Distributed Data Management Research Articles

A New Method of Dynamic Horizontal Fragmentation for Multimedia Databases Contemplating Content-Based Queries

The proper storage and management of multimedia data is a topic of great interest to industry and academia. Database fragmentation plays a fundamental role as a mechanism to guarantee cost reduction and improve response time performance in distributed data management environments. Multimedia database access patterns are constantly changing; due to this, it is important that the partitioning schemes also adapt to these changes. Dynamic fragmentation techniques offer this advantage and represent a reduction of the tasks that an administrator must perform and the complete autonomy to determine when to carry out a new fragmentation based on a cost model. This work proposes a new method of dynamic horizontal fragmentation for multimedia databases, including a way to contemplate content-based queries in the creation of new fragments. The use of content-based queries is on the rise, as multimedia elements are often presented within databases, and for this reason new fragmentation strategies must include this aspect to provide better-performing schemas. The method included in this research is placed within a current web application called XAMANA. We performed some experiments to demonstrate the effectiveness of our approach.

Virtualized High Throughput Satellite Gateway with a Global Bandwidth Management Method

With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.

The Role of Accrual Accounting and Information Systems on Asset Management Practice in the Malaysian Public Sector : A Conceptual Paper

Purpose: The purpose of this study to identify and examine the role of accrual accounting and information systems in asset management practice in the Malaysian public sector. This study is important to enable the government take appropriate measures to enhance asset management practice in view of its increasingly crucial role in optimising government performance. Design/Methodology/Approach: This study identifies and examines in detail the role of accrual accounting and information systems in asset management practices in the Malaysian public sector by reviewing the issues commonly raised in the Auditor-General Reports, particularly on asset management. The conceptual aspect consists of an extensive search and analysis of secondary sources of information through references and a review of academic articles, Auditor-General’s Reports, news in print and other media, and publications pertaining to asset management. Findings: Previous studies show that accrual accounting and information systems have affected asset management practice in the public sector. Accrual accounting measures the performance of an organisation by recognising economic events that provide more accurate financial information, particularly on the organisation’s assets and liabilities. Information systems provide substantial growth opportunities for asset management, allowing systematic and timely data collection, processing, distribution, and information sharing, which can assist in managing assets throughout their life cycle. Originality/Value: This concept paper provides valuable addition to the current knowledge base, particularly on the role of accrual accounting and information systems on asset management practice. Keywords: Asset management practice, public sector, accrual accounting, information systems.

A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson’s Disease

Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.

Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes

In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

IBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System.

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

Cross-chain deals and adversarial commerce

Modern distributed data management systems face a new challenge: how can autonomous, mutually distrusting parties cooperate safely and effectively? Addressing this challenge brings up familiar questions from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to synchronize concurrent access to data. Nevertheless, each of these issues requires rethinking when participants are autonomous and potentially adversarial. We propose the notion of a cross-chain deal, a new way to structure complex distributed computations that manage assets in an adversarial setting. Deals are inspired by classical atomic transactions, but are necessarily different, in important ways, to accommodate the decentralized and untrusting nature of the exchange. We describe novel safety and liveness properties, along with two alternative protocols for implementing cross-chain deals in a system of independent blockchain ledgers. One protocol, based on synchronous communication, is fully decentralized, while the other, based on semi-synchronous communication, requires a globally shared ledger. We also prove that some degree of centralization is required in the semi-synchronous communication model.

A New Adaptive Hybrid Mutation Black Widow Clustering Based Data Partitioning for Big Data Analysis

In recent years, big data plays a significant role in data storage development in high demand. The big data consists of a large number of datasets and it becomes trouble in handling large traditional based database management systems. Big data turns out to be more populous; since it has the capability in managing different data sources and formats under several advanced technologies. On the other hand, few research works are ineffective while dealing with today’s issues. So to overcome such shortcomings, this paper proposes a novel adaptive hybrid mutation black widow optimization (AHMBWO) based clustering approach for distributed data management system in HDFS. Also, the proposed AHMBWO approach summarizes three different phases namely the construction of resource description framework (RDF) graphs, AHMBWO based clustering approach for distributed data management system in HDFS as well as placement and partition for handling and managing the distribution of data. In addition to this, seven test functions are employed to compute the performances of the proposed AHMBWO algorithm. Then the evaluation results based on the clustering process of the proposed AHMBWO with several other approaches such as BWO, PSO, GA and BBO to test the validity of various approaches for the respective datasets. The experimental analysis reveals that the proposed AHMBWO approach provides better performances with less execution time when compared with all other approaches.

IMPROVING OPERATIONAL EFFICIENCY OF GRIDS BY INTEGRATING GEOGRAPHIC MAPS IN SCADA SYSTEM: DaNang STUDY CASE

The continuous development of the present-day smartgrid technology is required to improve the operational efficiency of the power system. Energy management system (EMS) technology solutions, including Supervisory Control And Data Acquisition (SCADA) and Distribution Management System (DMS), must be constantly developed in terms of application range and efficiency to meet the mentioned requirements. The article proposes a technique to integrate Google Earth geographic map into the SCADA system. The efficiency of the proposed method is evaluated through PSS/ADEPT software. The results show that the proposed solution contributes to improving the reliability of the grid and improving the efficiency of customer care service as well as training of junior dispatchers.

