Data Movement Research Articles

РОЗРОБЛЕННЯ КЛІЄНТ-СЕРВЕРНОГО ЗАСТОСУНКУ З МЕТОЮ КЕРУВАННЯ БАЗОЮ ДАНИХ ДЛЯ ПОКРАЩЕННЯ НАВЧАЛЬНОГО ПРОЦЕСУ

This article presents the development of a number of administrative functions for a client-server application aimed at optimizing and simplifying database management. The administrator, having these functions, has the ability to perform various operations with the database, including archiving, unpacking, creating copies and restoring information. These capabilities allow you to ensure the reliability and security of data, reducing the risk of losing important information. In addition, with the introduction of functionality for loading data from Excel files, the process of data management becomes even simpler and more productive. This opens up new opportunities for fast and continuous updating and processing of information, making the process of working with data more efficient and convenient for the administrator.

Implementation of Data Warehouse and Star Schema for Optimizing Property Business Decision Making

The property industry is a sector that faces intense competition, so innovation is needed to plan business strategies. PT Wijaya Karya Realty faces several problems, such as determining strategic locations for its property products, such as houses or shophouses, and comparing the price of company houses with the market price of homes in specific locations. To answer these needs, it is necessary to implement a data warehouse and Extract, Transform, Load (ETL). In this research, Pentaho Data Integration (PDI) is used as a tool for managing and cleaning data that can be used as analysis material to assist in data-based decision-making. Apart from that, Mondrian can be used through the workbench scheme to improve data analysis effectively. Designing a star schema that includes a dimensional table and fact table can help optimize the analysis process better. The implementation of a data warehouse allows companies to make in-depth decisions based on critical variables. The research results show that the implementation of Star Schema can provide information for companies in decision-making and business strategy planning in the property sector.

Comparability between wearable inertial sensors and an electronic walkway for spatiotemporal and relative phase data in young children aged 6–11 years

BackgroundApproaches to gait analysis are evolving rapidly and now include a wide range of options: from e-patches to video platforms to wearable inertial measurement unit systems. Newer options for gait analysis are generally more inclusive for the assessment of children, more cost effective and easier to administer. However, there is limited data on the comparability of newer systems with more established traditional approaches in young children. Research questionTo determine comparability between the Physilog®5 wearable inertial sensor and GAITRite® electronic walkway for spatiotemporal (stride length, time and velocity, cadence) and relative phase (double support time, stance, swing, loading, foot flat and push off) data in young children. MethodsA total 34 typically developing participants (41% female) aged 6–11 years old median age 8.99 years old (interquartile range 2.83) were assessed walking at self-selected speed over the GAITRite® electronic walkway while concurrently wearing shoe-attached Physilog®5 IMU sensors. Level of agreement was analysed by Lin’s concordance correlation coefficient (CCC), Bland-Altman plots and 95% limit of agreement. Systematic bias was assessed using 95% confidence interval of the mean difference. ResultsExcellent to almost perfect agreement was observed between systems for spatiotemporal metrics: cadence (CCC=0.996), stride length (CCC=0.993), stride time (CCC=0.996), stride velocity (CCC=0.988). The relative phase metrics adjusted for stride velocity showed improved comparability when compared to the unadjusted metrics: swing adjusted (adj) (CCC=0.635); stance adj (CCC: 0.879); loading adj: (CCC=0.626). SignificanceSpatiotemporal metrics are highly compatible across GAITRite® electronic walkway and Physilog®5 IMU systems in young children. Relative phase metrics were somewhat compatible between systems when adjusted for stride velocity.

Exploring open government data ecosystems across data, information, and business

Although the Open Government Data (OGD) movement is flourishing globally, the large-scale utilization and value creation of OGD has not really opened yet, which is far from the goal and expectation of data openness. Guiding the practice of OGD with the concept of “ecosystem” can help promote the efficient utilization and value realization of data. From the perspective of the whole process of OGD utilization, ecosystems have various types of ecological characteristics such as data flow, information expression and value realization. To this end, this paper proposes a new perspective of viewing OGD ecosystems across data, information and business based on the theories of supernetworks, data value chains, information ecosystems and business ecosystems, and further explores the ecosystems through a case study in Guizhou, China. We conclude that the hierarchical division of OGD ecosystems as a whole is clearer, and the focus of each layer is more focused; the cross-level dynamics mechanism of the ecosystem, and the information transmission mechanism assumed by the information ecosystem make the three layers interact with each other; and the different actors play different roles in the three layers and occupy different ecological position. This study provides useful reference for OGD practice and also expands new ideas and space for theoretical research and application practice of the ecosystem approach.

