Latest results and computing performance of the ePLACE data preparation tool

The requirements for advanced lithography solutions are constantly increasing. Up to now the optical lithography with its extension into the immersion lithography and strong resolution enhancement techniques (RET) represents the lithography solution of choice. With decreasing critical dimensions (CD) new approaches are needed to provide cost effective methods for small and medium volume production on one side and for efficient photomask fabrication including the RET on the other side. A viable solution for both application fields is the Multi Shaped Beam (MSB) technology, which is currently being developed at Vistec Electron Beam in Jena. E-beam technology has already been used for R&D as well as prototyping applications well ahead of the respective production technology node. It is as well the technology of choice for the future mask fabrication. However, the decreasing CD and thus increasing pattern density asks for new ideas to overcome the throughput challenges faced today. This bottleneck is addressed by parallel writing methods where MSB is one of these techniques. In correlation with the MSB writing technology an appropriate data preparation path is mandatory. One major criterion for the performance of MSB data processing is the shot count reduction which can be reached for different types of patterns. In this paper results from an analysis of the current status of MSB shot segmentation are reported. Beside collecting statistical data of the individual structure size distribution an analysis of the shot count on different pattern coming from mask write as well as direct write applications was carried out. This was performed for different technology nodes and the results are compared.

The subject of the paper is methods of classification of data mining and data processing tasks in the economy, as well as their classification characteristics. Methodology. The research used the general scientific methodology of analysis, synthesis, generalization, comparison. Taxonomy methods are used in the compilation of the classification. The selection of actual economic problems is carried out by researching scientific publications on analysis and data processing. The purpose of the article is to compile an actual classification of data mining and data processing tasks in the economy and to refine the terminology of this branch of science. Results. The classification of data mining tasks is proposed, consisting of four levels. All economic tasks of data mining are divided into two large groups: predictive and descriptive. Each group is subdivided into several classes, which combine tasks with similar taxonomic features. These are the classes of tasks classification, regression, clustering, link analysis, and outlier analysis. Classes of tasks are divided into types. An important criterion for this is the dimensionality of the input data representation. It means the number of neighbours for each individual data element. The data can be presented in the form of series, matrices, and graphs. The methods of analysing each form of data presentation vary considerably. The definition of data processing is clarified and a classification of relevant tasks is proposed. At the top level of classification, the data processing tasks are divided into two groups, depending on whether the order of the elements in the input data changes or not. The main classes of data processing tasks are identified, such as ranking, sorting, filtering, cleansing, recovery, and quantization. Practical use. The results of the research can be used to model intellectual decision-support systems. They allow improving the processes of problem formulation in data mining and data processing tasks. Also, it helps in the formalization of the procedures for selecting methods of their solutions. This leads to an increase in economic efficiency.

Exploration seismology uses reflected and refracted seismic waves, emitted from a controlled (active) source into the ground, and recorded by an array of seismic sensors (receivers) to image the subsurface geologic structures. These seismic images are the main resources for energy and resource exploration and scientific investigation of the crust and upper mantle. We survey recent advances in applications of machine-learning methods, more specifically deep neural networks (DNNs), in exploration seismology. We provide a technically oriented review of DNN applications for seismic data acquisition; data preprocessing tasks such as interpolation/extrapolation, denoising, first-break picking, velocity picking, and seismic migration; data processing tasks such as geologic and structural interpretations; and data modeling tasks such as the inference of subsurface structures and lithologic and petrophysical properties. DNNs have entered almost every sector of exploration seismology. They have outperformed many traditional algorithms for the automation of seismic data acquisition, data preprocessing, data processing, interpretations, and data modeling tasks. However, despite the impressive performances of DNN-based approaches, the out-of-distribution generalization and interpretability of these models remain challenging. To overcome these challenges, incorporating domain knowledge into the DNNs is a promising path and a focus of current deep-learning research in seismology.

In the era of digital technologies, working with large volumes of data is undergoing significant changes due to new technologies. This article presents a theoretical and methodological description and analysis of the integration of technologies such as artificial intelligence, machine learning, cloud computing, and blockchain to address complex tasks in data processing and information management. The subject of the study is the development of an efficient data storage system to solve applied tasks in various scientific fields. Cloud computing provides seamless access to digital resources, supports remote services, and reduces dependence on physical infrastructure by hosting databases and management systems on virtual platforms. This fosters scalability and distribution of computational power for processing vast amounts of information and deploying complex artificial intelligence models. Artificial intelligence, particularly automated machine learning (AutoML) and generative models, automates labor-intensive stages of data preparation, significantly enhancing the efficiency and accuracy of models. Blockchain technology, in turn, addresses critical issues of security, data integrity, and digital rights management, creating a trusted and immutable environment for decentralized data processing systems. The joint application of these technologies lays the foundation for the creation of self-optimizing, scalable, and secure automated data processing systems, as demonstrated by examples in library science, healthcare, and linguistics. For instance, blockchain technology ensures the transparency, reliability, and security of library records. It plays an important role in managing intellectual property, digital rights management (DRM), and authenticating rare or archival documents. In linguistics, we note the ability of data structures to handle linguistic ambiguity and demonstrate complex relationships between language elements, using logical input mechanisms and principles of synergetics. Further work is expected to develop more advanced automation technologies for rapid deployment of trained models with fewer data and the potential for use in various fields.

