Real-time Graph Research Articles

Incremental Sliding Window Connectivity over Streaming Graphs

We study index-based processing for connectivity queries within sliding windows on streaming graphs. These queries, which determine whether two vertices belong to the same connected component, are fundamental operations in real-time graph data processing and demand high throughput and low latency. While indexing methods that leverage data structures for fully dynamic connectivity can facilitate efficient query processing, they encounter significant challenges with deleting expired edges from the window during window updates. We introduce a novel indexing approach that eliminates the need for physically performing edge deletions. This is achieved through a unique bidirectional incremental computation framework, referred to as the BIC model. The BIC model implements two distinct incremental computations to compute connected components within the window, operating along and against the timeline, respectively. These computations are then merged to efficiently compute queries in the window. We propose techniques for optimized index storage, incremental index updates, and efficient query processing to improve BIC effectiveness. Empirically, BIC achieves a 14× increase in throughput and a reduction in P95 latency by up to 3900× when compared to state-of-the-art indexes.

#1820 Use of MiDiálisis App in peritoneal dialysis patients

Abstract Background and Aims Technological innovation has revolutionized medical assistance. Peritoneal dialysis (PD) patients, who prioritize their autonomy and freedom choosing to undergo dialysis at home, are the paradigm of patients willing to use telemedicine. Remote monitoring allows real-time therapy supervision, ensures proper adherence and empowers the patients by promoting self-care and self-awareness The aim of this study is to assess the feasibility and acceptance of MiDiálisis App. Method MiDiálisis App, developed independently, integrates essential self-care data (such as blood pressure, ultrafiltration and weight), presenting it in real-time graphs and tables for both patients and nephrologists. It also allows asynchronous communication between them. This is a prospective observational pilot study in which MiDiálisis App was offered to PD patients at a tertiary hospital. Results Out of the 10 patients offered the use of the MiDiálisis App, 3 did not use it (NU) due to technical issues. Seven patients used (U) MiDiálisis for a median of 9 [4-11] months. The majority were male (n = 6) and on automated PD (n = 6) for a period of 30 (15-38) months. We registered 6.3 (5.6-6.7) days with interaction/week. NU patients were older (U 61 [54-69] years vs NU 68 [34-75] years, p = 0.5), with higher comorbidity Charlson index (U 5 [4-7] points vs NU 7 [3-9] points, p = 0.5), and greater Frail index (U 2 [1-2] points vs NU 3 [2-4] points, p = 0.067). Six patients completed a survey after using MiDiálisis. 100% perceived a better communication with their nephrologist and greater accuracy of data compared to their previous method (paper-based recording). All of them entered the data by themselves and found both learning and using MiDiálisis to be easy; 83% considered the app beneficial for their health, and 100% felt secure using it. Furthermore, 67% reported significant changes in their self-care habits, and 83% mentioned an increased awareness of their health status. All participants would recommend it to other patients. Conclusion MiDiálisis App is applicable and has good acceptance among people undergoing PD. It might be a useful tool that can improve self-care, adherence, and communication with these patients.

Developing centrifugal force real-time digital PCR for detecting extremely low DNA concentration

Digital PCR (dPCR) is a technique for absolute quantification of nucleic acid molecules. To develop a dPCR technique that enables more accurate nucleic acid detection and quantification, we established a novel dPCR apparatus known as centrifugal force real-time dPCR (crdPCR). This system is efficient than other systems with only 2.14% liquid loss by dispensing samples using centrifugal force. Moreover, we applied a technique for analyzing the real-time graph of the each micro-wells and distinguishing true/false positives using artificial intelligence to mitigate the rain, a persistent issue with dPCR. The limits of detection and quantification were 1.38 and 4.19 copies/μL, respectively, showing a two-fold higher sensitivity than that of other comparable devices. With the integration of this new technology, crdPCR will significantly contribute to research on next-generation PCR targeting absolute micro-analysis.

