Data Processing System Research Articles

Multi-Threaded Data Communication in Java for Advanced Computing Environments

The performance of operating systems like computers requires the proper functioning of the computer language interpreter. This interpreter follows various types of programming languages that make the performing of computer programming easy and effective. The application of the programming language of Java helps in processing multiple tasks at once. This research analyzes the novelty of Java-based data communication models in advanced computing services. This performance makes the saving of the resources used for the development of the programming language. All these development includes the performing of the multiple threads communication data processing. These multiple threads help distribute the single processed input in the multi-channel language processing, thus helping the work competition in time. It also reduces the cost of maintaining the programming languages reduced. Thus, the implementing cost of resources required for programming performance is reduced. Therefore, this implementation impacts the programmer to become more indented to use the language transformation process of Java. Moreover, it creates a more effective representation of the audio or visual content represented by a multi-tasking operating system. In this process of development in the language transformation of the operating system, the ability of the operating system for data processing improves. The systematic process of this language transformation helps in systematically transforming multiple programs at once.

ExDe: Design space exploration of scheduler architectures and mechanisms for serverless data-processing

Serverless computing is increasingly used for data-processing applications in both science and business domains. At the core of serverless data-processing systems is the scheduler, which ensures dynamic decisions about task and data placement. Due to the variety of user, cluster, and workload properties, the design space for high-performance and cost-effective scheduling architectures and mechanisms is vast. The large design space is difficult to explore and characterize. To help the system designer disentangle this complexity, we present ExDe, a framework to systematically explore the design space of scheduling architectures and mechanisms. The framework includes a conceptual model and a simulator to assist in design space exploration.We use the framework, and real-world workloads, to characterize the performance of three scheduling architectures and two mechanisms. Our framework is open-source software available on Zenodo.

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe).

Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correction.

The residual error was a critical indicator to measure the data quality of ocean color products, which allows a user to decide the valuable envisioned application of these data. To effectively remove the residual errors from satellite remote sensing reflectance (Rrs) using the inherent optical data processing system (IDAS), we expressed the residual error spectrum as an exponential plus linear function, and then we developed neural network models to derive the corresponding spectral slope coefficients from satellite Rrs data. Coupled with the neural network models-based spectral relationship, the IDAS algorithm (IDASnn) was more effective than an invariant spectral relationship-based IDAS algorithm (IDAScw) in reducing the effects of residual errors in Rrs on IOPs retrieval for our synthetic, field, and Chinese Ocean Color and Temperature Scanner (COCTS) data. Particularly, due to the improved spectral relationship of the residual errors, the IDASnn algorithm provided more accurate and smoother spatiotemporal ocean color product than the IDAScw algorithm for the open ocean. Furthermore, we could monitor the data quality with the IDASnn algorithm, suggesting that the residual error was exceptionally large for COCTS images with low effective coverage. The product effective coverage should be rigorously controlled, or the residual error should be accurately corrected before temporal and spatial analysis of the COCTS data. Our results suggest that an accurate spectral relationship of residual errors is critical to determine how well the IDAS algorithm corrects for residual error.

Effective decision support systems in clinical practice and prevention: literature review

Clinical decision support systems (CDSS) often outperform human capabilities for processing a large amount of information, dramatically simplifying the work of specialists and avoiding medical errors. The implementation of such systems is a complex task that requires high-tech developments. The annual increase in the development of such systems has a geometric progression. However, it is unclear if most of them will be integrated into clinical practice and recommendations. The use of CDSS to address various disease diagnosis, treatment, and prevention issues is demonstrated, and possible linkages between scientific clinical observations and CDSS are examined. Currently, many data gathering and processing systems use machine learning algorithms and convolutional technologies to create CDSS, resulting in data that exceeds the ability of human thinking to determine the logic of recommended decisions. This study presents the most studied modern CDSS, the possibilities of their application, and the implementation issues.

