FIDRS Framework Architecture

• FIDRS Framework is an integrated distributed system architecture designed to enhance efficiency, scalability, and reliability by unifying CRM, heterogeneous databases, and decision support.
• Its modular design comprises a CRM phase, a heterogeneous distributed database system (HDDBS), and a decision support phase leveraging the RMSD extraction algorithm.
• Simulation results indicate reduced response times and 12–15% improved performance over legacy systems, underscoring its practical benefits in complex data environments.

The FIDRS Framework is an architecture for integrated distributed reliable systems designed to advance efficiency, performance, and dependability in environments handling large-scale, heterogeneous data and complex organizational processes. By unifying customer relationship management, heterogeneous distributed databases, and advanced decision support—including innovative extraction algorithms—the FIDRS framework achieves substantial improvements over prior systems, as demonstrated by simulation-driven metrics such as reduced response time and increased scalability.

1. Architectural Overview

FIDRS is structured as a modular, three-phase system. Each phase is intended to address a critical aspect of integrated distributed system performance:

1. Customer Relationship Management (CRM) Phase This phase manages the identification, attraction, and retention of customers, employing submodules such as Information Resource Management (IRM), Sell Configuration and Services System (SCSS), strategic planning (often with balanced scorecards), and risk management. These modules ensure alignment between organizational activity and customer needs, facilitating enhanced satisfaction and mitigating operational risk.
2. Database Phase Central to FIDRS is the adoption of a Heterogeneous Distributed Database System (HDDBS)—an architecture that employs standard interface protocols (e.g., ODBC, JDBC) to interconnect diverse databases. This enables seamless, concurrent access to distributed data sources, reducing latency and bottlenecks.
3. Decision Support System (DSS) Phase The DSS incorporates a database management system orchestrating both internal and external data sources, query packages utilizing a variety of statistical and optimization models (linear programming, regression analysis, etc.), and an extraction subphase. The extraction phase employs the RMSD algorithm to efficiently retrieve relevant data, especially from large-volume repositories.

This multi-phase design is engineered to deliver superior data accessibility, concurrent processing, and system reliability, with each phase contributing distinct, complementary strengths.

2. Comparisons with Prior Integrated Frameworks

Several legacy architectures—including ERPWKM, ERPDRT, and ERPSD—serve as touchpoints for assessing FIDRS's advances:

• ERPWKM leveraged the WKMSD algorithm for data accumulation control but incurred significant delays during high-load operations.
• ERPDRT employed simultaneity techniques and ERPASD for data reduction, supporting multiple levels of availability for reliability, but failed to sufficiently address data extraction latency and did not deliver robust speed improvements under large-scale operations.
• ERPSD relied on "index codes" and distributed firewalls for quick access and security, yet showed inefficiency at high request volumes.

Table 1 summarizes salient contrasts:

Framework	Core DB Technique	Extraction Algorithm	Response Time Improvement
FIDRS	HDDBS (ODBC/JDBC)	RMSD	+12–15% vs. ERPWKM/ERPSD
ERPWKM	Centralized/Distributed	WKMSD	–
ERPDRT	Distributed/Multi-level	ERPASD	–
ERPSD	Indexed/Distributed	Index Codes	–

This table appears verbatim in multiple sources (Gashti, 12 Oct 2025, Gashti, 15 Oct 2025).

FIDRS’s key improvement lies in its integrated CRM focus, flexible HDDBS infrastructure, and employment of advanced extraction mechanisms, directly addressing latency, scalability, and heterogeneous data integration.

3. Heterogeneous Distributed Database System (HDDBS)

The HDDBS is foundational to FIDRS. By enabling the simultaneous querying of multiple, disparate databases using standardized communication protocols (ODBC, JDBC), HDDBS offers:

• Reduced Latency: Parallel access and concurrent queries bypass the bottlenecks intrinsic to centralized database architectures.
• Increased Reliability: The distributed approach prevents single points of failure and provides robust fallback options.
• Enhanced Scalability: Database nodes can be added or reconfigured without significant impact on overall performance, accommodating growth in users and data volume.

The HDDBS architecture supports performance optimization and flexibility for integrative, cross-domain data operations, facilitating timely and reliable organizational decision making.

4. RMSD Algorithm and Decision Support Optimization

The extraction submodule within the DSS phase leverages the RMSD algorithm, which is central to FIDRS's low response times and high efficiency in data retrieval:

• Time Complexity Reduction: RMSD is designed to optimize both data scan and extraction processes, outperforming conventional methods like K-Means and ERPASD algorithms—particularly in large or high-throughput environments.
• Resource Optimization: The algorithm ensures that fewer computational resources are tied up per request, contributing to system-wide efficiency.
• Empirical Improvement: Simulated scenarios report response time reductions (e.g., 0.031 seconds for low-volume requests and up to 12–15% better performance over legacy systems as request load increases) (Gashti, 12 Oct 2025, Gashti, 15 Oct 2025).

Efficiency is succinctly represented through the proportionality $E = (P \times R) / T$, where $P$ is performance, $R$ is reliability, and $T$ is response time.

5. Simulation Results and Performance Metrics

Extensive simulations assess FIDRS in comparison to prior frameworks:

• Primary Metric: Response time (in seconds) over varied numbers of concurrent user requests.
• Findings:
  • For small-scale operations, improvements are moderate (e.g., 0.029s for FIDRS vs. 0.052s for ERPSD).
  • For large-scale loads, gains are more marked, with FIDRS showing approximately 15% effectiveness improvement over ERPSD and 8.7% over ERPDRT (Gashti, 15 Oct 2025).
• Implications:

FIDRS presents improved scalability, system responsiveness, and throughput. Percentage improvement calculations employ:

$$\Delta = \frac{T_{\text{old}} - T_{\text{FIDRS}}}{T_{\text{old}}} \times 100\%$$

where $T_{\text{old}}$ is the response time of the previous framework and $T_{\text{FIDRS}}$ is that of FIDRS.

6. Systemic Efficiency, Reliability, and Scalability

FIDRS advances integrated distributed systems by minimizing redundancy through its distributed design and by employing sophisticated algorithms like RMSD. The synergy among CRM strategies, heterogeneous database access, and analytical support ensures each subsystem contributes meaningfully to overall goals.

Reliability is reinforced through distributed risk management and redundant resource access enabled by HDDBS, so component failures do not render the system inoperative. Flexible strategy and risk modules further buffer the system against operational variances and evolving requirements.

7. Summary of Significance and Future Directions

FIDRS represents a substantial evolution in integrated distributed reliable system design, integrating CRM, advanced database techniques, and robust decision support. Its improvements over legacy frameworks are demonstrated both conceptually and with simulation data, highlighting reductions in latency, improvements in scalability, and increased dependability.

This suggests that wider adoption of such multi-phase, distributed approaches—with real-time, heterogeneous data handling and advanced extraction algorithms—may set the trajectory for future developments in integrated system design, especially in domains requiring both high throughput and strong reliability guarantees. Plausible future directions involve further performance tuning of HDDBS coordination mechanisms, expansion of algorithmic advancements in the extraction phase, and additional empirical studies under varied real-world deployment conditions.