Data Storage Processing Research Articles

Sistem Informasi Pengelolaan Data Marketing STMIK Bandung

In an era of rapid development of digitalization and information technology, digital transformation dominates almost all aspects of life. The world of education, including universities, cannot be separated from the positive impact of information technology. One area that continues to develop along with the digital transformation of the education industry is the STMIK Bandung Marketing Data Management Information System. UML (Unified Modeling Language). From the various explanations in this report, the following conclusions can be drawn: a) An information system for managing STMIK-Bandung marketing data has been created which facilitates the process of storing STMIK-Bandung marketing data. b) The STMIK Bandung Marketing Data Management Information System can replace the previous system which still uses a third party provider, namely Google Drive. Get better control over your marketing data to ensure data security and integrity. c) We have developed a system that makes the marketing data storage process more effective and efficient.

First principle atomistic modelling of magnetic tunnel junction (MTJ) to enhance its tunnel magneto-resistance (TMR) ratio

Abstract This paper investigates the impact of electrode materials on the Tunnel Magneto-Resistance (TMR) ratio of Magnetic Tunnel Junction (MTJ) device. Four different structures of MTJ have been simulated by using cobalt (Co), Nickel (Ni), Iron (Fe) and two alloy materials of nickel-iron (NiFe) and cobalt-iron (CoFe). These materials have been used as ferromagnetic electrodes. Mulliken population and transmission spectrum observed in both parallel and antiparallel configurations of these devices to understand the spin transport properties and Tunnel Magneto-Resistance (TMR) ratio has been estimated. The first principal study was performed based on density function theory (DFT) and Non-equilibrium Green’s Function (NEGF) computational methods using the QuantumATK simulation tool to study properties such as band structure, the density of states (DOS), Spin Transfer Torque (STT), I-V characteristics and TMR. This study explores how different electrode materials affect the Tunnel Magneto-Resistance (TMR) ratio in Magnetic Tunnel Junction (MTJ) devices. With these results, it is observed that cobalt-based MTJ devices (that is Co-MgO-Fe and CoFe-MgO-CoFe) exhibit higher TMR ratio as compared to Nickel- and Iron-based MTJ devices (that is NiFe-MgO-NiFe and Ni-MgO-Fe). As Cobalt has a high spin polarization this property makes it suitable for use in spintronics devices like MTJs, where the manipulation of electron spins is essential for data storage and information processing. These findings can be employed to improve the performance characteristics of the MTJ-based Random Access Memory (MRAM) in the field of spintronics.

Non-volatile Fermi level tuning for the control of spin-charge conversion at room temperature

Current silicon-based CMOS devices face physical limitations in downscaling size and power loss, restricting their capability to meet the demands for data storage and information processing of emerging technologies. One possible alternative is to encode the information in a non-volatile magnetic state and manipulate this spin state electronically, as in spintronics. However, current spintronic devices rely on the current-driven control of magnetization, which involves Joule heating and power dissipation. This limitation has motivated intense research into the voltage-driven manipulation of spin signals to achieve energy-efficient device operation. Here, we show non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. We use a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion we perform ferromagnetic resonance and inverse Edelstein effect experiments. The sign change of output voltage is derived by the change of carrier type, which can be achieved solely by a voltage pulse. Our results provide an alternative approach for the electric-field control of spin-charge conversion, which constitutes a building block for the next generation of spin-orbitronic memory and logic devices.

FaceDefend: Copyright Protection to Prevent Face Embezzle

With the rapid evolution of deep learning and the advent of artificial intelligence, the metaverse has emerged as a significant technology. Within the metaverse, diverse elements such as rich applications and realistic digital avatars provide users with immersive experiences, but it poses a series of security problems. Current research predominantly focuses on the data storage and transmission processes from the perspective of blockchain and the Internet of Things to achieve the protection of the metaverse. However, there exists a gap in security research on the digital avatar generation process. Given that digital avatars are the primary entities engaging in social activities within the metaverse and are crafted based on real face images, the virtual character can be generated easily by stealing the user’s face image and controlled to interact with others. In order to deal with the above problems, we propose a novel method to prevent the misuse of faces, which maintains the security of the metaverse by protecting facial data and thus preventing its misuse. We explore the common architecture of generative models and propose a defense method based on copyright protection to prevent face embezzling. Firstly, we utilize the copyright protection module to obtain copyright protection information. Secondly, we utilized the defense control module to ensure the representation of the protected images occurs errors in the latent space of the generation model. Therefore the subsequent generation task output fails, which effectively protects the face data and prevents the generation of digital avatars. Furthermore, the results on public datasets and across multiple generative models present unnatural outputs, indicating the excellence of our defense and transfer capabilities.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