Development of a multifunctional real-time data processing system for interferometers on EAST

In the latest campaign of EAST experiments, two new interferometers were installed, a dispersion interferometer (DI) based on a Carbon Dioxide laser and a solid source interferometer (SSI) based on microwave multiplier sources. To make them available for the Plasma Control System (PCS) system, each of them needs to be provided with a real-time processing system to extract the detector output signal and afterward obtain the electron density information through signal processing. To obtain interferometer data quickly and reliably, a unified hardware template was applied to both interferometers. Three main parts are included in this hardware template — digitization, digital signal processing, and output modules. In particular, the digitization section uses a multi-channel Analog-to-Digital Convertor (ADC) to acquire the signals that need to be calculated. The digital signal processing part is implemented by an FPGA, which is the hardware basis of this multifunctional template that can be used for multiple interferometers. This section includes two main parts, wrapped density signal extraction and signal unwrapping. For DI it is a phase calculation based on the intensity ratio, while for SSI it is a phase calculation by means of FFT or phase demodulation. Both computational approaches form independent IP cores to build systems quickly, while these IP cores provide parameter interfaces to adapt to different application scenarios. The output module provides a variety of data transmission methods, including fiber optic transmission and Digital-Analog-Convertor (DAC) based analog transmission, to interface with PCS or other subsequent systems.This data processing system template has been applied to each of the above two interferometers and valid data were obtained in a recent EAST experiment campaign. It demonstrates the usefulness of the template and provides a reference for the design of data processing systems on future devices.

Improving the Constructive Accuracy of Pipe-Mill Machinery Through the Use of Laser 3D Systems and Mathematical Models for Data Processing

Improving the Constructive Accuracy of Pipe-Mill Machinery Through the Use of Laser 3D Systems and Mathematical Models for Data Processing

Latituditual Structure of Dayside Polar Cusp Precipitation

The results of observations of low-altitude spacecraft crossing the daytime sector of the auroral zone and of high-apogee spacecraft in the equatorial plane of the magnetosphere were analyzed in order to identify the main processes leading to the formation of dayside polar cusps. Observations from the DMSP F7 spacecraft were used to analyze the latitudinal characteristics of ion precipitation in the cusp region and to study the latitudinal profile of ion pressure in the cusp depending on the IMF parameters. A significant difference was found in identifying the cusp boundaries using an automated data processing system and direct analysis of spacecraft observations. It is shown that for small negative values of the Bz-component of the IMF (〈Bz〉 = –3.0 nT), an ordinary feature of the cusp is the latitudinal profile of the ion pressure (Pi) with a width of ~1° of latitude with two maxima, one of which is located in the equatorward and the other in the poleward of the cusp. For large negative Bz values (–6, –8 nT), the polar maximum in the latitudinal profile Pi disappears; only the equatorial maximum remains, the Pi level at the maximum increases, and the width of the cusp decreases to ~0.7°. For Bz IMF 0, the most characteristic is the Pi profile with a maximum ion pressure in the polar part of the cusp. The cusp for Bz 0 is located at higher latitudes than for Bz 0, and its average latitudinal width increase to ~1.4° of latitude. In the prenoon sector MLT, the most typical for periods with a large negative By-component of the IMF (〈By〉 = –6.3 nT, 〈Bz〉 = –1.7 nT) is a cusp with a width of ~1.4° of latitude with a flat top in the latitudinal Pi profile. Comparison of the pressure distributions observed at low heights with data from high-apogee satellites confirmed the possibility of describing the formation of the cusp as a diamagnetic cavity and using observations in the cusp to determine the ion pressure in the magnetosheath.