SIM: A fast real-time graph stream summarization with improved memory efficiency and accuracy

A graph stream composed of sequentially approaching arriving edges is commonly utilized to represent complicated structured data in interactive application systems. Since graph streams are extraordinarily vast and high velocity, efficient storage and analysis of graph streams face serious challenges. Current graph stream summarization schemes have effectively achieved the storage and management of graph streams. Unfortunately, they either cannot accomplish real-time queries or have low overall query accuracy. To this end, a novel summary structure, named Shared Interaction Matrix (SIM), is proposed for real-time queries rapidly and accurately with smaller memory. SIM is designed as a two-layer adjacency matrix with different structures to improve memory efficiency while preserving the key of heavy edges to support real-time measurement. Moreover, SIM leverages shared hash technology and an integral replacement strategy to boost insertion query speed and query accuracy. The performance of SIM is evaluated by conducting extensive experiments on the CPU and OVS platform. The experimental results show that SIM significantly enhances measurement accuracy and reduces insertion and query processing latency by 39.21%–93.50% while achieving real-time queries, compared with the state-of-the-art schemes.

Real-time Graph Visualization with JavaFX: Exploring Large-scale Network Structures

The ability to visualize and comprehend complex network structures is crucial in various domains, including social media analysis, computer networks, and transportation systems. However, visualizing large-scale graphs poses significant challenges due to computational limitations and rendering performance constraints. This research paper presents a novel approach to real-time graph visualization using JavaFX, a powerful Java-based framework for developing rich client applications. By leveraging efficient data structures, rendering optimizations, and multithreading techniques, our proposed system achieves real-time visualization of large-scale graphs, enabling users to explore and interact with dynamic network structures seamlessly. The system incorporates advanced layout algorithms and visual encodings to enhance the clarity and interpretability of the visualizations. Extensive experiments were conducted using real-world and synthetic datasets to evaluate the system's performance, scalability, and usability. The results demonstrate the effectiveness of our approach in rendering large graphs in real-time, outperforming existing techniques. Furthermore, a user study was conducted to assess the system's usability and gather feedback on interaction and exploration features, highlighting potential applications in various domains. Keywords— Graph Visualization, Real-time Rendering, JavaFX, Large-scale Networks, Layout Algorithms, Rendering Optimizations, User Interaction, Performance Evaluation.

Simulasi Sistem Monitoring Kenaikan Level Air pada Area Rawan Banjir Secara Real-Time berbasis Smartphone Android

Abstract
 Flooding is the most prevalent disaster that often occurs, flooding occurs due to an increase in water levels in flood-prone areas such as rivers, creeks, swamps and seas that can no longer be dammed which results in overflowing water, in controlling the increase in water levels, a technology is needed that is able to minimize casualties and property. So, the purpose of this research is to create a technology or monitoring tool for water level rise that can be monitored through an Android smartphone where the use of this system will make it easier for the community and local authorities to monitor real-time water level rise. This research uses Research and Development (R&D) research methodology and data is managed and taken through observation, literature study and validation methods. This system uses the HCSR-04 ultrasonic sensor as a sensor and ESP32 as a microcontroller, as an output result, the data will be in the form of two visualizations, namely real-time graphs and tables and notifications via email. The results of the tool system output are built based on progressive web app through Laravel PHP.

Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle II central drift chamber using Field-Programmable Gate Arrays (FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated preprocessing of graphs, we enable the deployment of novel Graph Neural Networks (GNNs) in first-level triggers of particle physics experiments.

Design Of Temperature System On The Electrical Tank Heater Using PLC

A temperature control system is required in any situationthat requires a certain temperature to remain stable.Likewise, in the electrical processtank heater that requires a control system, because the temperature is notby the set value will cause overheating it,causing the product temperature is not as desired and can even causea dangerous explosion. In this project, the design is carried outtemperature control on the prototype electrical tank heater usingPLC Nano V5.2 as the controller, and the control method used is fuzzy Sugeno.Fuzzy logic has a value of obscurity and obscuritybetween right and wrong and control systems. Membership value may varyfrom 0 to 1. This means a situation can have two values, “yes or no” and "right or wrong", so it seems as if there is a "gray" area. Fuzzy Sugenois a fuzzy inference method for the conditions represented inthe form of IF–THEN, where the system output is not a fuzzy set but a constant form. Test results on the prototype confirm that the systemcontrol with PLC as controller and fuzzy as control methodhas a good ability to adapt the time needed totake longer to reach a stable state at higher temperatures (33.5°C to 37°C).Then at lower temperatures (29°C to 33.5°C). Haiwell-capable interfaceschange set values, display real-time graphs, and send commands toset PWM.

