Research Briefs on Information and Communication Technology Evolution

Decentralized Finance Integration with ERP Systems for Secure Smart Contract Based Transactions

2025-03-31T18:02:51+00:00

This study proposes a decentralized framework that merges smart contract based Decentralized Finance (DeFi) protocols and traditional Enterprise Resource Planning (ERP) systems to provide secure, automatic, and verifiable transaction execution. It constructs an additional middleware interface to guarantee interoperability between ERP modules and blockchain networks that utilize smart contracts for procurement, finance, and asset management modules. The system was tested empirically within a hybrid testbed of chains with Ethereum Virtual Machine (EVM) compatibility simulation executing ERP transaction testing on a simulated environment with physical hardware. According to quantitative assessment results, performance increased, achieving a 38% increase in transaction throughput, a 27% decrease in execution costs, increased trust and traceability due to cryptographic audit trails, and improved auditability. The research highlights the potential of DeFi integrated ERP systems for decentralized enterprise finance systems as a scalable secure replacement to centralized enterprise finance systems.

Neural Network Powered Indexing Techniques for High Performance Data Retrieval

2025-03-31T18:04:03+00:00

Modern applications’ data growth poses the limitation of requiring highly effective and flexible indexing methods. Structures such as B-tree, hash index, and spatial tree are considered the oldest forms of indexing, but they seem to fail when it comes to high and constantly changing dimensional structures. The objective of this paper is to increase the speed, precision, and scope of data retrieval enhancement using the indexing methods powered by neural networks. We teach neural networks that cleverly change the key space to storage space as a mapping function to minimize query time and enhance retrieval accuracy. The proposed framework has been tested thoroughly against learned index and classical index baselines on several datasets and workloads. The findings are substantial concerning response times greatly improving with lower memory use and tracking more data changes. Besides filling the gap between deep learning and systems optimization, this paper marks the first step towards building intelligent indexing systems. Deep learning coupled with fast changing data environments challenge the future of indexing systems, and this document is the groundwork for such advanced systems.

Reinforcement Learning Based Optimization of Query Execution Plans in Distributed Databases

2025-03-31T18:05:54+00:00

Troublesome workloads, data heterogeneity, and shifting resource conditions make efficient query execution highly difficult to achieve in distributed database systems. Traditional optimizers will almost always rely on handcrafted methods or static cost models to achieve the desired results, resulting in adaptative failures along the way and serving at best subpar query execution plans (QEPs). This paper presents a new architecture meant to optimize QEPs by utilizing deep policy reinforcement learning (RL) for dynamically shifting execution strategy adaptations over distributed nodes. The proposed model considers and structures the optimization problem as a Markov Decision Process (MDP) with states available in the form of system and query profiles, actions available being the choices of QEPs, and the rewards acting as a mere performance measurement for execution. We analyze this approach with different combinations of queries and nodes through benchmark datasets and simulated environments. The objective of this evaluation is to test the model’s performance in regards to differing query kinds and node configurations. The experiments indicate remarkable advances in system throughput and execution time while achieving strong generalization to unfamiliar queries. These results support the hypothesized ability of query processing in future distributed databases to not have suggestion mechanisms reliant on rules or costs, unlike their predecessors, and instead implement optimizers that utilize RL.

Automation of SAP ERP Processes Using Agentic Bots and UiPath Framework

2025-03-31T18:07:22+00:00

This research focuses on an automation approach to implementing SAP ERP processes with the help of agentic bots created in the UiPath RPA platform. As enterprise resource planning systems become increasingly complex, manual processes using SAP from the modules becomes more time-consuming, increases errors, and results in highly detailed operational workflows. Using appropriate intelligent, event-driven agentic bots, and combining them with automation functionalities of UiPath, this research exhibits remarkable efficiency improvements in finance, purchasing, and human resource SAP modules. The method used was partitioning of the SAP domain into set SAP tasks that can be automated using reusable automation patterns with the support of custom bots, then measuring how fast the bots could execute the tasks compared to doing them manually. Experiments indicate that the bots were able to perform tasks with up to 72% less execution time, while also displaying more precise results and better handling of exceptions. In addition, the paper looks at scalability, organizational acceptance, and post-deployment results with different business situations. These results highlight the cognitive automation paradigm on enterprise systems as well as suggest a base model for transforming SAP systems based on RPA technology.