WITHDRAWN: Efficient fragmentation and allocation on clustering in distributed environment (FACE)

Distributed data processing systems require a reliable storage structure that ensures consistent, high-scale, and scalable data access for all users. Data fragmentation and subsequent allocation are important aspects of a flexible distributed database system. Existing techniques may result in significant data loss while performing data allocation and fragmentation. To overcome these issues a novel fragmentation and Allocation on Clustering in Distributed Environment (FACE) is proposed for reliable storage with scalable data access for all users. Initially, the user query is given to distributed database system (DDBS), then the query is fed to the query parser. The query parser parses and converts a textual query into a query object. After parsing the query data, it is passed to the query optimizer, which optimizes the query to access the data more efficiently. In the next step, the query is fragmented using dual (horizontal and vertical) fuzzy C means clustering. In Dual fuzzy C means clustering, the vertical fragmentation focuses on clustering attribute values that are frequently accessed together, and horizontal fragmentation focuses on grouping records that meet data selection criteria. After fragmentation, the query is allocated using sequential rule mining, in which queries occur in sequential order. The allocated query is optimized using the Efficient Fragment Security (EFS) algorithm to normalize the parameters to achieve better results. The performance metrics of the proposed techniques are calculated based on Final Prediction Error (FPE), and Execution time. The proposed FACE achieves a lower execution time of the 1.56 s. The result shows that the proposed FACE decreases the overall execution time lower than the 4.70 s, 4.46 s, 3.3 Sec, and 3.14 s in MAPA, DMA, BBE, and RATS-HM respectively.

Improving the efficiency of diagnosing errors in computer devices for processing economic data functioning in the class of residuals

This paper proposes an improved method for diagnosing errors of computer devices, which process economic data in computer systems, by increasing efficiency. The object of research is the processes of control and diagnosis of data errors, in the class of residuals (CR). The improved method of diagnosing economic data represented in the non-positional system of calculation in CR is based on the application of orthogonal bases of partial sets of bases. The use of these bases makes it possible to organize the process of parallel processing of the projections of the output number of the non-positional code structure. This makes it possible to increase the efficiency of data diagnostics by n times, depending on the length of the bit grid of computer systems. For a single-byte computer system, the efficiency of data diagnosis increases by 1.2 times compared to the existing method based on the zeroing principle. At the same time, the effectiveness of operational control and diagnostics systems for data processing in CR with the growth of bit grids has been proven. Based on the results, it is shown that, unlike the correction codes used in the positional counting system, the arithmetic codes in CR have additional correction capabilities. An example of a specific implementation of the process of applying operational control and diagnosing errors in the processing of economic data presented in CR is given. The improved diagnostic method will make it possible (in comparison with existing diagnostic methods) to reduce the time, which increases the efficiency of the diagnostic procedure of data operating in CR. The results give grounds for asserting that, based on the improved method and the developed algorithm for the implementation of data diagnostics, it is possible to synthesize a device for reliable and operational control and diagnostics of economic data operating in CR

SISTEM INFORMASI INVENTORI PERSEDIAAN BARANG PADA PT. YASA BERKAH MANDIRI MENGGUNAKAN PHP DAN MYSQL

The development of computer-based information systems at this time has become a major requirement for every company on a large or small scale as a data processing system. Not infrequently the general problem of a company engaged in the procurement of goods at this time is that there are errors in several processes of processing and conveying information. Especially in the process of incoming and outgoing transactions of goods from the warehouse and then reporting the results of the transaction process and managing the inventory of goods needed by management, there are often errors and discrepancies. The purpose of this study is to produce an Inventory Inventory Information System application that is expected to be useful for the company concerned, namely to facilitate the company in carrying out the process of recording goods transactions in and out of the warehouse and produce more accurate report data. The research method used is qualitative description. Results the use of PHP and MyQSL is very effective and plays a role in inputting data. Conclusions and suggestions are that the use of PHP and MyQSL can increase the effectiveness and efficiency in data processing. It is hoped that there will be good cooperation, discipline in work, and the need for system updates so that it can facilitate data entry.

Perancangan Sistem Informasi Penjualan Berbasis Web di PT. MARKTEL

Information systems are needed in every company to facilitate fast and accurate data processing. One of them is a sales information system currently used at PT. MARKTEL still has processes that have not been computerized. To meet these information needs, especially to deal with data processing problems, report generation and systems that have not worked as expected, companies need a computer-based information system. The system development method in this study uses the waterfall method which has a systematic and clear flow so that the system development is more detailed. The waterfall method is a structured modeling-based method consisting of Flowmaps, Context Diagrams, Level 0 Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD). In designing and implementing the program, the author uses the PHP programming language with MySQL database. The existence of a web-based sales information system at PT.MARKTEL is expected to simplify and speed up data processing and report generation and can minimize errors so that company goals can be achieved optimally.

