Across a wide range of modern process-aware systems, behavioral knowledge is captured and used in multiple shapes. The same system may be described at design time through process modeling, observed at runtime through execution data, and acted upon through decision-making components that adapt to changing operational conditions. As a result, process representations must remain consistent across heterogeneous tasks, supporting verification, diagnostics, and optimization, without the need to redefine them from scratch every time the viewpoint changes. Meeting these requirements calls for representations that are expressive enough to capture complex behavioral relations, while remaining susceptible to analytical reasoning, quantitative assessment, and integration with data-driven techniques. These challenges arise in a variety of domains, including business processes, service- oriented architectures, and autonomous and learning-based systems. Among these landscapes, Business Process Management (BPM) provides a particularly illustrative domain in which the value of behavioral process knowledge becomes both operational and measurable. Business information systems generate large amounts of execution data that records how processes unfold in practice. This has fostered the development of process mining techniques, which aim to discover process representations from observed execution traces in the form of event logs and to assess whether observed behavior matches expected behavior. Consequently, process representations should not be regarded as mere design-time artifacts. Instead, they enable organizations to obtain a complete view of their operations, identify inefficiencies and potential deviations, and systematically improve the way work is performed. Indeed, behavioral insights directly translate into measurable performance gains, affecting key performance indicators (KPIs), namely throughput time, resource utilization, regulation compliance, and operational costs. Business process modeling (hereafter process modeling) has traditionally relied on imperative (or procedural) representations (e.g., Workflow net or BPMN) that explicitly define the allowed execution paths of a system. While these models provide precise operational semantics and support automated execution, their explicit nature may restrict analysis and diagnosis in settings characterized by variability, flexibility, or partial observability. Conversely, declarative specifications have received increasing interest, as they suggest a significantly different way of representing processes, describing behavior in terms of constraints that executions must satisfy, offering a more flexible representation that is particularly suited to highly dynamic environments. These constraints are typically expressed as temporal rules grounded in logics such as Linear Temporal Logic on Finite Traces (LTLf), stating what must hold in a process rather than prescribing how it is achieved. As a result, they can serve as an internal behavioral layer that can be reused across multiple analysis and reasoning tasks. Despite their complementary strengths, imperative and declarative paradigms are often treated as separate approaches, adjusting their adoption across different analytical and reasoning tasks. Similar process representation challenges arise in service-based and cyber-physical settings, as well as in autonomous and learning-based systems, where agents must continuously adapt their behavior while still respecting safety and compliance constraints. In these scenarios, behavioral knowledge configures as a basis for monitoring and assessing whether observed or learned policies remain consistent with expected temporal and causal relations. This thesis investigates the use of declarative temporal specifications as a unifying abstraction span the full lifecycle of behavioral reasoning in process-aware systems. The main research objective guiding the work is stated as follows: RO: Declarative Specifications as a Unifying Behavioral Abstraction Investigate the use of declarative temporal specifications as a unifying abstraction for representing, verifying, andexploiting behavioral knowledge in process-aware systems. With this goal in mind, we set three research questions across process representations, verification, and decision making. RQ1: Process Representation Paradigms How can a declarative process specification be systematically derived from an imperative process model, preserving behavioral equivalence between the two representations? First, we discuss the relationship between imperative and declarative process representations in the business processes domain, showing how a declarative specification can be systematically derived from an imperative model while preserving behavioral equivalence. To this end, we introduce a systematic method to synthesize declarative specifications from safe and sound Workflow nets, together with formal guarantees ensuring that the two representations capture exactly the same behavior. This result enables principled transitions between paradigms, allowing analysts to select the most suitable representation depending on the task at hand. RQ2: Conformance Measurement How can the satisfaction of declarative process specifications be quantitatively assessed over observed executions? Once a process has been modeled, the growing availability of execution data makes it natural to quantify how closely observed traces adhere to the normative representation. This is the goal of conformance checking, which measures the degree of correspondence between a model (or specification) and recorded executions. Traditional conformance checking verification techniques for declarative specifications often assess individual rules in isolation, overlooking their mutual interplay and offering primarily binary satisfaction outcomes. We address this gap by proposing a probabilistic framework to quantify the satisfaction of declarative process specifications over event logs. We introduce interestingness measures that characterize the degree to which LTLf declarative constraints are supported by observed behavior. These measures enable behavioral diagnostics and provide a foundation for detecting deviations, changes, and emerging patterns in process executions. RQ3: Decentralized Data Sources How can declarative specifications be checked against execution data that is distributed across multiple independent sources? Capturing specification-level insights becomes even more relevant when execution data are distributed across independent sources, each party observing only a partial view of the process, while a global assessment must be derived from distributed evidence. In these inter-organizational settings, traditional conformance checking approaches are hindered by confidentiality and governance constraints that prevent centralizing raw event logs. To overcome this limitation, we propose CONFINE, a secrecy-preserving framework that enables process mining and declarative conformance checking without disclosing sensitive execution data between collaborating organizations. CONFINE executes mining and checking algorithms inside trusted execution environments (TEEs), where event logs are transmitted and processed under attested confidentiality guarantees. The architecture supports a symmetric collaboration model in which each party can provide execution data and perform joint analyses, enabling behavioral insight to cross organizational boundaries while preserving data sovereignty.