Instant Graphification How to Graph and Display Student-Generated Data in Real Time ​

Instant Graphification How to Graph and Display Student-Generated Data in Real Time ​

LM-SRPQ: Efficiently Answering Regular Path Query in Streaming Graphs

Regular path query (RPQ) is a basic operation for graph data analysis, and persistent RPQ in streaming graphs is a new-emerging research topic. In this paper, we propose a novel algorithm for persistent RPQ in streaming graphs, named LM-SRPQ. It solves persistent RPQ with a combination of intermediate result materialization and real-time graph traversal. Compared to prior art, it merges redundant storage and computation, achieving higher memory and time efficiency. We carry out extensive experiments with both real-world and synthetic streaming graphs to evaluate its performance. Experiment results confirm its superiority compared to prior art in both memory and time efficiency.

A Deep Community Detection Approach in Real Time Networks

Community detection in real time networks is one of the important aspect of social network analysis. Deep learning has been applied successfully in a variety of research fields in recent years. Proximity matrix is frequently used as the representation of the network structure. However, there are issues with the proximity matrix's insufficient spatial contiguity information. As a result, this research provides a deep learning applied community identification approach that combines the reorganization of the matrices, spatial attribute uprooting, and community identification. For obtaining a spatial proximity matrix, the primary proximity matrices in a real time graph is recreated using the highest weight and adjacent users. The dimensional proximity matrix can obtain a subdomain of the network, allowing the convolutional neural network (CNN) to draw out dimensional localization more easily and fast. Ten different real time datasets of social networks are used in tests to examine our proposed approach. Our results show that the proposed community identification approach has higher compatibility than existing deep learning-based strategies. As a result, the proposed deep community identification approach is capable of detecting the excellent clusters in real time networks.

Design and Development of Flow Analyzer for Peak Inspiratory Flow (PIF) and Peak Expiratory Flow (PEF) Parameters.

The PIF and PEF parameters on the ventilator need to be taken into account to monitor the condition of patients undergoing mechanical ventilation. Both of these parameters need to undergo periodic testing to ensure that the ventilator can provide accurate information to its users. The testing of these two parameters can be conducted using a flow analyzer. The objective of this research is to develop a flow analyzer for PIF and PEF parameters using the AFM3000 flow sensor, with the results displayed on an LCD TFT screen in the form of graphs and numerical values. The research measurements were conducted in Volume Control (VC) mode with VT settings of 200, 300, 400, 500, and 600 mL. Data collection was done using two methods to obtain two different types of data. From the first data collection, the largest errors in reading PIF and PEF values were found to be 3.49% and 2.99%, respectively. The second data collection resulted in a sensor's change in reading constant flow of ±0.1 LPM. Overall, the research findings indicate that the AFM3000 sensor has good accuracy and stability. Additionally, the AFM3000 flow sensor has good sensitivity and very short delay, making it suitable for real-time graph display in the module.

Design and implementation of a remote monitoring system of mold cavity using eject pin in high-pressure die casting

ABSTRACT This research presents a design process for mould cavity monitoring with a temperature and force-generated pressure measurement system in high-pressure Die casting (HPDC). K-type thermocouple sensor was selected to measure the harsh environment of the Mould Cavity. An eject pin was processed and used for the installation of the sensor in the measurement system, and data was collected from the sensor and displayed as a real-time graph. The rationality of the obtained data was verified with comparative analysis with finite element analysis simulation (FEM) result as a reverse engineering approach. The proposed system is successfully measuring and monitoring the temperature and pressure inside the mould, allowing for the safe monitoring of Die casting. Through monitoring, abnormal conditions in the process can be detected. It is expected that this will reduce the defect rate and improve the quality of manufactured products.