A View on technology and controls for epidemic preparedness and the growth of COVID-19 disease in Saudi Arabia

This work emphasizes that the best chance of ending the pandemic lies in the Measures that can help us respond to these epidemic threats more effectively than before in the scope of protection from epidemic risks that we were not aware of before the outbreak of the disease, which is to benefit from the study of previous experiences. The work show the importance of technology about how to prepare for and handle any pandemic by discussing a successful case from the past. We examine Saudi Arabia's COVID-19 pandemic response experiences. We proposed a case evolution study from the southern region of Jazan to demonstrate the consequences of these techniques. Data on the daily Jazan COVID-19 infection curve collected from July 1st to September 30th, 2020, was processed using multiple Python software modules, such as encoder-decoder LSTM, 1D-CNN, and Complex network analysis of time series to identify changes within the infection curve's structure. A Gaussian modeling study was performed to compare the data movement with the protection from epidemic risks computed by stratification based on the data group.The network analysis shows that hubs are stable while medium and low-degree nodes are unstable. Also, from the perspective of Gaussian modeling, the COVID-19 infection curve parts indicate that the pandemic in Saudi Arabia is about to peak and move into the second (declining) half of the bell-shaped distribution.Technology in many areas and internet access make Saudi Arabia's social separation strategies a pandemic-eradication success story

D 2 Comp: Efficient Offload of LSM-tree Compaction with Data Processing Units on Disaggregated Storage

LSM-based key-value stores suffer from sub-optimal performance due to their slow and heavy background compactions. The compaction brings severe CPU and network overhead on high-speed disaggregated storage. This paper further reveals that data-intensive compression in compaction consumes a significant portion of CPU power. Moreover, the multi-threaded compactions cause substantial CPU contention and network traffic during high-load periods. Based on the above observations, we propose fine-grained dynamical compaction offloading by leveraging the modern Data Processing Unit (DPU) to alleviate the CPU and network overhead. To achieve this, we first customized a file system to enable efficient data access for DPU. We then leverage the Arm cores on the DPU to meet the burst CPU and network requirements to reduce resource contention and data movement. We further employ dedicated hardware-based accelerators on the DPU to speed up the compression in compactions. We integrate our DPU-offloaded compaction with RocksDB and evaluate it with NVIDIA’s latest Bluefield-2 DPU on a real system. The evaluation shows that the DPU is an effective solution to solve the CPU bottleneck and reduce data traffic of compaction. The results show that compaction performance is accelerated by 2.86 to 4.03 times, system write and read throughput is improved by up to 3.2 times and 1.4 times respectively, and host CPU contention and network traffic are effectively reduced compared to the fine-tuned CPU-only baseline.

A Minimal Metadata Schema and Its Tool to Improve the Searchableness of Research Data in Bioinformatics

Bioinformatics develops methods to understand biological data and to explain biological processes. The discipline operates on huge data sets and is computationally intensive. The fast growth of the field and its specialization into many subdisciplines makes it hard to search for, find, and to keep track of scientific results relevant for one’s own research. Being able to get knowledge of prior studies, and the data they rely on, is a prerequisite to further one’s own research and to ensure effective progress of bioinformatics as a field. At the same time, scientists’ own research and reputation benefit from the best possible searchableness of their research data. The FAIR data movement draws from this motivation. Before research data can be accessed and reused, however, it must first be found or discovered. For this purpose, the large space of highly diverse research data must be conquered, it must have a high quality of searchableness. To increase searchableness, we have devised a metadata schema to describe the entire field of bioinformatics by a small set of descriptors. Our metadata schema has been inspired by Dublin Core and aims at replicating its success in the domain of bioinformatics. The schema aims at complementing the many metadata schemes used by bioinformaticians in practice by extracting their common core, yielding to a schema that can be used across bioinformatics subdisciplines. Our minimal schema for bioinformatics metadata is complemented by a Web-based annotation tool where such metadata can be provided in an effective, time-saving, and concise manner.

Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics

Deep reinforcement learning (DRL) has emerged as a promising approach for handling highly dynamic and nonlinear active flow control (AFC) problems. However, the computational cost associated with training DRL models presents a significant performance bottleneck. To address this challenge and enable efficient scaling on high-performance computing architectures, this study focuses on optimizing DRL-based algorithms in parallel settings. We validate an existing state-of-the-art DRL framework used for AFC problems and discuss its efficiency bottlenecks. Subsequently, by deconstructing the overall framework and conducting extensive scalability benchmarks for individual components, we investigate various hybrid parallelization configurations and propose efficient parallelization strategies. Moreover, we refine input/output (I/O) operations in multi-environment DRL training to tackle critical overhead associated with data movement. Finally, we demonstrate the optimized framework for a typical AFC problem where near-linear scaling can be obtained for the overall framework. We achieve a significant boost in parallel efficiency from around 49% to approximately 78%, and the training process is accelerated by approximately 47 times using 60 central processing unit (CPU) cores. These findings are expected to provide valuable insight for further advancements in DRL-based AFC studies.

A 1024-Channel 268 nW/pixel 36×36 μm2/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.

This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm2) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.

S-MBR: An Efficient Scaling Scheme for PM-MBR-Coded Distributed Storage System

<p>With the rapid increase in data storage, distributed storage systems need to add new nodes to reduce storage and computation pressure. However, the existing scaling schemes are not efficient enough. To solve these issues, A scaling scheme called S-MBR is proposed, which has two notable features. Firstly, S-MBR achieves uniform data distribution by using a symmetric data layout to evenly place data blocks on the expanded data nodes. Secondly, S-MBR minimizes data movement during data redistribution and parity update processes by re-locating data blocks to symmetric positions and performing partial parity block calculations after the storage nodes are expanded. S-MBR can reach the lower bound of data migration volume when the number of nodes required for reconstruction is equal to the number of nodes required for repair. According to mathematical analysis and experimental results, compared to many typical scaling schemes, S-MBR can reduce data transmission volume by up to approximately 93% and average response time by around 41%.</p> <p> </p>

Markov Lifetime Prediction Model for Wireless Sensor Network

The Wireless Sensor Network (WSN) consists of sensor nodes that can communicate with each other via wireless transceivers to gather information from the environment. Sensor nodes are often placed in critical areas that are difficult to reach, sometimes in large numbers, and are unpredictable when the battery runs out on WSN. Thus, replacing the sensor node battery is difficult or even impossible. On the other hand, frequent battery replacement due to critical battery performance makes it uneconomical and unmeasurable. This paper proposes a lifetime battery prediction model for WSN. What is new in this study is that the battery lifetime is influenced by three factors, namely temperature, humidity, and data movement. Temperature and humidity significantly affect the battery lifetime, while data movement has little effect. Predictions of battery types one to four, with energy depletion (ED) consisting of Temperature, Humidity, and data movement. Each battery with these characteristics has a different lifetime; type one battery has a lifetime of 0.5 years, type two battery has a life of 0.16 years, type three has a life of 0.17 years, and type four has a lifetime of 0.4 years.

United States house dust Pb concentrations are influenced by soil, paint, and house age: insights from a national survey

BackgroundLead (Pb) in house dust contributes significantly to blood lead levels (BLLs) in children which may result in dire health consequences. Assessment of house dust Pb in the United States, relationships with Pb in soil and paint, and residential factors influencing Pb concentrations are essential to probing drivers of house dust Pb exposure.ObjectivePb concentrations in vacuum-collected house dust are characterized across 346 homes participating in the American Health Homes Survey II (AHHS II), a US survey (2018–2019) evaluating residential Pb hazards. Connections between house dust Pb and soil Pb, paint Pb, and other residential factors are evaluated, and dust Pb concentration data are compared to paired loading data to understand Pb hazard standard implications.ResultsMean and median vacuum dust Pb concentrations were 124 µg Pb g−1 and 34 µg Pb g−1, respectively. Vacuum-collected dust concentrations and dust wipe Pb loading rates were significantly correlated within homes (α < 0.001; r ≥ 0.4). At least one wipe sample exceeded current house dust Pb loading hazard standards (10 µg ft−2 or 100 µg Pb ft−2 for floors and windowsills, respectively) in 75 of 346 homes (22%). House dust Pb concentrations were correlated with soil Pb (r = 0.64) and Pb paint (r = 0.57). Soil Pb and paint Pb were also correlated (r = 0.6).ImpactThe AHHS II provides a window into the current state of Pb in and around residences. We evaluated the relationship between house dust Pb concentrations and two common residential Pb sources: soil and Pb-based paint. Here, we identify relationships between Pb concentrations from vacuum-collected dust and paired Pb wipe loading data, enabling dust Pb concentrations to be evaluated in the context of hazard standards. This relationship, along with direct ties to Pb in soil and interior/exterior paint, provides a comprehensive assessment of dust Pb for US homes, crucial for formulating effective strategies to mitigate Pb exposure risks in households.

