Real-Time Data Processing and Feedback During In-Situ Heating

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope

Real-time big data processing is a crucial need of meteorology today. As a matter of fact, the weather is a determining factor in decision making in different areas such as air or sea transport, so acquiring knowledge of the environment in real time is a consequential question. A promising approach for big data treatment in real time is based on the concept of complex events processing CEP). The CEP aims to identify significant events in a cloud of simple and individually detectable events in real time. The meteorological variables, constituting simple event's spaces, offer significant flow of data to be processed using CEP technologies. This real-time data processing is done by phase of their integration with weather systems. This paper discusses the big data's meteorological aspects, and shows the architectural model for integration of weather data and its processing in real time.

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

The problems of design, computer simulation, manufacture of the device sample and its characteristics investigation of the acoustooptical scanner based on surface acoustic waves for 2D data processing in real time are discussed. The method is based on waveguide Bragg and radiation acoustooptic interactions with a synthesized optical aperture using a few optical beams. The total information field is divided into subareas, each of which is irradiated using an individual optical beam controlled by the corresponding waveguide acoustooptic Bragg cell and interaction cell. During the work elaboration of unit contraction, computer simulation, fabrication sample on based Y-cut lithium niobate and its characteristics investigated. This unit makes it possible to process in real time 2600times650 scanning points (information bits) representing two-dimensional arrays

Real-time big data processing for anomaly detection: A Survey

Electrical submersible pump (ESP) operation, like many other segments of the oil industry, is recovering from having been drowned in large number of data, the aftermath of information technology revolution, and refocusing on effective utilization of reservoir and production data. This paper presents results of the application of real-time data processing and interpretation in a mature field (Permian basin) through operating and service company alliances. The process involves application of production system efficiency management coupled with newly developed advanced ESP technology and analysis tools-computer simulation software packages that could simulate actual ESP and downhole performance. Data are acquired from the wellsite using data acquisition panels and are sent over a secure network connection to a data hub. The data are also sent for intelligent real-time ESP data processing and interpretation. Well and reservoir monitoring, surveillance, analysis, and diagnosis are also performed. Active collaboration with producing companies at every stage of the process was encouraged through the formation of a new alliance. Finally, various recommendations and suggestions that could operate, optimize the ESP system, reduce well downtime, and prevent catastrophic and premature ESP failures were made. This new approach to ESP operation reduces daily challenges in operating brownfields, such as keeping wells economically viable; solving production problems associated with operating mature assets in today's oil industry that is plagued with continuous declining of manpower resources. As might be expected, lease operating expense (LOE) and/or deferred oil production is reduced. Operational case studies and individual benefits are highlighted and included in this paper. This new approach is a significant departure from the conventional ways of operating ESP where service companies traditionally supply ESPs to producing companies to operate. This new alliance will have a decided influence on future ESP operation and the survival of many brownfields worldwide. Introduction ESP applications as a form of artificial lift method have continued to increase through the years, and significant growth is expected in the next decades as artificial lift requirements increase to a greater extent than current usage. ESP application covers all aspects of hydrocarbon exploitation. Its usage ranges from mature marginal onshore wells to high profile, deep-water subsea wells.

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

In order to meet the tomato color sorting system with the requirements of multi-channel data acquisition and real-time processing, [ ]firstly, a multi-channel color digital acquisition and processing technique program based on FPGA were proposed . Then, a multi-channel data acquisition and real-time parallel processing system was built. The RGB color sensors were slected as detection components and the FPGA chip EP1C3T144C8 was slected as a core of data acquisition and processing. Meanwhile, ports of signal acquisition and the solenoid valve control were adopted a two-way analog switch HC4051 for saving I/O ports of FPGA. Thirdly, under the modular design and module reuse principles, we developed all modules with VHDL Language in QuartusII development environment. Finally, RTL and time sequence simulation for the system were done. The results showed that the connections between the modules accord with control logics. [²»´o¶O accorded ¹ýE¥E½] The performance of system time sequencev are good, which can meet with the requirements of real-time processing of tomato color sorting. On these bases, we also conducted a tomato sorting test verification. The results showed that the average accuracy of sorting is 98.70% and coefficient of variation is 0.01. This results can meet requirements of production.

This study investigates the role of real-time data processing in optimizing inventory management within distributed cloud systems. As businesses increasingly adopt cloud technologies, the need for efficient inventory management solutions becomes paramount. This research highlights the challenges faced by organizations in maintaining optimal inventory levels while leveraging real-time data for decision-making. Through an analysis of existing literature and statistical methods, we present a framework for implementing real-time data processing techniques that enhance inventory optimization. The findings suggest that integrating real-time data analytics significantly improves inventory turnover rates, reduces stockouts, and enhances overall operational efficiency. This paper contributes to the growing body of knowledge on inventory management in cloud environments and provides practical recommendations for organizations seeking to optimize their inventory practices.

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Mobile Edge Computing (MEC) frameworks improve real-time data processing and system scalability by making networked game AI NPCs more responsive and flexible. MEC-based frameworks are tested for latency reduction and NPC real-time performance in complex and dynamic environments. Simulated and real-life user experiments evaluated the proposed system's response times, accuracy, and latency. Python simulations of network settings with different NPC concentrations and complexity produced massive datasets. NPC behaviour feedback was collected from 100 diverse users of various ages, genders, gaming experiences, and preferences. In low- to medium-density scenarios, the edge computing framework improved NPC responsiveness with low latency and high accuracy, enhancing player immersion. Due to the environment's complexity and NPC density, response times increased and accuracy decreased, requiring further optimisation for harsher conditions. Despite bugs and repetitive behaviours that suggested the Likert scale could be improved, the qualitative results praised the NPCs' lively conversation and realistic movements. Edge computing improves game AI and NPC realism with adaptive responses and real-time data processing. Scaling NPC densities and integrating edge computing with game architectures require more research. Next, improve NPC AI algorithms, reduce computational complexity and scalability, and expand testing environment game scenarios. Edge computing and AI techniques like deep learning and natural language processing can create immersive and engaging gaming experiences. This may present new gaming industry challenges and opportunities for innovation. Edge computing's real-time data processing and adaptive responses may change video game non-player characters.