Data Processing Operations Research Articles

How to specify the content of a cookie consent request

This paper provides an introduction to the information that forms part of a cookie consent request as well as describes the components that need to comprise a data processing operation, including cookies, which would ensure that the consent given is specific and well informed. The paper uses the Google Analytics (GA) script as an example. It presents a ‘full’ specification for the consent request to use the GA script. The paper ends with a few remarks on how to present the information in the consent request in a clear and comprehensible manner so as to enable the user to comprehend the functioning of the GA script.

SeisIO: A Fast, Efficient Geophysical Data Architecture for the Julia Language

Abstract SeisIO for the Julia language is a new geophysical data framework that combines the intuitive syntax of a high-level language with performance comparable to FORTRAN or C. Benchmark comparisons against recent versions of popular programs for seismic data download and analysis demonstrate significant improvements in file read speed and orders-of-magnitude improvements in memory overhead. Because the Julia language natively supports parallel computing with an intuitive syntax, we benchmark test parallel download and processing of multiweek segments of contiguous data from two sets of 10 broadband seismic stations, and find that SeisIO outperforms two popular Python-based tools for data downloads. The current capabilities of SeisIO include file read support for several geophysical data formats, online data access using a variety of services, and optimized versions of several common data processing operations. Tutorial notebooks and extensive documentation are available to improve the user experience. As an accessible example of performant scientific computing for the next generation of researchers, SeisIO offers ease of use and rapid learning without sacrificing computational efficiency.

Development of hardware and software platform for collection and processing of operational data of agricultural equipment

The article denotes to the issues of the use of modern digital technologies for the development of mobile monitoring system for technical condition of the agricultural machinery for predicting the resource and planning the maintenance. (Research purpose) The research purpose is developing a structure and hardware and software tools for creating a platform for collecting and processing operational data of agricultural machinery. (Materials and methods) Based on modern microcontroller technology, authors created a two-level structure of the hardware and software platform, a scheme of first-level modules designed to collect data from sensors and pre-process it, a scheme of a second-level module for collecting information from first-level modules, structuring it and transmitting it to a remote server via a GPRS channel for storage, processing and analysis. The article presents the developed database structure and site layout on a remote server to control incoming information and graphical representation of the dynamics in measured parameters. (Results and discussion) The created platform has been installed on the Kioti CK22 tractor for experimental research and demonstrated at the agro-industrial exhibition-forum “Field Day of the Ryazan region-2019” on July 19, 2019. (Conclusions) With relatively low hardware costs, the developed platform has sufficient functional and computational resources to implement mobile monitoring of the technical condition of units and aggregates of agricultural machines and their compliance with technological modes of soil treatment. The universal structure of the developed platform can serve as a basis for digitalization of many other objects of agricultural production in such industries as animal husbandry; crop production; accounting, storage and processing.

ДОСЛІДЖЕННЯ РОБОТИ ШТУЧНОЇ НЕЙРОННОЇ МЕРЕЖІ, ЯКА ПРАЦЮЄ В ЕНЕРГОСИСТЕМІ З МЕТОЮ ЗАПОБІГАННЯ АВАРІЙНИХ РЕЖИМІВ

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models

Penerapan Haversine Formula Pada Penerimaan Peserta Didik Baru Jalur Zonasi

The government through the Ministry of Education and Culture issued a regulation on the admission of new students as stipulated in the Minister of Education Regulation Number 51 of 2018 to apply the zoning system. The regulation emphasizes that schools are required to give priority to prospective students who are in the domicile of the closest radius to the location of the school. To facilitate relevant agencies and the public in getting the closest school information in a zone, it is necessary to create an information system that can present information related to the existence or location of the school. One of the systems designed to solve and work with spatially referenced data or geographic coordinates is the Geographic Information System (GIS). Geographical Information Systems have the ability to perform data processing and freeze certain operations by displaying maps and able to analyze data properly. Research carried out applies the haversine formula in the search for the location of the closest school. To build digital maps using Google Maps, and developed with mobile services. The Google Maps API presents the programming functions provided by Google Maps for integration in the web or the application that is being created. While information about school data is presented and integrated in the goegraphic application.