MemFHE: End-to-end Computing with Fully Homomorphic Encryption in Memory

The increasing amount of data and the growing complexity of problems have resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance, or defense, demand security and privacy, which today’s solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today’s solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion—the ciphertext is much bigger than the native aggregate of native data types. In this article, we present MemFHE, which is the first accelerator of both client and server for the latest Ring-GSW (Gentry et al. [ 17 ])-based homomorphic encryption schemes using Processing in Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data while providing in situ execution and extensive parallelism needed for FHE’s polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE’s server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security levels based on the application requirements. We evaluate MemFHE for various security levels and compare it with state-of-the-art CPU implementations for Ring-GSW-based FHE. MemFHE is up to 20 k × (265×) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2,007× higher throughput than [ 36 ] while implementing neural networks with FHE.

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

Investigating techniques to optimize data movement and reduce memory-related bottlenecks

In the ever-changing realm of computing, the importance of efficient data movement and the reduction of memory-related bottlenecks cannot be overstated. This research paper delves into a thorough examination of diverse methodologies and approaches aimed at optimizing data transfer and mitigating the constraints imposed by memory limitations. It offers an all-encompassing survey of pertinent literature, delving deep into techniques designed to enhance data movement efficiency, discussing effective strategies for alleviating memory bottlenecks, and presenting the outcomes of extensive experiments conducted. The findings of this study underscore the critical role played by these techniques in augmenting the performance, efficiency, and scalability of contemporary computing systems. In a world where the demand for computational power continues to grow, the ability to streamline data movement and overcome memory constraints is essential. By shedding light on these pivotal aspects of computing, this paper contributes to a more profound understanding of how to harness the full potential of modern computing systems, ultimately paving the way for groundbreaking advancements in the field.

The different effects of polished and post-slip roughnesses on fault stability

Linking fault morphology to its mechanical properties in laboratory experiments may shed some light on analyzing the seismogenic potential of a natural fault. Many previous experiments suggested that increasing the roughness of a polished fault tends to stabilize the fault. Furthermore, the roughness of faults, both experimental and natural, spontaneously evolves during slip. Here, we investigate the effects of the polished roughness and the post-slip roughness on experimental fault stability. The experiments were conducted on a granodiorite fault under initial normal stresses of 5–6 MPa and total slip distances of 1.5–2.5 mm. The results reveal that the polished fault with a higher roughness exhibits stick-slip, while the post-slip fault with a lower roughness exhibits stable slip. Moreover, a decrease in the roughness of a post-slip fault tends to stabilize the fault. The monitored macroscopic loading data indicate that the critical slip distance is significantly greater in a post-slip fault than in a polished fault. These results imply that the critical slip distance of young, rough faults may evolve dramatically with slip, subsequently affecting the fault stability.

Dataflow optimization with layer-wise design variables estimation method for enflame CNN accelerators

As convolution layers have been proved to be the most time-consuming operation in convolutional neural network (CNN) algorithms, many efficient CNN accelerators have been designed to boost the performance of convolution operations. Previous works on CNN acceleration usually use fixed design variables for diverse convolutional layers, which would lead to inefficient data movements and low utilization of computing resource. We tackle this issue by proposing a flexible dataflow optimization method with design variables estimation for different layers. The optimization method first narrows the design space by the priori constraints, and then enumerates all legal solutions to select the optimal design variables. We demonstrate the effectiveness of the proposed optimization method by implementing representative CNN models (VGG-16, ResNet-18 and MobileNet V1) on Enflame Technology's programmable CNN accelerator, General Computing Unit (GCU). The results indicate that our optimization can significantly enhance the throughput of the convolution layers in ResNet, VGG and MobileNet on GCU, with improvement of up to 1.84×. Furthermore, it achieves up to 2.08× of GCU utilization specifically for the convolution layers of ResNet on GCU.