A preliminary discussion on data privacy and data security issues

With the expeditious evolution of technologies such as the internet, big data, cloud computing, and artificial intelligence, data has ascended to the status of an invaluable and indispensable resource within the intricate tapestry of modern society. Yet, the labyrinthine ethical quandaries that intertwine with the processes of data collection, storage, processing, and application have progressively surfaced, capturing the rapt attention of academia, industry, and government as they grapple with this nascent challenge. However, with the extensive application of data technology, issues of data ethics have become increasingly prominent, posing unprecedented challenges to personal privacy, data security, social equity, and justice. This article aims to delve deeply into the connotation and extension of data privacy and data security, as well as its significant role in current society. By examining relevant research findings at this stage, analyzing the major challenges faced by data ethics and exploring corresponding strategies, this paper delves into the multifaceted challenges faced by data privacy and data security, highlights the inadvertent data collection by smart devices, the potential for re-identification of blurred and anonymized information through big data analysis, as well as the ongoing threats posed by hacker attacks, internal vulnerabilities, and the complexity of data circulation.

Health information systems data for decision-making: case study in three cities on current practices and opportunities

A functional and reliable Health Information System (HIS) is vital for data-based decision-making in public health. Here we describe the assessment of data processes and general HIS principles by adapting a global approach to three cities. The assessments supported the data strategy of the CARDIO4cities initiative in each city aiming to improve urban population health by increasing the use of cardiovascular disease (CVD) data to inform decision-making. We aimed to explore data collection processes and entities, data availability and quality as well as data ownership and sharing regarding a set of identified key performance indicators (KPIs). KPIs were based on a global theory of change (ToC) and a global evaluation and indicator framework and were tailored to each location. By first assessing existing sources and processes regarding data, recommendations for changes and improvements are sure to build on current circumstances. To map the data, existing data collection, analysis and storage processes were investigated. A flow chart was created to visualize the data pathways and challenges for each system and findings were compared across cities to document differences and similarities. Data quality and interoperability of various separate systems were the most prominent challenges for all HISs. The observed dvata quality issues originated from incorrect, missing and incomplete data and were connected to the misunderstanding of indicators, incomplete data input forms or the lack of a systematic data routine in the workflow. Harmonization of the HISs to ensure interoperability can facilitate data collection and analysis of health data and can provide a solid basis for health management decision-making. Based on the presented HIS cases, we recommend to examine, map and verify current processes when conducting a HIS assessment, to visualize findings and to gauge the interest of government entities to ensure political support.

Data acquisition system for a newly-built hard x-ray/soft gamma-ray spectrometer imaging system on experimental advanced superconducting tokamak

A portable hard X-ray and soft gamma-ray spectrometer imaging system (HXS) has been constructed to gather physical information about fast electrons confined in the Experimental Advanced Superconducting Tokamak (EAST). The system is installed on the low field side of the mid-plane and provides a viewing field tangential to the toroidal field. The system utilizes a two-dimensional Cadmium Zinc Telluride (CdZnTe) semiconductor detector with 128 channels, and a data acquisition (DAQ) system has been designed for it. The DAQ system features a highly integrated signal processing system with the capability of high-speed processing and digital transmission of signals from 128 channels. In addition, a related DAQ software has been developed using a modular design approach, facilitating tasks such as data reception, storage, and preliminary processing. HXS, which has been applied during the recent EAST campaign, directly obtains the digital energy spectrum of incident photons. The DAQ system is described in detail in this paper. The hardware components and energy calibration have also been described. Experimental data have been successfully obtained and briefly discussed. More physical research will be reported in future publications.