Light Drone-Based Application to Assess Soil Tillage Quality Parameters.

The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces.

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

Information security evaluation for Android mobile operating system

One of the main directions of information systems development is to increase the efficiency of collecting, processing and exchanging information through the introduction of modern data transfer technologies, automated remote monitoring and control. The cornerstone of this concept is mobile devices that solve the issue of operational data exchange and processing. Modern mobile services used including the exchange and processing of personalized, banking and critical data are the result of the steady increase in the number of crimes in the field of information security in relation to and using mobile devices. The widespread use of these devices for access to protected information contained in information systems has given special importance to the issue of information security.The subject of this research is to assess the current state of information security mechanisms for mobile operating systems that form the basis of the structure of episodic distributed mobile networks. The Android OS (operating system) was used as an example. The article discusses the problems of the development of a mobile ecosystem and methods aimed at solving them, the main vectors of malicious impact, ways of countering the means of static and dynamic analysis and modern protection mechanisms.

The methods for processing and storage of data in global navigation satellite systems for the task of perimeter protection

The methods for processing and storage of GNSS data for the task of perimeter protection are described. The article proposes a basic algorithm for the operation of a GNSS data collection, processing and storage system that allows you to track processed GNSS data on a remote web service in public formats, as well as send signals through a client mobile application to available platforms and systems about temporarily suspending or canceling GNSS data recording.

Using Warshall to Solve the Density-linked Density Clustering Algorithm

Clustering algorithm has a wide range of applications in data mining, pattern recognition and machine learning. It is an important part of data mining technology. The emergence of massive data makes the application of data mining technology endless. Cluster analysis is the basic operation of big data processing. The clustering algorithm is to divide similar elements into one class, and to divide elements with large differences into different classes. Aiming at the computational complexity of the density clustering algorithm, this paper proposes an improved algorithm W-DBSCAN which uses Warshall algorithm to reduce its complexity. In the density clustering algorithm, the data with high similarity are densely connected. In this paper, aiming at the complexity of the density clustering algorithm, an improved algorithm W-DBSCAN using the Warshall algorithm to mitigate its complexity is proposed. In the density clustering algorithm, the data with high similarity is density-connected. This paper constructs a matrix <i>n</i>×<i>n</i> where the element (<i>x</i>, <i>y</i>) is marked as 1 means that the data x and data y are directly reachable, and then the reachability matrix of the matrix is calculated using the Warshall algorithm. The solution density connection problem is transformed into the solution reachability matrix problem, thus reducing the complexity of the algorithm.

Tipping bucket raingauge with GPRS data logger DL-2016: Specifications, installation, and analysis

Rain sustains life by providing the means for transferring water from the atmosphere to the surface, and it is an important measure and predict local and global rainfall behavior to understand the water cycle better. Mainly agricultural engineers, civil engineers want to know the precipitation statement and capacity of water for the catchment area. For that, install a rain gauge and data analysis. Among all rain gauges (recording and nonrecording), the tipping bucket rain gauge works very efficiently, accurately, and economically also. For this project, selected tipping bucket rain gauge with GPRS based data logger DL-2016 for data storage with a longer time. A placed rain gauge on top of the roof with the constructed block (dimensions 0.46 * 0.46 m2, and the height is 1 m). We built the block with the aspects of the regular brick. In the neighborhood, we constructed the concrete bed with 0.15 m height with M30 grade concrete. On the practical bed, we placed two wooden blocks with dimensions of 0.08 * 0.45 m2 after rain gauge set on the woodblocks. After construction of the block and installation of the rain gauge, have to give a power connection to a data logger with extended wire. Actually, can not place data logger under precipitation due problematic with data storage and power connection. Hypertrm software used to retrieve the precipitation recordings from the GPRS based data logger DL-2016. The user has to perform, however, several additional data filtering and processing operations before storing the records in the final database. From GPRS based datalogger DL-2016. Major all industries, irrigation, drinking, and municipalities depend on precipitation. In this project, selected recording type of rain gauge for determining annual data and installation in ABR College. From rain gauge, get data for a given time to analyze and calibrate precipitation. Hydrographs do calibration and analysis in that unit-hydrographs. Draw hydrograph From rain gauge for precipitation data in ABR collage catchment, we are installing tipping bucket rain gauge (recording type rain gauge) on the top floor with following standards from the data logger we get data in terms of precipitation intensity with time. So from this data, we can analyze precipitation in the ABR college as a catchment area.