GeoSparkViz: a cluster computing system for visualizing massive-scale geospatial data

In the last decade, geospatial data which is extracted from GPS traces and satellites image has become ubiquitous. GeoVisual analytics, abbr. GeoViz, is the science of analytical reasoning assisted by geospatial map interfaces. GeoViz involves two phases: (1) spatial data processing: that loads spatial data and executes spatial queries to return the set of spatial objects to be visualized. (2) Map visualization: that applies a map visualization effect, e.g., Heatmap, on the spatial objects produced in the first phase. Existing GeoViz system architectures decouple these two phases, which lose the opportunity to co-optimize the data processing and map visualization phases in the same cluster. To remedy this, the paper presents GeoSparkViz, a full-fledged system that allows the user to load, process, integrate and execute GeoViz tasks on spatial data at scale. GeoSparkViz extends a state-of-the-art distributed data management system to provide native support for general geospatial map visualization. The system encapsulates the main steps of the map visualization process, e.g., pixelize spatial objects, pixel aggregation, and map tile rendering into a set of massively parallelized map building operators. This allows the system to co-optimize the spatial query operators and map building operators side by side. GeoSparkViz is also equipped with a GeoViz-aware spatial partitioning operator that achieves load balancing for GeoViz workloads among all nodes in the cluster. Experiments based on an implementation in Spark show that GeoSparkViz achieves up to an order of magnitude less data-to-visualization time than its counterparts when running visual analytics tasks over large-scale spatial data extracted from the NYC taxi dataset and OpenStreetMaps.

Design and implementation of a distributed data acquisition function architecture based on DOA/Handle technology

With the development of new generation Internet technology, Digital Object Architecture (DOA)/Handle system plays an important role in industrial system. The development and technical characteristics of DOA/Handle technology is studied in this paper. Then function architecture of distributed data acquisition system based on DOA/Handle technology is proposed. Through the detailed design of the functions and characteristics of the architecture, the core functions such as distributed data acquisition, distributed data management, data security sharing, and data security governance are realized in the industrial Internet environment. At the end of the paper, the typical scenarios of this architecture are summarized, and a prospect of the application prospect of DOA/Handle technology is introduced.

Integration of Rucio in Belle II

The Belle II experiment, which started taking physics data in April 2019, will multiply the volume of data currently stored on its nearly 30 storage elements worldwide by one order of magnitude to reach about 340 PB of data (raw and Monte Carlo simulation data) by the end of operations. To tackle this massive increase and to manage the data even after the end of the data taking, it was decided to move the Distributed Data Management software from a homegrown piece of software to a widely used Data Management solution in HEP and beyond : Rucio. This contribution describes the work done to integrate Rucio with Belle II distributed computing infrastructure as well as the migration strategy that was successfully performed to ensure a smooth transition.

Computational Resource Allocation Strategy in a Public Blockchain Supported by Edge Computing

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.

The Rucio File Catalog in DIRAC implemented for Belle II

DIRAC and Rucio are two standard pieces of software widely used in the HEP domain. DIRAC provides Workload and Data Management functionalities, among other things, while Rucio is a dedicated, advanced Distributed Data Management system. Many communities that already use DIRAC have expressed their interest in using DIRAC for Workload Management in combination with Rucio for Data Management. In this paper, we describe the integration of the Rucio File Catalog into DIRAC that was initially developed for the Belle II collaboration.

Blockchain-Based Voting System

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trust-less, peer-to-peer systems. With the accelerated iteration of technological innovation, blockchain has rapidly become one of the hottest Internet technologies in recent years. As a decentralized and distributed data management solution, blockchain has restored the definition of trust by the embedded cryptography and consensus mechanism, thus providing security, anonymity and data integrity without the need of any third party. But there still exists some technical challenges and limitations in blockchain. The blockchain, with its own characteristics, has received much attention at the beginning of its birth and been applied in many fields. Voting is a very important process that takes place in almost every country of the world and many efforts have been made to improve the process over the years. In this report, the process of building an electronic voting system has been chalked out using Blockchain Technology. This new system will not only make the whole process much fairer but will also greatly reduce the time and workforce spent over it. This paper explains the prospects on which we can improve and also talks about the limitations of the system. An experimental set-up is also carried out with a proper result analysis to show the efficiency of the E-Voting System over the traditional process of voting.