Abstract Non-Fungible Tokens (NFTs) have be- come a significant component of the dig- ital asset ecosystem, yet the visualiza- tion of rarity—a primary determinant of NFT value—remains fragmented, in- consistent, and methodologically opaque across web platforms. This paper pro- poses a comprehensive framework for NFT rarity visualization in web systems, integrating on-chain metadata extrac- tion, information-theoretic rarity scor- ing, and interactive, multi-layered vi- sualization components. The frame- work addresses three core challenges: (1) the absence of a unified, transpar- ent rarity computation standard; (2) the lack of effective visual encodings tai- lored to trait-level and collection-level rarity distributions; and (3) the scala- bility constraints inherent in real-time web-based rendering of large NFT col- lections. We describe the architecture in detail—from blockchain data ingestion pipelines through normalization, scoring, and front-end rendering—and present a prototype implementation using modern web technologies. A comparative eval- uation against existing commercial rar- ity tools demonstrates improvements in computational transparency, visual inter- pretability, and user task performance. The contributions of this work are three- fold: a formalized rarity scoring model grounded in information content theory, a modular visualization architecture for web deployment, and empirical evidence of the framework’s effectiveness for collec- tors, analysts, and marketplace designers.
Truffle is a framework that provides compiling, testing and systematic project management for developing Ethereum decentralized applications. As of now, Truffle provides a way to easily deal with bundling node.js modules of decentralized application using the webpack tool. However, due to the Truffle project structure, server-side node.js modules such as network communication modules are not usable in a direct way. In this paper, to address this issue, we propose a method to use server-side node.js modules through Ethereum smart contracts and event processing mechanism. In the proposed method, a separate node application is associated to the server-side module to execute the module in response to the request of the decentralized application. To this end, we introduce the notion of function gateway, a smart contract for connecting two applications with Ethereum's event-watch processing technique. Also, to use the function gateway contract in a robust way, we introduce a robust function gateway that includes the process of confirming whether or not the event-watch has occurred and the node.js module function has been executed. In addition, we present a decentralized application using node.js module for sending actual e-mails based on the function gateway.
Regulated service platforms like Upwork liaise freelancers with employers. These platforms require strong identity verification and transaction monitoring, but use an opaque and custodial escrow to orchestrate payments to users. This project proposes and implements “FR33”, a hybrid Web2-Web3 architecture for conditional escrow payments that preserves on-chain auditability, while enforcing identity-based access for critical payment logic. It combines various components: (i) off-chain identity verification that uses existing identity infrastructure, (ii) an on-chain registry that manages credential-based access control, (iii) a state-machine-based smart contract that enforces escrow payments, and (iv) an off-chain event indexing and rule-based monitoring layer that intervenes in suspicious transactions. These components are demonstrated via a prototype freelance marketplace that aligns with Singapore’s regulatory context. The prototype is evaluated via its functional correctness, where 11 unit and cross-layer test suites cover these workflows. At the same time, its cost of operation is evaluated by benchmarking gas usage across 30 executions of escrow operations on the Polygon Amoy testnet. Finally, the operational trade-offs introduced by the hybrid architecture were evaluated qualitatively against fully on-chain escrows. Experimental results on the network show that the escrow lifecycle incurs an average on-chain cost between $0.006 and $0.013 USD, which is more efficient and consistent than fees charged by centralised platforms for settling payments. These results illustrate the feasibility of the design for a proof-of-concept (PoC) system, by depicting the tradeoffs between performance and trust assumptions.