РАЗВИТИЕ АПК С ИСПОЛЬЗОВАНИЕМ ЦИФРОВЫХ ТЕХНОЛОГИЙ КАК УСЛОВИЕ МОДЕРНИЗАЦИИ РЕГИОНАЛЬНОЙ ЭКОНОМИКИ

At the present stage of development of the agro-industrial complex (AIC), the role of scientific research and the implementation of their results in the agrarian sector has significantly increased. This is due to the activation of the agricultural sector in the innovation sphere and the emergence of a new form of financing capital investments for agricultural enterprises, i.e. risk capital. The transition of the agro-industrial complex economy to an innovative track makes it possible to pursue a flexible policy in relation to the competitiveness of products. The active introduction of digital technologies in the production and marketing of agricultural products is aimed at bringing society and industry to a new technological level. Advantages of digital technologies: introduction of remote operation, reduction of the cost of manufactured products, free access to the market for all agribusiness entities, facilitation of data collection and processing operations, etc. As technologies continue to develop, digital technologies also continue to expand, since the range of goods and services in the agro-industrial sector of the national economy is constantly growing. However, studies have shown that not all companies in the agricultural sector in the Russian market have the opportunity to implement an innovation policy, and most importantly, not everyone knows how to attract investment in their project and get financial benefits from it. The work revealed the reasons that hinder the work of the agro-industrial complex of the Russian Federation. On the basis of the research carried out, the forms of organizing innovation processes were proposed, which are most acceptable for the functioning of agricultural organizations. These forms may in the future help to overcome the problems in the modernization of the agro-industrial complex economy. In accordance with the peculiarities of the nature of production in the agricultural sector, criteria for assessing the innovativeness of enterprises were determined, which makes it possible to choose the direction of investment.

Date

The problem of interpreting the emerging terms of the digital economy is discussed. Examples demonstrating different understanding of the term “digital economy” by economists and information technology experts are given. We consider the basis of the digital economy - the cyberspace, as an environment of people’s activity in which any interactions are carried out due to digital data. The complexity of organizing cyberspace requires approaching the definition of the concept of the digital economy in terms of functional and structural descriptions. The functional approach has made it possible to identify three key attributes that reveal the essence of the digital economy: digital data, digital infrastructure, models of people’s activities. The activity is carried out through human interaction with natural objects through the "intermediary" - digital data. Changes in activities caused by the work with digital data, have come to be called transformation, its modern examples being medicine and transport. The examples show that the models of the digital economy involve a partial or complete rejection of human labor. The official government documents of the Russian Federation follow the functional approach to describing the concept of the digital economy: the program “Digital Economy of the Russian Federation”, decree of the President of the Russian Federation “On the Strategy for the Development of the Information Society in the Russian Federation for 2017-2030”. The structural description has made it possible to single out three echelons of managing the digital economy: the technological environment, the social environment, markets and industries. Each echelon has the right to make decisions within its competencies. The purpose of the technological echelon is the technical support of the digital economy, including information systems and information technology. At the level of the social environment of the digital economy, the following ones are formed: the legal regime for the development and use of digital technologies, the education and training system of the digital economy, the infrastructure of the digital economy, information security technologies for digital processes, and many others. At the level of markets and industries, spheres of activity of the digital economy are implemented. The structural approach has allowed us to propose an architectural model of the digital economy. The model is a stratified description, characterized by vertical decomposition, the sequence of digital data processing operations on each stratum, and the interconnection of performance indicators of all strata for a comprehensive assessment of the activity process as a whole.