Data preparation is an iterative-agile process for exploring, combining, cleaning and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and BI/analytics. To perform data preparation, data preparation tools are used by analysts, citizen data scientists and data scientists for self- service. The tools are also used by citizen integrators and data engineers for data enablement to reduce the time and complexity of interactively accessing, cataloging, harmonizing, transforming and modeling data for analytics in an agile manner with metadata and lineage support. These tools can provide data access for use in mostly analytical tasks that include storage, logical and physical data modeling, and data manipulation for data visualization, data integration and analytics. Some tools support machine-learning algorithms that can recommend or even automate actions to augment and accelerate data preparation. Keywords: Data Mining, Data preparation, Data Analysis, Big Data Analytics

In this chapter we begin with a detailed explanation about data gathering and preparation process such as data acquisition and data collection (Sects. 4.2 and 4.3). Then a temporal coverage of land use change trend in the study area will be presented (Sect. 4.4). It will be followed by temporal change mapping and change visualisation within the presumed time period to measure urban sprawl in that period (Sects. 4.5–4.7). Furthermore, the demographic changes over this time will be taken into account (Sect. 4.8).

Training deep neural networks may be challenging in real world data. Using models as black-boxes, even with transfer learning, can result in poor generalization or inconclusive results when it comes to small datasets or specific applications. This tutorial covers the basic steps as well as more recent options to improve models, in particular, but not restricted to, supervised learning. It can be particularly useful in datasets that are not as well-prepared as those in challenges, and also under scarce annotation and/or small data. We describe basic procedures as data preparation, optimization and transfer learning, but also recent architectural choices such as use of transformer modules, alternative convolutional layers, activation functions, wide/depth, as well as training procedures including curriculum, contrastive and self-supervised learning.

The production system for Grid Data Processing handles petascale ATLAS data reprocessing and Monte Carlo activities. The production system empowered further data processing steps on the Grid performed by dozens of ATLAS physics groups with coordinated access to computing resources worldwide, including additional resources sponsored by regional facilities. The system provides knowledge management of configuration parameters for massive data processing tasks, reproducibility of results, scalable database access, orchestrated workflow and performance monitoring, dynamic workload sharing, automated fault tolerance and petascale data integrity control. The system evolves to accommodate a growing number of users and new requirements from our contacts in ATLAS main areas: Trigger, Physics, Data Preparation and Software & Computing. To assure scalability, the next generation production system architecture development is in progress. We report on scaling up the production system for a growing number of users providing data for physics analysis and other ATLAS main activities.

