Real-time Data Systems Research Articles

Unlocking Insights: The Imperative of Real-Time Data Collection and Mapping Systems

In today's rapidly evolving landscape, the need for timely and accurate data is paramount for informed decision-making across various sectors. However, traditional data collection methods often fall short in providing real-time insights necessary for addressing dynamic challenges. This study examines the critical problem of outdated data and the imperative for adopting real-time data collection and mapping systems to overcome it. The aim of the article is to highlight the transformative potential of such systems in unlocking actionable insights for decision-makers. By leveraging advanced technologies like geospatial mapping, sensor networks, and mobile data collection apps, organizations can gather, process, and analyze data in near real-time. This enables them to monitor trends, detect anomalies, and respond promptly to emerging issues. Ultimately, the adoption of real-time data systems offers a solution to the problem of outdated data, empowering decision-makers with the timely information needed to navigate complex scenarios and drive positive outcomes

Can ICT-enabled real-time data contribute to adaptive management in development programming?

ABSTRACTThis article aims to explore whether ICT-enabled real-time data (RTD) systems can help to improve the operationalisation of adaptive management of international development programmes. Using a qualitative multi-method approach consisting of 48 semi-structured key expert interviews and four exploratory case studies, we found that RTD can successfully inform rapid tactical adaptive management in development programmes but is, on its own, of only limited use for most strategic adaptive management. The research identified multiple contextual barriers to the use of RTD for adaptive management. These barriers need to be addressed to realise the full potential of real-time adaptive management of programmes.

Performance evaluation of High Definition video streaming over Mobile Ad Hoc Networks

Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.

Our Knowledge Management Hubble May Need Glasses

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

Scalable Data-Oriented Replication with Flexible Consistency in Real-Time Data Systems

Scalability is an increasingly important target for distributed real-time databases. Replication is widely applied to improve the scalability and availability of data. With full replication, database systems cannot scale well, since all updates must be replicated to all nodes, whether or not they are needed there. With virtual Full Replication, all nodes have an image of a fully replicated database and the system manages the knowledge of what is needed for each node to adapt to the actual needs, so that the system can be more scalable. This work proposes a scalable and consistent replication protocol using an adaptive clustering technique that dynamically detects the new data requirements. Because time is critical in such systems, the clustering technique must take into account both the communication time cost and the timing properties of the data. The proposed protocol also proposes a new updated method for addressing the temporal inconsistency problem by skipping unnecessary operations. It allows many database nodes to update their data concurrently, without any need for distributed synchronization. It uses state-transfer propagation with on-demand integration techniques to reduce the temporal inconsistency. The experimental results show the ability of the proposed protocol to reduce the system resources consumed and improves system scalability while maintaining consistency.

Fault-y Reasoning

Intelligent transportation systems (ITS) hold great promise to improve personal and commercial travel, but intelligently linking vehicles and travelers via increasingly interconnected real-time data systems creates a host of new and largely untested liability questions when things go wrong. In this article, we examine current policies, laws, and administrative codes guiding ITS liability by reviewing the scholarly literature and case law in the United States. We find (a) a patchwork of industry self-regulation, (b) a modicum of tort case law precedent that varies substantially across states, (c) many unresolved questions, and (d) little prospect of guiding federal legislation on the horizon. While the liability questions raised by driverless cars have received much attention, we conclude that ITS liability standards are likely to be settled incrementally in the near term on decidedly narrow grounds via case law on navigation and collision-avoidance systems, long before fully automated vehicles are deployed.

Sea-Ice Freeze-up Forecasts with an Operational Ocean Observatory

ABSTRACTThe evaluation of a new method for forecasting freeze-up at the eastern end of the Northwest Passage, a principal gateway into the Canadian High Arctic, is presented. The technique uses real-time data from a novel ocean observatory that combines acoustic, cable, and satellite communications technology to provide year-round bihourly data. The basis for the predictive capability comes from a previous analysis of a decade-long time series of instrumented mooring data collected in the area, which demonstrated a strong link between late summer upper ocean salinity and the timing of freeze-up there. Using real-time data from the observatory and this previously established relationship, accurate predictions of the timing of freeze-up in 2012 and 2013 with lead times of four weeks and two weeks, respectively, are presented. This unique forecasting capability points to the enhanced value of real-time data systems when these systems are located where previously collected long time series monitoring has revealed key relationships in the marine environment.

Emergency Medical Services Information Systems

The future of EMS and the U.S. health care system is dependent on interactive, real-time data systems that can be used to design, develop, implement, evaluate, and maintain quality evidence-based systems of care. North Carolina is a national and international leader in EMS given its support of the PreMIS System, the EMS Toolkit Project, EMS Bioterrorism Surveillance, and participation in the National EMS Database.