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

The paper addresses a highly relevant and contemporary topic in the field of data processing. Big data is a crucial aspect of modern computing, and the choice of processing framework can significantly impact performance and efficiency. The technical revolution of big data has changed how organizations handle and value large databases. As data quantities expand quickly, effective and scalable data processing systems are essential. MapReduce and Apache Spark are two of the most popular large data processing techniques. This study compares these two frameworks to determine their merits, shortcomings, and applicability for big data applications. Nearly quintillion bytes of data are created daily. Approximately 90% of data was produced in the previous two years. At this stage, data comes from temperature sensors, social media, movies, photographs, transaction records (like banking records), mobile phone conversations, GPS signals, etc. In this article, all key big data technologies are introduced. This document compares all big data technologies and discusses their merits and downsides. Run trials using multiple data sets of varying sizes to validate and explain the study. Graphical depiction shows how one tool outperforms others for given data. Big Data is data generated by the rapid usage of the internet, sensors, and heavy machinery, with great volume, velocity, variety, and veracity. Numbers, photos, videos, and text are omnipresent in every sector. Due to the pace and amount of data generation, the computing system struggles to manage large data. Data is stored in a distributed architectural file system due to its size and complexity. Big distributed file systems, which must be fault-tolerant, adaptable, and scalable, make complicated data analysis dangerous and time-consuming. Big data collection is called ‘datafication’. Big data is ‘datafied’ for productivity. Organisation alone does not make Big Data valuable; we must choose what we can do with it.

Ankara AMATEM Birimi Başvurularının Geriye Dönük Analizi: Pandemi Yıllarına Ait Bir Değerlendirme

Aim: In this study, it was aimed to determine the risk groups, diagnoses and co-diagnoses of addictive disorders and to determine the demographic and clinical variables that might affect addiction by examining all applications to the Ankara AMATEM unit during the COVID-19 pandemic. Materials and Method: The sample of this retrospective and cross-sectional study consisted of individuals aged 14-83 years who applied to the AMATEM Clinic of Ankara Training and Research Hospital between 11.03.2020-11.03.2022 for forensic or individual reasons. Data were obtained by retrospective examination of the archive records of the hospital data processing system and subjected to statistical analysis. Results: Of the 10508 participants, 10.2% (n=1071) were female and 89.8% (n= 9437) were male. The mean age was 32.13±9.92 years. Of those diagnosed with an addiction-related disorder, 9.9% (n=696) were female and 90.1% (n=6316) were male. The most common diagnosis was opiate use disorder (40.5%), the second most common was multiple drug and substance use disorder (15.5%), and the third most common was alcohol use disorder (8%). The most common psychiatric comorbidity was anxiety disorder (1%), followed by depression (0.3%). Conclusion: During the pandemic period, the prevalence of addictive disorders was higher in the male gender and young adults. Opiate users were the most frequent users in children and adults. The pandemic period, which constitutes multifaceted stress, may contribute to vulnerability to addictive disorders. Prospective follow-up studies are needed to understand the effects of the pandemic on addictive disorders.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Excitation and modulation of exchange spin waves in CoFeB films