Persistent graph stream summarization for real-time graph analytics

Persistent graph stream summarization for real-time graph analytics

Incremental Connected Component Detection for Graph Streams on GPU

Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we propose a new incremental processing method to solve the problems found in the existing methods for detecting connected components on GPUs. The proposed method minimizes the amount of data to be sent to the GPU by determining the subgraph affected by the graph stream update and by detecting the part to be recalculated. We consider the number of vertices to quickly determine the connected components of a graph stream on the GPU. An asynchronous execution method is used to shorten the transfer time between the CPU and the GPU according to real-time graph stream changes. In order to show that the proposed method provides fast incremental connected component detection on the GPU, we evaluated its performance using various datasets.

Design visual studio based GUI applications on-grid connected rooftop photovoltaic measurement

This article describes the design of a data system to integrate energy conversion from photovoltaic measurements connected to the power grid. The software used is visual studio, while the hardware uses polycrystalline photovoltaic (PV) with a capacity of 2.08 kW and several sensors that have been integrated into Arduino. Parameter data in measuring the performance of this PV system consists of temperature and humidity sensors to measure the panel surface, direct current (DC) current sensor, DC voltage sensor. To measure the current and voltage sourced from the electricity network, the module (PZEM-004T) is used. Measurements are designed using a graphical user interface (GUI) on a Visual Studio application that has been interfaced through Arduino programming. The data output on the sensor measurement will simultaneously record the circuit that has been connected to the solar panel and then display it visually in the form of tables and graphs in real time with a delay of 1 minute. The results of PV on grid measurements in sunny weather conditions obtained the maximum value of all measurements with a DC voltage of 221 V, while for an alternating current (AC) voltage of 231.60 V, the DC value reached 1827.17 W while the AC power was 1681 W.

RapidFlow

Continuous subgraph matching (CSM) is an important building block in many real-time graph processing applications. Given a subgraph query Q and a data graph stream, a CSM algorithm reports the occurrences of Q in the stream. Specifically, when a new edge e arrives in the stream, existing CSM algorithms start from the inserted e in the current data graph G to search Q. However, this rigid matching order of always starting from e can lead to a massive number of partial results that will turn out futile. Also, if Q contains automorphisms, there will be a lot of redundant computation in the matching process. To address these two problems, we propose RapidFlow, an effective approach to CSM. First, we design a query reduction technique, which reduces CSM to batch subgraph matching (BSM) where we enumerate all results in a region of G that will be affected by the update. The well-established BSM techniques can determine effective matching orders, not necessarily starting from the newly inserted edge. Second, to eliminate redundant computation caused by automorphisms in Q , we propose dual matching, which leverages the duality of Q and G in the matching process. Extensive experiment results show that RapidFlow outperforms state-of-the-art algorithms, including TurboFlux and SymBi, by up to two orders of magnitude on various workloads.

Pengembangan Karakterisasi Gelembung Mikro Menggunakan Metode PIV beserta Pemantauan dengan IOT

The development of microbubble technology has been widely used in various fields. One of these areas is sterilization technology using microbubbles to speed up and increase the effectiveness of the sterilization process. One example is room sterilization using vaporized ozone gas microbubbles which can minimize the spread of the Covid-19 virus. However, research and development of microbubbles are still not much related to the characterization of their size and hydrodynamic properties. In this study, we propose the development of a continuous characterization of the size of microbubbles using the image analysis method. Image analysis aims to find the displacement vector using particle image velocimetry (PIV) techniques. The Hadamard-Rybczynski equation was used to calculate the size of the microbubbles based on the rising velocity vector of the microbubbles. The image capture process uses an LED light shadow technique to get a brighter and more stable image. The template matching algorithm is used to speed up the displacement vector analysis process used in the PIV technique. The analysis process is carried out in-situ in parallel processing using the python program on the raspberry pi4 unit. The analysis process uses three program services that run parallel, namely recording, pre-processing, and processing services. The measurement process uses three validations, namely pixel validation, template matching algorithm validation, and interrogation window. Bubble size data is displayed in the form of real-time graphs and size distribution histograms online using IoT. The test results show the size distribution of the microbubbles produced has an average of 14.60 µm with a deviation value of 0.11µm.

An Anthropocentric and Enhanced Predictive Approach to Smart City Management

Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city’s assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly.