Web3, referring to the next-generation decentralized web, has gained extensive attention from industry and academia. Its popularity drives considerable demand for continuous development and delivery of web3 applications and services. Blockchain is an essential technology that enables web3 but comes with considerable operational cost. Blockchain-as-a-service (BaaS) is considered as a promising solution for supporting web3 applications; however, existing BaaS platforms are conceptual, built for specific applications, or not optimized for emerging web3 applications. This work analyzes the primary features and fundamental requirements of decentralized web3 applications and articulates the critical role of blockchain for web3. To fill the gap, we introduce DAPPaaS, the first customizable BaaS platform specializing in web3 applications, with carefully designed goals and principles to meet the goals. Our key technical contributions lie on the approaches enabling component modularization, communication efficiency, automated scaling and performance monitoring, distributed deployment, and resource optimization. We implement a real-world application based on DAPPaaS and conduct extensive performance evaluations to validate its utility and efficiency.
A system implementaion paper on a decentralized application for transparent charity transactions built on hardhat , next js ,and django that combines the web2 and web3 dynamics.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
E. Orestes O’Brien, Breno da Costa Paulo, Ángel Martín, Muhammad Shuaib Siddiqui · 5 authors
Future networks will deliver unprecedented performance, versatility, and efficiency, driven by advancements in automated monitoring and control. As networks evolve into interconnected platforms capable of enabling advanced features, allocating assets, and configuring them via northbound Application Programming Interfaces (APIs), dynamic control over multiple actors and stakeholders becomes essential. At this point, identity management plays a crucial role in overseeing onboarding, administering access, and registering activity to log and account for all operations. This paper compares the Open Common API Framework (CAPIF), based on the European Telecommunications Standards Institute (ETSI) standard, with a novel distributed identity-based approach using Distributed Ledger Technology (DLT). To assess the viability of these solutions, various tests are conducted to evaluate the frameworks’ latency and processing workloads.
The proliferation of multi-vendor autonomous systems (MVAS), such as shared warehouses, necessitates robust cooperation frameworks for robotic agents from different vendors. Ensuring operational integrity in these environments is a critical challenge, as the misbehaviour of a single agent can disrupt the entire system. This paper proposes a decentralised framework for secure and trustworthy multi-robot cooperation. Our approach leverages distributed ledger technology (DLT) to create an immutable record of operations and introduces a novel, lightweight Proof of Location (PoL) mechanism. This PoL allows agents to verify task completions both physically and cryptographically with minimal computational overhead. The framework is further strengthened by a real-time, algorithmic trust and reputation system that continuously evaluates agent behaviour. A key advantage over traditional methods is our support for asynchronous verification, which enhances both scalability and efficiency by removing centralised bottlenecks. Extensive simulations in a warehouse setting demonstrate that our framework effectively detects and mitigates malicious activities, thereby improving the security and performance of MVAS. The broader applicability of our approach is further illustrated through a conceptual case study in a smart city parking scenario.
Fernando Castillo, Eduardo Brito, Sebastian Werner, Pille Pullonen · 5 authors
Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic foundations for genuine trustworthiness in service ecosystems. Our approach starts with machine-readable SLA clauses converted into verifiable predicates and monitored within Trusted Execution Environments. These monitors collect timestamped telemetry, organize measurements into Merkle trees, and produce signed attestations. Zero-knowledge proofs aggregate Service-Level Indicators to evaluate compliance, generating cryptographic proofs verifiable by stakeholders, arbitrators, or insurers in disputes, without accessing underlying data. This ensures three security properties: integrity, authenticity, and validity. Our prototype demonstrates linear scaling up to over 1 million events per hour for measurements with near constant-time proof generation and verification for single violation claims, enabling trustless SLA enforcement through cryptographic guarantees for automated compliance verification in service monitoring.