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 the fourth industrial revolution, various inter-mediation such as theories, techniques, or implementation would be used. It contains computational intelligence, applied soft computing and fuzzy logic and artificial neural networks, intelligent contents security, model driven architecture and meta-modeling, multimedia contents processing and retrieval, vehicular N/W, big data, intelligence information processing, convergence/complex contents, smart learning, intelligent contents design management, methodology and design theory, intelligent media contents convergence/complex media, social media and collective intelligence, and social media big data analytic. To consider these interesting and significant issues which have related to the fast developing area of smart media and application, the editors have edited this special issue Smart Media and Application, and finally, it selected eleven papers. In Motion data acquisition method for motion analysis in golf by Hwang et al,1 it studied the process a system that obtains the same motion as the actual one by acquiring information on the motion of the subject using 15 inertial sensors, and using the information on the actual joint length and initial direction of the subject extracted from a depth camera. As a result, it shows that measurement error of information on the joint length and the foot stance of subjects extracted through the depth camera ranges from a minimum of 4.4% to a maximum of 6.94%. In NSCT domain–based secure multiple watermarking technique through lightweight encryption for medical images, which is written by Thakur et al,2 it defined non-subsampled contourlet transform (NSCT) domain, in which the proposed scheme first partitions the host image into sub-components and then calculates the entropy values. In Environmental monitoring system for intelligent stations by Li et al,3 it proposed the design of the high-speed rail station environmental monitoring system based on the LoRa communication technology, and it aims to enable the real-time monitoring of temperature, humidity, illuminance, and noise decibels in high-speed railway stations. Efficient dummy generation for considering obstacles and protecting user location is by Song et al.4 This paper was to study an efficient dummy creating techniques to improve user privacy protection, and as a result, it showed that it improved on other recent techniques. Key node selection based on a genetic algorithm for fast patching in social networks by Kim et al5 provided considerable amounts of personal information and shares this information with friends without space-time limitations. It tried to improve the patch propagation speed; it was important to select key nodes that are the starting points of the patch process. In this paper, the authors proposed a key node selection scheme based on a genetic algorithm to find the most significant contribution nodes of the patch propagation. Following the simulation result, it showed the proposed scheme propagates patches more rapidly than the existing one. In Effective computer-assisted pronunciation training based on phone-sensitive word recommendation by Jo et al,6 they proposed a computer-assisted pronunciation training system targeting unacceptable pronunciation due to confusion among contextual allophones, a problem that often emerges from phoneme-based feedback provided during pronunciation training. Finally, it showed that the proposed system results in the improvement of pronunciation skills through training sessions that use recommended words containing phoneme pairs that were initially pronounced incorrectly. In Dataset retrieval system based on automation of data preparation with dataset description model by Mun et al,7 it tried to overcome the problem by proposing a dataset description model that can express the requirements for data processing and dataset retrieval system based on automated data preparation and it could possibly provide good quality datasets for statistical learning applications using data preparation methods such as data acquisition, refinement, and organization. In An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia data analysis by Wang et al,8 it used 3DCNN in CNN part and ConvLSTM in RNN part. In A web-based group decision support system for multicriteria problems written by Conceição et al,9 it used a multiagent system to combine and process this information, using virtual agents that represent each decision-marker. The high level of usability that the system provides will contribute to an easier acceptance and adoption of this kind of systems. In Intelligent and semantic threshold schemes for security in cloud computing by Ogiela and Snasel,10 it tried to enhanced with new classes of solutions, including semantic protocols of secret sharing, and it is subjected to the process of splitting and hiding that could take various forms including that of services. In Estimation of a physical activity energy expenditure with a patch-type sensor module using artificial neural network by Kang et al,11 it proposed the most accurate method using a wireless patch-type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, it achieved RMSE of 0.1893, R2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. We would like to thank you our sincere appreciation of the valuable contributions to all authors.

PurposeThe role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.Design/methodology/approachThe methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.FindingsThe proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.Research limitations/implicationsThere is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.Originality/valueWith the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.

A relatively new set of transport-based transforms (CDT, R-CDT, LOT) have shown their strength and great potential in various image and data processing tasks such as parametric signal estimation, classification, cancer detection among many others. It is hence worthwhile to elucidate some of the mathematical properties that explain the successes of these transforms when they are used as tools in data analysis, signal processing or data classification. In particular, we give conditions under which classes of signals that are created by algebraic generative models are transformed into convex sets by the transport transforms. Such convexification of the classes simplify the classification and other data analysis and processing problems when viewed in the transform domain. More specifically, we study the extent and limitation of the convexification ability of these transforms under an algebraic generative modeling framework. We hope that this paper will serve as an introduction to these transforms and will encourage mathematicians and other researchers to further explore the theoretical underpinnings and algorithmic tools that will help understand the successes of these transforms and lay the groundwork for further successful applications.

Multidimensional on-line analytical processing (MOLAP) systems deal well with dense data than relational ones (ROLAP). In the existence of sparse data, MOLAP systems become memory consuming, which may limit and slow down data processing tasks. Many compression techniques have been proposed to deal with the sparsity of data in MOLAP systems. One of these techniques is the bitmap compression, which allows a significant reduction of the memory space used for data processing. In this article, we propose an extension to the bitmap compression technique by storing the compressed data as bits into multiple efficient data structures based on a new indexing strategy instead of the linear structure. Compared with the classical bitmap, the proposed enhancement not only allows space reduction but also reduces the search time through the compressed data. We present some algorithms that allow maintaining and searching within the compressed structure without the need for decompression. We demonstrate that the complexity of the proposed algorithms varies from logarithmic to constant, compared with the linear complexity of the classical bitmap technique.

MapReduce has become a popular method for data processing, in particular for large scale datasets, due to its accessibility as a scalable yet convenient programming paradigm. Data processing tasks often involve joins, and the repartition and fragment-replicate joins are two widely-used join algorithms utilised within the MapReduce framework. This paper presents a multi-join supporting tuple redistribution, building on both the repartition and fragment-replicate joins. Hadoop is used to demonstrate how reduce tasks may improve performance by passing intermediate results to other reduce tasks that are better able to process them using Apache ZooKeeper as a means of communication and data transfer. A performance analysis is presented showing the technique has the potential to reduce response times when processing multiple joins in single MapReduce jobs.

ActiveSort: Efficient external sorting using active SSDs in the MapReduce framework