Blockchain-Based Data Management and Edge-Assisted Trusted Cloaking Area Construction for Location Privacy Protection in Vehicular Networks

With the tremendous pervasion of location-based services (LBSs) in vehicular networks, the location privacy of vehicles has become an utmost concern. K-anonymity is one of the most popular privacy protection solutions, in which the real location of the request vehicle (RV) can be covered by a cloaking area including the location of ${k}$ –1 cooperative vehicles (CVs). However, K-anonymity assumes all CVs are always honest and thus offering opportunities for dishonest CVs to provide false location information. To combat such threats, we propose a trusted cloaking area construction (TCAC) scheme based on the trust mechanism to protect the location privacy of vehicles. In this article, the trust value is not only used to identify dishonest CVs, but also utilized to decide the LBS request of RV. A low trust value will make the LBS request be rejected due to dishonest and selfish cooperative behaviors when the RV has played the role of CV. To deal with the massive trust requirements caused by frequent vehicle movement, edge computing is employed to assist trust value evaluation. Moreover, traditional central and distributed trust data management may be unsuitable for vehicular networks. We also propose a blockchain-based trust data management method by combining vehicular regions partition, so as to rapidly evaluate trust value during the cloaking area construction. The security analysis and simulation results indicate that our proposed scheme is resilient to suppress dishonest CVs, inspire selfish CVs, and protect the location privacy of vehicles effectively, whereas the required computation time and communication cost are both limited.

Towards Log-Less, Fine-Granular State Machine Replication

State machine replication is used to increase the availability of a service such as a data management system while ensuring consistent access to it. State-of-the-art implementations are based on a command log to gain linear write access to storage and avoid repeated transmissions of large replicas. However, the command log requires non-trivial state management such as allocation and pruning to prevent unbounded growth.By introducing in-place replicated state machines that do not use command logs, the log overhead can be avoided. Instead, replicas agree on a sequence of states, and former states are directly overwritten. This method enables the consistent, fault-tolerant replication of basic data management primitives such as counters, sets, or individual locks with little to no overhead. It matches the properties of fast, byte-addressable, non-volatile memory particularly well, where it is no longer necessary to rely on sequential access for good performance. Our approach is especially well suited for small states and fine-granular distributed data management as it occurs in key-value stores, for example.

Intelligent interaction design research based on block chain communication technology and fuzzy system

With the increasing amount of information on the Internet, data storage management tends to be distributed. In distributed storage environment, users pay more and more attention to the timeliness of user interaction experience and the reliability of information interaction. However, the efficiency of users is often limited by the efficiency of data communication between distributed sites. One of the important goals of distributed data management is to improve the efficiency of data transmission and ensure the reliability of data transmission. Block chain technology is one of the emerging technologies supporting the development of management information system; it provides a solution for the storage, verification, transmission and communication of the distributed data. This paper focuses on solving the problem of block chain data transmission, and studies it from three aspects: improving the efficiency of data communication, ensuring the reliability of transmission, and improving the fairness of service, and different block chain data communication performance optimization strategies are proposed under the constraints of node communication capability, node trust, weight, priority of service request and other influencing factors.

Development and Evaluation of a Smart Contract–Enabled Blockchain System for Home Care Service Innovation: Mixed Methods Study

BackgroundIn the home care industry, the assignment and tracking of care services are controlled by care centers that are centralized in nature and prone to inefficient information transmission. A lack of trust among the involved parties, information opaqueness, and large manual manipulation result in lower process efficiency.ObjectiveThis study aimed to explore and demonstrate the application of blockchain and smart contract technologies to innovate/renovate home care services for harvesting the desired blockchain benefits of process transparency, traceability, and interoperability.MethodsAn object-oriented analysis/design combined with a unified modeling language tool was used to construct the architecture of the proposed home care service system. System feasibility was evaluated via an implementation test, and a questionnaire survey was performed to collect opinions from home care service respondents knowledgeable about blockchain and smart contracts.ResultsAccording to the comparative analysis results, the proposed design outperformed the existing system in terms of traceability, system efficiency, and process automation. Moreover, for the questionnaire survey, the quantitative analysis results showed that the proposed blockchain-based system had significantly (P<.001) higher mean scores (when compared with the existing system) in terms of important factors, including timeliness, workflow efficiency, automatic notifications, insurance functionality, and auditable traceability. In summary, blockchain-based home care service participants will be able to enjoy improved efficiency, better transparency, and higher levels of process automation.ConclusionsBlockchain and smart contracts can provide valuable benefits to the home care service industry via distributed data management and process automation. The proposed system enhances user experiences by mitigating human intervention and improving service interoperability, transparency/traceability, and real-time response to home care service events. Efforts in exploring and integrating blockchain-based home care services with emerging technologies, such as the internet of things and artificial intelligence, are expected to provide further benefits and therefore are subject to future research.

Distributed Data Platform for Machine Learning Using the Fog Computing Model

In this paper, we propose a machine learning-driven information platform for distributed data management without data accumulation using a fog computing model. It helps in analyzing the features of managed contents through several kinds of methods and their synchronization among the distributed nodes. Moreover, the feature model generated based on the features of all contents is achieved by combining the respective feature models distributed to some nodes. In other words, a flexible feature model is created by the combining feature models adapting to targets arbitrarily. In this paper, we propose the basic ideas and some basic functions for the data management platform among distributed nodes. Our evaluation confirms the effectiveness of combining the feature models using datasets.