Decentralized Autonomous Organizations (DAOs) are emerging as key governance structures in Web3 ecosystems, enabling community-driven decision-making without centralized control. Yet, current DAO implementation frameworks often lack modularity, role adaptability, and low-code accessibility, limiting broader adoption-especially among non-technical users and emerging organizations. Targeting these issues, this paper proposes a modular, template-driven DAO system for public blockchains such as Ethereum, integrating reusable governance logic units, token-based role configuration, and low-code interfaces to support flexible and secure deployment. The system allows role-scoped configuration of governance parameters via templates and supports scenario-specific deployment using predefined smart contracts, avoiding the need for direct code modification in the demonstrated use case. A quadratic votingbased governance scenario was used to demonstrate how the proposed framework can enable DAO setup and participation without requiring direct contract modification for that specific use case. Internal validation, conducted with simulated stakeholder roles, confirmed improvements in usability, configuration safety, and governance clarity, while also identifying practical gaps in role-specific UI guidance and simulation tooling. Although the current implementation is limited to a single voting model and local deployment, the findings highlight the potential of templatebased DAO systems to enable more inclusive, adaptable, and transparent decentralized governance.
Web3 incorporates blockchain technology, decentralisation, and token economies, requiring new service design approaches. Existing design principles, tools, methods, and processes were developed before Web3 and may not address its unique characteristics. This paper extracts four Web3 characteristics − Ownership, Transparency, Tokenisation, and Community Governance − through literature review and constructs three corresponding service design tools: a service concept sheet, community member journey map, and service ecosystem map. Semi-structured interviews with five Web3 and service design experts evaluated these tools through content analysis, identifying improvement areas and refinement proposals.
V. Hemamalini, M. Jeyaselvi, Amit Kumar Tyagi, Shabnam Kumari
Decentralized Applications (dApps) and Distributed Ledger Frameworks (DLFs) represent critical advancements in the realm of blockchain technology, promising enhanced security, transparency, and efficiency. Decentralized Applications, leveraging the inherent properties of blockchain, operate without central control, offering users greater autonomy and reducing reliance on intermediaries. These applications span various domains, from finance and supply chain management to gaming and social networks, demonstrating the versatility and transformative potential of dApps. Distributed Ledger Frameworks, encompassing both blockchain and non-blockchain-based systems, provide the underlying architecture for these applications. DLFs ensure data integrity, immutability, and consensus across distributed nodes, fostering a trustless environment where participants can transact securely. Key frameworks like Ethereum, Hyperledger, and Corda offer diverse features tailored to different use cases, ranging from public, permissionless networks to private, permissioned systems.
Bin Cao, Shaohe Xiao, Long Shi, Taotao Wang · 10 authors
Web 3.0 represents the next evolutionary stage of the Internet, characterized by a “read-write-own” paradigm that empowers users to control their own data. Driven by advancements in edge technologies and rising concerns about privacy and security, a growing number of Web 3.0 applications have emerged, sparking widespread concern and research in both the industrial and academic fields. To address the demands for secure identity management, trustworthy data circulation, and reliable network services, more emerging technologies beyond blockchain have been introduced to Web 3.0. However, this integration has also introduced increased complexity and ambiguity in Web 3.0’s implementation and future development. Therefore, this survey aims to provide a systematic review of Web 3.0, elucidating its definition, architecture, technologies, ecosystem, and future directions. The main contributions include identifying an adaptable Web 3.0 architecture that illustrates the correlation of each layer and functionalities, summarizing Web 3.0 workflow with three functions as identity management, data publication and retrieval, and network service. Besides that, this survey categorizes enabling technologies into five areas, highlighting the comparison of different technologies. As the Web 3.0 application ecosystem flourishes, this survey further explores use cases in finance, governance, and gaming, and introduces evaluation criteria tailored for Web 3.0 applications. Finally, we highlight some future research directions for the full realization of Web 3.0. This survey outlines the current landscape and advancements of Web 3.0, laying a solid foundation for future research and standardization, with the ultimate goal of realizing a decentralized and user-empowered digital infrastructure.