Performance Analysis of Distributed Processing System using Shard Selection Techniques on Elasticsearch

In distributed systems multiple computers interact with each other over a network to perform data processing operations parallel over a network. Distributed systems can be setup depending on the application and its data with different combination of replications and then the partitioning of the data can be performed using sharding technique to enhance the scalability, consistency and fault-tolerance issues. The gaps identified in existing sharding techniques are, it is difficult to determine which particular shard or partition need to be searched to retrieve the relevant document matching to the user query. And also to rank the documents which has highest relevance to the user query. The main goal is to develop a mechanism to handle large scale data processing and searching operations using efficient sharding technique. The proposed paper aims to enhance the performance of distributed processing systems by applying effective shard partitioning and efficient shard selection techniques and perform the comparative study analysis of shard selection techniques considering precision, MAP and cost measures. The results interpret that sharding technique provides efficient mechanism to handle large scale data. Effectiveness and efficiency of shard selection techniques interpret that Rank-S algorithm performance better compared to CORI and Redde algorithms by reducing the overall cost by 28%. Sharding technique is an efficient mechanism to process big data, and provides better scalability and fault-tolerance.

Использование Big Data в международном бизнес

The term BigData causes a lot of controversy among specialists, many of whom note that it only means the volumes of accumulated data, but do not forget about the technical side, the considered direction includes technologies for computing, storage, and also service services. Big Data is a term that denotes technologies for processing large unstructured and structured data to obtain results that are understandable and useful to humans. In business, Big Data is used to support the adoption of transformations by a manager (for example, based on an analysis of financial indicators from an accounting system) or a marketer (for example, based on an analysis of customer preferences from social networks). By themselves, Big Data algorithms appeared with the introduction of the first mainframes (high–performance servers), which have the necessary resources for operational data processing and are suitable for computer calculations and subsequent data analysis. As the number of embedded computers rises due to falling processor prices and the ubiquity of the Internet, so too is the amount of data transferred and then processed (often in real time). Therefore, we can assume that the importance of cloud computing and the Internet of Things will increase in the coming years. It should be noted that Big Data processing technology boils down to three main areas that solve three types of tasks: (1) translation and storage of incoming information in gigabytes, terabytes, petabyte, etc. for their processing, storage and application in practice; (2) structuring of disparate content, namely: photos, texts, audio, video and all other types of data; (3) analysis of Big Data and the implementation of different methods of processing unstructured data, the creation of various analytical reports. In essence, the application of Big Data implies all areas of work with large volumes of the most disparate data, constantly updated and scattered across various sources. The goal is quite simple – the most efficient work, the introduction of new products and increased competitiveness. In this article, we will consider the features of solving the problems of using Big Data in international business.

Diffusion Kurtosis Imaging for Detection of Early Brain Changes in Parkinson's Disease.

We aimed to evaluate microscale changes in the bilateral red nucleus and substantia nigra of patients with Parkinson's disease (PD) using diffusion kurtosis imaging (DKI). Twenty-six patients with PD [mean age, 62.5 ± 8.7 years; Hoehn-Yahr stage, 0–4.0; Unified Parkinson's Disease Rating Scale (UPDRS) scores, 8–43] and 15 healthy controls (mean age, 59.5 ± 9.4 years) underwent DKI of the substantia nigra and red nucleus. Imaging was performed using a General Electric (GE) Signa 3.0-T MRI system. Patients with PD were divided into two groups consisting of 12 patients with UPDRS scores ≥ 30 and 14 patients with UPDRS scores < 30. All DKI data processing operations were performed with commercial workstations (GE, ADW 4.6) using Functool software to generate color-coded and parametric maps of mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD). MK values in the bilateral substantia nigra were significantly lower in patients with early- and advanced-stage PD than in controls. Moreover, MK values in the left substantia nigra were significantly lower in patients with advanced-stage PD than in those with early-stage PD. Patients with advanced-stage PD also exhibited significant decreases in MK values in the bilateral red nucleus relative to controls. No significant differences in FA or MD values were observed between the PD and control groups. There were no significant correlations between MK, FA, or MD values and UPDRS scores. Our findings suggest that decreased MK values in the substantia nigra may aid in determining the severity of PD and help provide early diagnoses.

Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis

This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data acquisition process, as well as the technical architecture needed for data processing. Subsequently, a technique for operational machine data segmentation based on dynamic time warping and hierarchical clustering is introduced. The formerly mentioned data segmentation and analysis technique allows for machine tools anomaly detection thanks to comparison between near real-time machine operational information, coming from strategically positioned sensors and outcomes collected from previous production cycles. Anomaly detection techniques shown in this article could achieve significant production improvements: “zero-defect manufacturing”, boosting factory efficiency, production plans scrap minimization, improvement of product quality, and the enhancement of overall equipment productivity.

Regulatory Architecture of Data Processing for Connected and Automated Driving in Europe

The beginning of the 2020s ought to reflect a steady conclusion of the vast majority of the European Union’s projects with regards to the new era of connectivity and mobility within the European Union dimension. We expect Intelligent Connected Vehicles (ICVs) to step into free circulation within the internal market. Since the operation of the ICVs depends on the number of data processing operations, data processing operations should be precisely determined and framed beforehand. ICVs data operations consist of extraordinarily large volumes and velocity of a data flow which previously existed in traditional relational database systems and could not have been processed within the desired timeframe. Even though the currently adopted database systems are ready to face the new level of data processing, a huge data stream is also faced with complex obstacles and new risks which have never been experienced beforehand.While seeking to ensure safe and secure introduction of a new level of data processing for connectivity and automation at the European Union market, the author precisely examines all potential risks and possibilities of integration into a uniform legal regulation to ensure secured ICVs data processing at all levels. The regulatory framework should document adequate security requirements and defences against ICVs attacks e.g. interference and remote-control interception.

Positional Characteristics for Efficient Number Comparison over the Homomorphic Encryption

Modern algorithms for symmetric and asymmetric encryptions are not suitable to provide security of data that needs data processing. They cannot perform calculations over encrypted data without first decrypting it when risks are high. Residue Number System (RNS) as a homomorphic encryption allows ensuring the confidentiality of the stored information and performing calculations over encrypted data without preliminary decoding but with unacceptable time and resource consumption. An important operation for encrypted data processing is a number comparison. In RNS, it consists of two steps: the computation of the positional characteristic of the number in RNS representation and comparison of its positional characteristics in the positional number system. In this paper, we propose a new efficient method to compute the positional characteristic based on the approximate method. The approximate method as a tool to compare numbers does not require resource-consuming non-modular operations that are replaced by fast bit right shift operations and taking the least significant bits. We prove that in case when the dynamic range of RNS is an odd number, the size of the operands is reduced by the size of the module. If one of the RNS moduli is a power of two, then the size of the operands is less than the dynamic range. We simulate proposed method in the ISE Design Suite environment on the FPGA Xilinx Spartan-6 SP605 and show that it gains 31% in time and 37% in the area on average with respect to the known approximate method. It makes our method efficient for hardware implementation of cryptographic primitives constructed over a prime finite field.

VTEAM Model Based In-Memory Computation using Memristors

The recent applications in the computing field deals with huge data processing operations, which involves a lot of data transfer from the memory to processing unit and vice versa. If these data transfers between the CPU and memory are reduced, a lot of time could be saved along with advantage of getting desirable outputs at a lower latency. One of the ways to achieve this is to adopt In-memory computational techniques, where most of the computations happen inside memory itself. Memristive crossbar architecture is a memory architecture which makes use of memristors as memory cells to store data with some added computational capabilities. The need for power efficient computing in the present days motivates the search for new nonvolatile universal memories like memristors. In this paper, we present an optimal way of implementing basic ALU operations in the memristive memory block. A detailed description for simulating this is given, which includes the creation of memristor symbol, choosing a suitable memristive logic to implement basic digital logics, creation of memory crossbar and the way to realize basic ALU operations.