Plugins and POPI: A Critical Discussion into the Legal Implications of Social Plugins and the Protection of Personal Information

Social plugins are one of the many trackers used by companies with an online presence. However, under the Protection of Personal Information Act 4 of 2013 (POPI), these trackers have certain legal consequences for internet users. The main reason for this is that trackers tend to process personal information without informing internet users that their data are being collected, the reason for the collection or processing thereof, or who the responsible parties are that are collecting and processing the personal information. The article looks at these issues, amongst others, in the light of a 2019 judgment from the Court of Justice of the European Union or CJEU, namely, Case C-40/17 Fashion ID GmbH &amp; Co. KG v Verbraucherzentrale NRW e.V. EU:C:2019:629. Due to the fact that it has had data protection legislation for much longer than other countries or legal jurisdictions, including South Africa, the European Union (the EU) has a substantial body of case law interpreting the data protection legislation of the EU itself as well as that of the individual member states. One of the main instruments used as guidance by the drafters of POPI was Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data (hereafter Directive 95/46). Directive 95/46 was previously considered the gold standard, before Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data (General Data Protection Regulation) (hereafter the GDPR) was enacted and Directive 95/46/EC was finally repealed. Since Directive 95/46 was one of the main guiding documents used in drafting POPI, one may expect that the South African courts may turn to the EU and consider how the CJEU has interpreted the similar provisions contained in Directive 95/46, especially since there is very little South African jurisprudence available on POPI. The four main issues under discussion are: who, other than the internet users, has the locus standi to bring an application in terms of POPI? Second, what are the responsibilities of joint responsible parties towards internet users? Third, where there are joint responsible parties, do both need a legitimate interest to process personal information? Lastly, who will be responsible for obtaining the necessary consent to process the personal data?

Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries

In this article, an innovative approach is presented to the sizing and technical–economic analysis of battery energy‐storage systems (BESS) designed for customers in the free energy market in Brazil. The tool enables the integration of photovoltaic (PV) energy sources and includes a comparison between the BESS + PV system and diesel generators. Integrating the computational capabilities of Microsoft Excel in the backend and the intuitive interface of PowerApps in the front end, the sizing process is based on analyzing historical energy invoices spanning at least 12 months or loading data with a 1 h measurement interval. The data discretization over the 8760 h of the year, considering the consumer's load profile, is facilitated by the PowerApps interface, providing a comprehensive visualization of the technical‐economic sizing results for BESS. A case study is conducted for a commercial load with a specific tariff for free energy market customers, revealing viable solutions for BESS compared to diesel alternatives. In this approach, it is aimed to simplify the analysis and decision‐making process, offering a valuable tool for power system engineers to evaluate sustainable and economically viable solutions in the Brazilian free energy market.

Enhancing handicraft exports in West Java: A business intelligence approach to market expansion

The creative industries in West Java have significantly boosted the region's economy, contributing to higher GDP, more jobs, and increased exports. However, the handicraft sector seeks to grow its presence in the international market, where it currently holds a minor share. To address the challenges of expanding, such as limited information, marketing obstacles, and regulatory hurdles, the handicraft industry is encouraged to adopt a business intelligence (BI) platform. This study aims to use a BI platform to present and analyze export data for West Java's craft industry, examining its distribution, trends, and future prospects to help increase exports from this Indonesian province. The analysis employs clustering with k-means, time series analysis, and forecasting methods, including exponential smoothing and the compound annual growth rate (CAGR), using export data from 2018 to 2022. The process involves collecting primary and secondary data, transforming it through ETL (Extract, Transform, Load) technology, and integrating it into the BI platform for analysis. This analysis aims to identify export patterns, trends, and make forecasts that can guide decision-making. The findings indicate that handicraft exports are categorized into three destination country clusters, each favoring different product types, revealing trends and growth opportunities for various handicraft items. Additionally, the study provides forecasts for handicraft exports, offering valuable insights for strategic planning.