur des opérations en se basant sur des données historiques, ce qui aide à optimiser l'utilisation des ressources et à gérer efficacement les charges de travail. Appliquer ces techniques aux processus pilotés par courriel présente des défis uniques. En effet, ces processus manquent souvent des modèles formels trouvés dans les systèmes BPM traditionnels, ce qui nécessite des méthodologies adaptées. Les traces d'exécution dérivées des courriels ont une structure particulière, comprenant des attributs tels que les actes de parole des interlocuteurs et les données commerciales pertinentes. Cette complexité rend l'application des méthodes standard de fouille des processus plus difficile. L'intégration de ces attributs dans les techniques existantes de BPM et les systèmes de courriel demande des algorithmes avancés et une personnalisation importante, d'autant plus que le contexte des communications par courriel est souvent dynamique. Pour relever ces défis, cette thèse propose plusieurs objectifs. D'abord, mettre en place une vérification de conformité multi-aspects et concevoir un système de recommandation de réponse par courriel qui tient compte des activités du processus. Ensuite, il s'agit de concevoir un modèle de processus basé sur des contraintes séquentielles et contextuelles spécifiées par un analyste/expert en données. Il est également crucial de développer des algorithmes pour identifier les événements conformes et non conformes, d'utiliser les traces d'exécution pour prédire les connaissances des processus métier et de proposer des modèles de réponse par courrier électronique. Les principes directeurs de cette approche sont la sensibilité au contexte, l'interdisciplinarité, la cohérence, l'automatisation et l'intégration. L'une des contributions majeures de cette étude est le développement d'un logiciel complet pour l'analyse des processus pilotés par courriel. Ce programme combine la prédiction des processus et la vérification de conformité pour améliorer la communication par courriel. Il propose des modèles de réponse adaptés et évalue la conformité des courriels avant leur envoi. Pour valider ce logiciel, des données de courriels réels ont été utilisées, fournissant ainsi une base pratique pour des comparaisons et des recherches futures
Business Process Management (BPM) involves continuous improvement through stages such as design, modeling, execution, monitoring, optimization, and automation. A key aspect of BPM is Business Process (BP) mining, which analyzes event logs to identify process inefficiencies and deviations, focusing on process prediction and conformance checking. This thesis explores the challenges of BP mining within email-driven processes, which are essential for streamlining operations and maximizing productivity.Conformance checking ensures that actual process execution aligns with predicted models, maintaining adherence to predefined standards. Process prediction forecasts future behavior based on historical data, aiding in resource optimization and workload management. Applying these techniques to email-driven processes presents unique challenges, as these processes lack the formal models found in traditional BPM systems and thus require tailored methodologies.The unique structure of email-derived event logs, featuring attributes such as interlocutor speech acts and relevant business data, complicates the application of standard BP mining methods. Integrating these attributes into existing business process techniques and email systems demands advanced algorithms and substantial customization, further complicated by the dynamic context of email communications.To address these challenges, this thesis aims to implement multi-perspective conformance checking and develop a process-activity-aware email response recommendation system. This involves creating a process model based on sequential and contextual constraints specified by a data analyst/expert, developing algorithms to identify fulfilling and violating events, leveraging event logs to predict BP knowledge, and recommending email response templates. The guiding principles include context sensitivity, interdisciplinarity, consistency, automation, and integration.The contributions of this research include a comprehensive framework for analyzing email-driven processes, combining process prediction and conformance checking to enhance email communication by suggesting appropriate response templates and evaluating emails for conformance before sending. Validation is achieved through real email datasets, providing a practical basis for comparison and future research

AbstractOn the eve of spring break March 2020, we received an emergency email from Purdue University’s Office of Teaching and Learning. We were invited to be part of the University’s contingency team planning for and developing resources to assist faculty with transitioning from face-to-face to emergency remote teaching and learning. Using the experience and expertise we have in the online learning realm, we were tasked to develop resources covering the most essential topics faculty needed for emergency remote teaching. Seven topics were selected based on the Community of Inquiry framework and the needs shared by our colleagues: (1) Availability and Communication, (2) Discussion Board Tips, (3) Setting Student Expectations, (4) Building Community, (5) Delivering Course Materials, (6) Adding Technology to Your Toolkit, and (7) Checking for Student Understanding. Recognizing the recommended resources and strategies needed to be relatable and replicable to earn faculty’s buy-in, we used the snowball sampling method to identify faculty from across campus who could help achieve this goal. This process gave us the opportunity to get to know our colleagues, uncover their talents, and create a new platform for shared expertise in teaching and learning. This book chapter will discuss how we structured and developed the resources for faculty by including them in the process. Further, it will show how faculty driven approach can augment the collaboration between the administration and faculty to enhance teaching and learning.