Spin waves in nanostructures have great potential to be future spintronic information carriers. Here, we report the excitation and modulation of exchange spin waves in Co20Fe60B20 (CoFeB) films by varying magnetic anisotropy. In a 50 nm-thick CoFeB film, the effective excitation of perpendicular standing spin waves (PSSWs) is achieved due to the uniaxial anisotropy induced by oblique sputtering. Additionally, patterning the films into a periodic array of rectangles enables the excitation of both exchange-dominated surface spin waves and PSSWs, with the modulation of spin waves achievable by altering the length–width ratio of the rectangle. It is worth noting that PSSWs are present in the easy axis, whereas surface spin waves are observed in the hard axis, emphasizing the significant influence of shape anisotropy on spin waves. Furthermore, we demonstrate the existence of PSSW in a CoFeB/Pb(Mg1/3Nb2/3)O3-Pb0.7Ti0.3O3 (PMN-PT) multiferroic heterostructure. The resonance field of PSSW can be shifted by applying electric fields, which originates from the strain anisotropy driven by the piezoelectric effect. These findings are expected to provide valuable insights for the development of energy-efficient spintronic devices and high-frequency data processing systems.

Federated learning optimization: A computational blockchain process with offloading analysis to enhance security

The Internet of Things (IoT) technology in various applications used in data processing systems requires high security because more data must be saved in cloud monitoring systems. Even though numerous procedures are in place to increase the security and dependability of data in IoT applications, the majority of outside users can decode any transferred data at any time. Therefore, it is essential to include data blocks that, under any circumstance, other external users cannot understand. The major significance of proposed method is to incorporate an offloading technique for data processing that is carried out by using block chain technique where complete security is assured for each data. Since a problem methodology is designed with respect to clusters a load balancing technique is incorporated with data weights where parametric evaluations are made in real time to determine the consistency of each data that is monitored with IoT. The examined outcomes with five scenarios process that projected model on offloading analysis with block chain proves to be more secured thereby increasing the accuracy of data processing for each IoT applications to 89%.

Distributed Architecture of Geospatial Data Collection and Processing System based on Web Design Patterns

This article discusses the issues of improving the reactivity and stability of web-based geographic information systems. It proposes a solution based on the integration of well-known web design patterns, which provides high internal and low external coupling of modules in the plugin architecture of the application. A prototype system was developed utilizing web design and programming patterns such as Model-View-Controller pattern and message-based communication between modules. The experiment was conducted on the dataset based on the results of registering the values of the Earths geomagnetic field parameters and its variations. The experiment demonstrates increased performance when using the suggested approach. The results show a reduction in server response time by 47 %.

Research on Guangdong Province Meteorological Data Analysis System under Hadoop Environment

In order to better monitor and manage the real-time transmission of meteorological information in Guangdong Province and improve communication quality, the Guangdong Meteorological Information Center has organized the development of the Guangdong Meteorological Data Analysis System. The system has the characteristics of intuitive and easy to use, strong real-time performance, and convenient query and statistics, which better meets the real-time monitoring and time management requirements of meteorological information transmission in Guangdong Province. Hadoop is a popular big data computing oriented, open-source big data processing system. Its characteristics of high throughput, high fault tolerance and easy expansion make it widely used in all walks of life. Therefore, this article focuses on the characteristics of real-time reception of meteorological information in Guangdong Province, and introduces the development ideas, use of some new technologies, and page implementation of the Guangdong Meteorological Data Analysis System under Hadoop environment. In the Hadoop environment, it is possible to dynamically exchange data with the server without refreshing the entire page, improving the efficiency of data transmission between the server and client, making browsing more intuitive and convenient. Enriched the methods and capabilities of data processing.

IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENINGKATKAN POLA PENJUALAN

Medicine is a necessity for someone who is sold to people with disease. Therefore, every pharmacy or hospital must have a data processing system so that each transaction data can be used to make reports. From this report a useful result will be created to determine which drugs are most frequently purchased and sold so as to be able to determine the amount of stock at the pharmacy. But at the present time drug sales transaction data continues to increase every day so that it has accumulated because the system used is a system for storing or archiving bookkeeping without utilizing the transaction data, besides that the problem that often arises is the lack of maximum customer service at pharmacies because the drugs or needs that consumers are looking for are still not available. The a priori algorithm functions as a candidate for possible item combinations, then tests whether the combination meets the minimum support and minimum confidence parameters which are the threshold values ​​given by the user, so that it finds patterns in the form of products that will often be purchased together or products that tend to appear together in a transaction.