As digital transformation accelerates, data has become a core driver of technological innovation and economic growth. However, a key challenge in data utilization is the semantic misalignment between data supply and the demands of business scenarios. This misalignment significantly hinders efficient data flow and collaborative utilization. To address this issue, this article proposes a federated service solution integrating blockchain and decentralized autonomous organizations and operations (DAOs), large language models (LLMs) and scenarios engineering, federated learning and edge computing, as well as encryption technologies and privacy-computing. A five-layer federated service framework is introduced, consisting of the foundation layer, the data-scenario layer, the semantic coordination layer, the incentive-security layer, and the application layer, which is designed to ensure efficient and context-aware data supply–demand matching while preserving privacy and scalability. Moreover, the core mechanisms for semantic coordination are proposed, and a detailed solution process for resolving semantic misalignment with these mechanisms, as well as an illustrative example, is also presented. The proposed federated service framework offers an effective solution to semantic misalignment in supply–demand matching, fostering seamless data collaboration across diverse business scenarios. This work provides an intelligent service paradigm that leverages distributed data co-governance to address semantic challenges in the digital economy.
The process of exchanging healthcare data introduces stringent requirements regarding users’ privacy. Federated learning (FL) is a novel model-sharing technique that aims to give additional privacy guarantees during machine learning process. Blockchain, as a form of distributed ledger technology, possesses the characteristic of trustworthiness; however, it is deficient in terms of computational capacity with a high-latency network due to its laborious consensus protocols. In this paper we present a distributed healthcare FL-based secure model sharing architecture to ensure healthcare data privacy and scalability. The solution relies on state channels technique to reduce on-chain transactions, contrast architecture latency, and reduce bandwidth consumption, alleviating the burden on the blockchain. State channels can be utilized to efficiently execute the tasks of federated learning models sharing and to solve the scalability problem.
As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently. This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms. On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment. My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.
Shuning Sun, Xi Ran, Shuchao Pang, Xiaofeng Chen · 5 authors
In Web 3.0, to achieve the continuous release of the value of data elements, we start with technologies related to data circulation and conduct research and analysis on the current data element market. We find that in the traditional model of data element circulation, the sharing and exchange of multi-source heterogeneous data are disconnected from the circulation and release of data element value. This results in poor data reusability, unclear data requirements, small market size, short lifecycle of data elements and data markets, making it difficult to motivate sellers to actively participate in building the data element market, and thus hard to achieve a positive cycle of data element value release. To address this, we have conducted a strategic analysis of the value release of data elements and designed a universal reference model, DataR2E, for the entire process of value release in the data elements market. Within the DataR2E model, we introduce the concept of data element bounty production, utilizing Web3 technology and data production tools to encourage sellers to actively provide the data elements needed by buyers. We envision using data production tools to build a bridge between buyers and sellers in the data elements market, promoting the sustainable development of the data element market, overcoming the mismatch in data elements expected by buyers and sellers, thereby resolving the issues of market presence without pricing and pricing without market presence, achieving sustainable development of the data elements market, and maximizing the potential of data element value release.
Sajan Poudel, Rasib Khan, Aalok Dhonju, Nishar Miya
Edge computing is revolutionizing digital infrastructures by enabling low-latency processing, bandwidth optimization, and real-time decision-making across applications like IoT, smart cities, and industrial automation. However, its decentralized nature introduces significant security challenges, making trust and accountability crucial for reliable service delivery. Secure provenance management is vital to address these challenges, ensuring that distributed services and high data volumes are protected from tampering and unauthorized access. This research presents SPHERE, a scalable framework that integrates distributed ledgers, digital signatures, and cryptographic techniques to ensure service integrity and traceability. By leveraging blockchain for tamper-proof provenance, EdgeX Foundry for service orchestration, and an off-chain database for load balancing, our approach enhances security while maintaining system performance. Empirical analysis through a proof-of-concept deployment on a virtualized testbed validates its effectiveness in strengthening service reliability, auditability, and compliance, addressing critical gaps in edge service security and underscoring the importance of secure provenance-awareness in decentralized environments.