An electronic audit tool to track the donning and doffing practices of personal protective equipment in the emergency department

An electronic audit tool to track the donning and doffing practices of personal protective equipment in the emergency department Maya Jusza, Ramya Sridhar, Jennifer Woods, Sharon Drury Background: Maintaining the safety of patients and healthcare professionals is a priority in all healthcare settings. Infection prevention measures such as donning and doffing practices of personal protective equipment (PPE) have become even more imperative in light of the SARS-CoV-2 pandemic. Potential PPE breaches and the degree of frontline compliance are currently being analyzed through the use of paper PPE audit tools which can be laborious and time-consuming. The development of an electronic alternative would improve frontline safety and enhance the efficiency of data collection, while optimizing the ability to share these observations with the frontline team in real time. Two nursing leadership students from the University of Alberta were tasked with developing an electronic PPE audit tool prototype for the University of Alberta Hospital’s emergency department. Implementation: An electronic PPE audit tool prototype was developed using Google Forms which provided a user-friendly interface. Google applications were used as no confidential or patient data was collected during PPE audits. The prototype auto-populated the data entries into linked spreadsheets and interactive data dashboards that visualized the data using graphs in real time. This enabled users to easily identify trends and direct educational interventions as required. Instructional one pagers and screencast videos were also created to accompany the prototype. The prototype was reviewed by and received extensive support from: Unit Managers, Patient Care Managers, Process Improvement Nurses, Infection Prevention Control (IPC), the Executive Director of the University of Alberta Hospital and Stollery emergency department and Edmonton Zone medicine programs, and the University of Alberta Hospital and Mazankowski Executive Leadership Team. Several changes and improvements were made using the Plan-Do-Study-Act cycle. This prototype has currently been replicated onto an Alberta Health Services (AHS) server and has completed the formal testing phase with a planned application launch date. Evaluation Methods: Plan-Do-Study-Act cycles were used to guide the implementation of this audit tool prototype. After development, the prototype was tested and revised which included six rounds of audit trials at the University of Alberta Hospital’s emergency department and on some inpatient medicine units. This prototype was consistently evaluated at various stages of development and changes were made to include feedback. After approval was received to recreate this prototype onto an AHS compatible server, additional changes were made to ensure functionality. These changes included adding designations and simplifying certain questions. IPC was consulted to ensure the steps outlined for donning and doffing in the prototype were accurate and reflected requirements in the clinical environment. Results: This audit tool prototype has gathered tremendous support through various demonstrations of its ability to streamline data collection in the healthcare setting. This data is relevant to the safety of both frontline workers and patients as it identifies inconsistencies in donning and doffing practices. In addition, the prototype also complements the Edmonton zone-wide PPE coaching initiative by allowing for a quantitative measurement of its efficacy. This has prompted the fast-tracked replication of an AHS compatible version with the assistance of a dedicated team that includes the creators of the prototype tool, IPC, Quality Assurance, Information Technology, and Clinical Services Development. This version has a scheduled launch date on March 22, 2021 and is to be initially rolled out to University of Alberta’s emergency department and medicine units. The objective is to eventually make this the standardized PPE audit tool throughout Alberta. Advice and Lessons Learned: 1) In order to be sustainable and implemented site-wide, an AHS compatible tool isrequired. The use of Google applications is not preferred as data will be stored outside ofthe AHS server. Even though there is no confidential information, wide-spread use mayoverwhelm the Google platform and a Gmail account is required to view data. An in-house AHS alternative has been developed. 2) Several discussions took place regarding discretions on what steps can be auditedaccording to IPC protocols. For example, some clinical nurse educators prefer handhygiene to be completed between donning steps, while this is not mandatory or auditableaccording to IPC. In addition, several discussions took place to identify the operationaland business owners of this tool which are required to support the AHS compatibleversion of the application. 3) PPE audits are vital across all hospital departments to improve the quality of healthcare.The use of PPE during patient care has grown exponentially due to the SARS-CoV2pandemic and has amplified the need for an electronic alternative to the existing paperPPE audit tool. The electronic audit tool offers an innovative way to accurately andefficiently collect and display data which will promote an improved quality of care.