To create a campaign blueprint, data-driven marketing optimizes client details. To comprehend our clients entails gathering complicated data through online and physical channels and evaluating it. Through the collection and analysis of information, advertisers create and execute highly customized advertising efforts. Digital advertising is an essential tool for advertising tactics because it's a great way to promote and sell products as well as raise brand knowledge and visibility. Even with the rise of online communities and networking sites, marketing through email remains the most popular way to educate, impact, and create business opportunities. Big Data is thought to revolutionize corporate intelligence, a discipline that depends on statistical analysis to gain understanding and improve making choices. Although the idea of big data is not exclusive to advertising or trade, the growth of e-commerce and online advertising has been crucial in raising awareness of the problem. These industries indeed produce enormous amounts of data by nature that need to be processed.

Abstract Our objective is to streamline the identification and evaluation of potential workover candidates in ADNOC's dynamic drilling and workover schedule. We address challenges related to timing issues in email communications and the need for efficient decision-making amidst many rigs. Our platform offers two workflows, involving all stakeholders, to secure well candidates efficiently and ensure seamless technology implementation, fostering operational excellence and comprehensive records for future reference. This paper presents the comprehensive implementation and subsequent outcomes of ADNOC’s (Abu Dhabi National Oil Company) dual workflow platform for selecting workover candidates, emphasizing enhanced operational efficiency, safety, and alignment with strategic objectives. The dual workflows consist of two primary processes: a rigorous validation stage initiated by the Well Integrity team, involving extensive stakeholder engagement, and an endorsement phase for workover proposals, ensuring alignment with Drilling and Well Services (DWS) objectives. The first workflow involves a rigorous validation process conducted by the Well Integrity team, engaging all relevant stakeholders. This multi-stage process includes detailed assessments and diagnostics to evaluate the well’s structural integrity, pressure containment, and overall health. Advanced technologies, such as wellbore imaging tools and real-time data analytics, are employed to identify potential issues and ensure that only wells needing intervention are selected. This thorough validation minimizes downtime and optimizes resource allocation, enhancing operational efficiency. The second workflow focuses on the endorsement of workover proposals by stakeholders from various departments, including engineering, safety, operations, and finance. This multidisciplinary review ensures that the workover plan is technically sound, economically viable, and aligned with ADNOC’s strategic goals. The endorsement process involves several key steps: identification of wells requiring intervention, comprehensive validation, multidisciplinary review, and approval of the workover plan, followed by its execution and continuous monitoring. The implementation phase is meticulously planned, leveraging advanced technologies to optimize operations, reduce risks, and enhance outcomes. Continuous monitoring allows for real-time adjustments, ensuring that operations stay on track and meet their objectives. The integration of technologies such as automated monitoring systems further improves operational efficiency and safety. The dual workflows have led to significant improvements in various areas: **Operational Efficiency**: The systematic validation and endorsement processes have reduced unnecessary workovers and optimized resource allocation, resulting in substantial cost savings and more effective use of resources.**Safety and Integrity**: The comprehensive validation stages have proactively identified and mitigated potential issues, reducing the risk of well failures and other safety hazards, thereby ensuring a safer working environment.**Stakeholder Collaboration**: Engaging all relevant stakeholders in the decision-making process has fostered a culture of collaboration, improving the quality of decisions and strengthening inter-departmental relationships.**Technological Innovation**: The integration of advanced technologies has enhanced the accuracy and efficiency of workover operations, enabling precise diagnostics, real-time monitoring, and data-driven decision-making.**Continuous Improvement and Learning**: Maintaining comprehensive records of all activities, decisions, and outcomes has provided a valuable resource for analyzing past interventions, identifying best practices, and developing more effective strategies for future operations. The success of ADNOC’s dual workflow platform highlights the importance of collaboration, technological innovation, and continuous improvement in achieving operational excellence. By ensuring that workover operations are meticulously planned, executed, and monitored, ADNOC not only meets immediate operational needs but also supports long-term strategic goals. The insights gained from this implementation set a benchmark for the industry, demonstrating the value of a systematic and data-driven approach to workover operations. As ADNOC continues to refine these workflows, it ensures that its operations remain safe, efficient, and aligned with corporate objectives, driving forward the company’s commitment to excellence and innovation in the oil and gas sector.