Harshit Mula, Vedant Shirish Utage, S. Ganesh Kumar, Syed Ismail Abdul Lathif · 5 authors
In a time where professional and social networks are at the forefront of connectivity, the shortcomings of existing platforms come to the fore, particularly for specialist communities in the technical sector. These networks tend to lack in establishing trust, confidentiality, and quality of interaction. This paper proposes an innovative platform that addresses the specific needs of technical experts and enthusiasts via strong verification and decentralized trust systems. Utilizing state-of-the-art technologies such as Large Language Models (LLMs), zero-knowledge proofs (zkSNARKs), and blockchain integration, GeeksGather provides a setting in which users are verified on the basis of actual expertise without sacrificing privacy. By using LLM-created quizzes, privacy-preserving development verification, and implementation with such tools as Scroll blockchain and Guild.xyz, GeeksGather maintains both transparency and user autonomy. The architecture of the system fosters meaningful, secure, and high-quality interaction by means of token-gated groups and encrypted communication channels. This article introduces the design, deployment, and future implications of GeeksGather for professional digital networking to come-raising the bar in trust, decentralization, and data integrity.
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa · 5 authors
Abstract Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator , a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.
This article examines key emerging technologies transforming financial platform engineering. Platform engineering plays a pivotal role in building these systems by leveraging microservices architecture, event-driven systems, and cloud-native technologies. This article explores how modern platform engineering practices ensure low latency, high throughput, security, and regulatory compliance while integrating cutting-edge technologies like machine learning and blockchain. Machine learning has revolutionized fraud detection by enabling the analysis of vast transactional datasets to identify patterns invisible to human observers. Blockchain technology has gained adoption for transaction verification, providing distributed ledger systems that ensure security and immutability while enabling smart contracts that automate complex financial agreements. Real-time analytics capabilities allow financial institutions to process streaming data for immediate insights on market trends, customer behavior, and risk factors, supporting data-driven decision-making at market speed. Finally, API ecosystems have created interconnected networks of services that facilitate innovation through standardized interfaces, transforming how financial services are developed and consumed across core banking, partner integration, and public marketplace contexts.
This study analyzes web services' functionalities and performance, focusing on mobile platforms and Web3 applications. Web services enable communication between applications but pose challenges due to different types (RESTful, SOAP), data formats, and protocols. Choosing the right service requires considering scalability, security, and interoperability. The study provides a comparative analysis of web service types, evaluating their strengths, limitations, and suitability for mobile platforms. It highlights challenges in selecting services and offers insights to guide decision-making. By assessing performance metrics, this research aids in optimizing web-based applications, enhancing decision-making for Web3 and mobile development.
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageable groups of nodes that collaborate to achieve consensus without the overhead associated with traditional large-scale consensus protocols. CohortSync operates by first classifying nodes based on their network latency, data relevance, and operational load. This classification enables the protocol to intelligently form micro-cohorts that are geographically and contextually optimized, reducing the latency typically experienced in global consensus operations. Each micro-cohort is responsible for a subset of the reconciliation tasks, allowing for parallel processing and significantly reducing the time to reach consensus. The protocol incorporates a hybrid approach to consensus that combines elements of both deterministic and probabilistic consensus mechanisms. This hybrid model allows CohortSync to maintain high availability and consistency, even in the face of node failures or network partitions. By adapting the consensus mechanism based on real-time network performance and node responsiveness, CohortSync can dynamically adjust its operations to maintain system performance and data accuracy. Another key feature of CohortSync is its reconciliation process, which uses a version-controlled state reconciliation algorithm. This algorithm ensures that all nodes within a micro-cohort maintain a synchronized state, with conflicts resolved through a majority rule among the cohort members. This approach not only minimizes the risk of data divergence but also optimizes the reconciliation process to be both time-efficient and resource-conservative. CohortSync also integrates a continuous learning component that analyzes past consensus rounds to optimize future cohort formation and consensus strategies. This machine learning-driven adaptability makes the protocol robust against evolving network conditions and varying operational loads across nodes. The protocol has been tested in various simulated environments that mimic real-world distributed systems across different industries, including finance, healthcare, and e-commerce. The results demonstrate that CohortSync significantly outperforms existing consensus protocols in terms of scalability, fault tolerance, and operational efficiency. In conclusion, CohortSync presents a transformative approach to consensus and reconciliation in distributed systems. By decentralizing the consensus process into manageable micro-cohorts and integrating adaptive learning mechanisms, CohortSync offers a scalable, efficient, and robust solution that can meet the demands of contemporary distributed computing environments.