In recent years, enterprise service bus (ESB) has become a favorable adoption as a technology category in the IT industry as it provides secure and guaranteed delivery of services. The elasticity of Enterprise Service Bus (ESB) enables numerous applications to exchange information makes it a significant middleware layer responsible for transferring information in a Service-Oriented Architecture (SOA). ESB is presently the utmost promising tactic for the integration of business applications in distributed and diverse environments. It also offers essential infrastructure support for transforming messages or data, intelligent routing, and protocol transformation. The idea of ESBs emerged from the requirements to move out from traditional integration patterns, that becomes difficult to manage with the passage of time. Our study aim is to understand and provide ongoing research topics, challenges and future directions concerning ESB applications. A systematic mapping study (SMS) is therefore implemented to categorize the selected papers into the following classification: contribution type, ESB applications, research type and their approaches. We have extracted a total of twenty-two papers for this systematic study and they are classified according to defined criteria. The findings of this SMS are discussed and researchers were provided with suggestions on possible directions for future research.

In recent years Fifth Generation (5G) technology is the most recent advancement in a wireless communication network. There is the advent of using the 5G with diverse data structures. The Blockchain (BC) has become an approving adoption for decentralized, peer-to-peer, distributed transparent ledger systems with a diverse data structure. The use of 5G with BC is an emerging trend in communication technology. The elasticity of 5G with BC enables many applications to reciprocity information molds it a fast, transparent, consequential, and safe for transportation of data in this smart era. Green computing (GC) is presently the intense optimistic tactic for the integration of smart technology in a diverse and distributed world of power consumption. This Systematic Mapping Study (SMS) has been analyzed by cautiously elected publications between 2016 and 2020 in well-putative venus. This study analyzed the advanced research on power consumption solutions for BC-based 5G communication, Moreover, a taxonomy of 5G based on green BC and GC in various areas is presented. Furthermore, Green energy renewable communication (GERC) problems are being observed in this research by integrating three discrete technologies such as 5G with green BC and GC also along with smart systems. Lastly, the research gaps had been bestowed to render future directions for the researchers in 5G with green BC and GC as the solution for rechargeable data packets.

With gigantic growth of data volume that is moved across the web links today, there has been a gigantic measure of perplexing information produced. Extremely huge sets of data including universities, organizations framework, institution gas, petroleum sector, healthcare, that have so enormous thus complex information with more differed structure. The major challenge is how to handle this significant volume of data, also in archaeological photogrammetry which alluded to as Big Data. Although big data has to be securely flying and conveyed through the internet. It cannot be controlled with regular conventional methods that fail to handle it, so there is a need for more up-to-date developed tools. The big data have frequently divided into V’s characteristics beginning from three V’s: volume, velocity and variety. The initial three V’s have been stretched out during time through researches to arrive 56 V’s till now. Among them are three newfound by the author that implies it multiplied near twenty times. Researcher had to dive to search for all of these characteristics in many researches to detect and build comparisons to answer the old, current, and restored essential inquiry, “how many V’s aspects (characteristics) in big data with archaeological photogrammetry and blockchain”. This paper provides a comprehensive overview of all secured big data V’s (characteristics) as well as their strength and limitations with archaeological photogrammetry and blockchain.

The rapid evolution of the digital landscape has catalyzed the integration of blockchain technology within the domain of cultural heritage, particularly in virtual museums within the Metaverse. This study introduces ArchaeoMeta, a novel framework designed to leverage blockchain technology to enhance security, authenticity, and visitor interaction in a virtual museum environment. Utilizing smart contracts deployed on the Ethereum Sepolia testnet, the framework manages visitor interactions and secures digital artifacts, addressing challenges associated with scalability and user experience under varying loads. The performance evaluation involved simulating user interactions, scaling up to ten thousand concurrent users, to assess the impact on transaction latency, gas usage, and blockchain size. Findings reveal significant scalability challenges, as transaction latency and blockchain size increased with the number of users, highlighting areas for optimization in managing high user traffic within the blockchain infrastructure. This study contributes to the understanding of blockchain applications in cultural heritage, suggesting that while ArchaeoMeta offers a robust platform for virtual museums, enhancements in scalability through layer-2 solutions or alternative blockchain platforms are essential for its practical implementation. The framework sets a precedent for future research in the convergence of blockchain technology and cultural heritage preservation, promising a transformative impact on how digital cultural experiences are curated and consumed.

As rapid decision making in business organizations gain in popularity, the complexity and adaptability of extract, transform, and load (ETL) process of near real-time data warehousing has dramatically increased. The most important part of near real-time data warehouse is to feed new data from different data sources on near-real-time basis. However, this new data is not in the format of the data warehouse therefore, it needs to be transformed into the required format by using transformation algorithms which is essential part of ETL process. A semi-stream join algorithm is required to implement this transformation, for this purpose a HYBRIDJOIN (hybrid join) algorithm has been presented in the literature. However, major design issue with this algorithm is that it uses a single buffer to load the disk partitions and therefore, the algorithm has to wait until the next disk partition overwrites the exiting partition in the disk buffer. As the cost of loading disk partition into disk buffer is the major cost of overall algorithm processing cost, this leaves the performance of algorithm sub-optimal. Moreover, existing approaches only considering the oldest key join attributes for finding the matches with master data and maintaining the Queue of key join attribute. However, performance can be improved if recent and oldest attributes process in parallel. This article addresses the limitation of HYBRIDJOIN by presenting two optimized new algorithms named: Parallel-Hybrid Join (P-HYBRIDJOIN) and Hybrid Join with Queue and Stack (QaS-HYBRIDJOIN). Proposed algorithms aim to reduce major processing cost that is disk I/O as well as to increase number of matching stream tuples. Both of these algorithms perform significantly better in terms of throughput and number of matching tuples as compared to existing approaches. Performance analysis and cost model for proposed algorithms show the best performance using intermittent stream data under limited resources.

In cultural heritage, archeological artifact preservation is paramount, underscoring the importance of safeguarding history. The sub-domain of volunteer incentivization in open-source software for archeological photogrammetry software (OSSAP) is vital for fostering effective volunteer engagement and rewards. Yet, OSSAP’s lack of a structured reward system and financial limitations in archeology impede proper volunteer compensation. Our introduction of the Trust-Based Blockchain Model for Contribution (TBBMC) addresses these gaps by providing ethical and efficient incentives. TBBMC utilizes cryptocurrency wallets and an economic model for better auditability and safety, while Initial Coin Offerings and Contribution Rewards facilitate financial transactions and fundraising. Smart contracts enhance operational efficiency and transparency, improving communication, coordination, and trust in archaeological volunteering. Finally, TBBMC’s innovative reward system not only motivates volunteers but also bolsters the ongoing preservation and discovery of archeological artifacts.