Intelligent privacy‐preserving data management framework for medicine supply chain system

AbstractIn today's era, the pharmaceutical industry has integrated blockchain to secure the sensitive information of medicines, wherein public and private blockchains are used to preserve the security and privacy of the medicine supply chain data. However, conventional blockchains often limit scalability due to limited storage. Moreover, blockchain has loopholes; for example, it is not able to prove the validity of the data prior to being stored in the blockchain, which leads to fake data being added to the blockchain. As a result, it causes an issue of data provenance. Motivated by this, the proposed framework incorporated artificial intelligence (AI) algorithms to enhance the efficiency of the medicine supply chain data. The proposed framework integrated machine learning (ML) and blockchain, where ML classifies the valid and invalid data of the medicine supply chain, whereas blockchain stores only valid data and maintains its security and privacy. This identification helps the blockchain to verify medicine supply chain data before adding it to the blockchain. Additionally, we employed an InterPlanetary file system (IPFS) that saves medicine supply chain data and computes its hash to offer decentralized storage. Further, this hash data is stored on a private Hyperledger Fabric blockchain, which requires minimal storage instead of storing an entire large file. This minimal storage optimizes the process of data storage and retrieval in the Hyperledger Fabric blockchain, which enhances the scalability of the proposed framework. Finally, the result of the proposed framework is assessed in two phases: ML and blockchain, wherein the ML model's performance is measured by statistical measures and the blockchain‐based result is assessed using several performance parameters such as throughput is around (618 transactions per second), latency (0.12 s), response time (11 s) and data rate (282 Mbps).

Recent progress in DNA data storage based on high-throughput DNA synthesis.

DNA data storage has emerged as a solution for storing massive volumes of data by utilizing nucleic acids as a digital information medium. DNA offers exceptionally high storage density, long durability, and low maintenance costs compared to conventional storage media such as flash memory and hard disk drives. DNA data storage consists of the following steps: encoding, DNA synthesis (i.e., writing), preservation, retrieval, DNA sequencing (i.e., reading), and decoding. Out of these steps, DNA synthesis presents a bottleneck due to imperfect coupling efficiency, low throughput, and excessive use of organic solvents. Overcoming these challenges is essential to establish DNA as a viable data storage medium. In this review, we provide the overall process of DNA data storage, presenting the recent progress of each step. Next, we examine a detailed overview of DNA synthesis methods with an emphasis on their limitations. Lastly, we discuss the efforts to overcome the constraints of each method and their prospects.

Improved optical contrast of plasmonic phase change memory using a Fabry-Perot cavity

As a promising technology to realize multilevel, non-volatile data storage and information processing, optical phase change technologies have attracted extensive attention in recent years. However, existing phase-change photonic devices face significant challenges such as limited switching contrast and high switching energy. This study introduces an innovative approach to tackle these issues by leveraging Fabry-Perot (F-P) cavity resonance and plasmon resonance techniques to enhance the modulation effect of phase change materials (PCMs) on the light. To the best of our knowledge, a novel device structure is proposed, featuring an elliptic nano-antenna placed on an F-P cavity waveguide composed of symmetric Bragg grating. This design exploits the enhanced electric field to achieve low power consumption and high contrast. The device enables crucial functions, including read, write, and erase operations, under all light conditions. Through the synergistic utilization of plasma and F-P cavity effects, an ultra-high switching contrast of around 70.6% is achieved. By varying the pulse power or duration, the proportion between the crystalline and amorphous states of the PCMs is altered, consequently modifying its refractive index. With its wide range of applications in optical storage and computing, the device holds significant potential for advancing these fields.

Enhancing Biometric Attendance Systems for Educational Institutions

his research paper aims to contribute to the field of biometric attendance systems by conducting a comprehensive analysis of fingerprint-based attendance systems described in the provided abstracts. The focus will be on systems designed for educational institutions with an emphasis on time efficiency, user satisfaction, and accuracy in attendance tracking. The study will compare and contrast the methodologies, technologies, and implementation details presented in the abstracts, identifying strengths and weaknesses of each system. Special attention will be given to the use of Internet of Things (IoT) technology, biometric design processes, and the integration of fingerprint sensors with microcontrollers like ESP32.Furthermore, the research will explore optimization strategies to enhance the overall performance of these systems. This includes investigating the feasibility of incorporating advanced fingerprint identification algorithms, improving user interface design, and streamlining the data collection and storage processes. The experimental aspect of the research will involve implementing the proposed optimization strategies on a prototype system and evaluating its performance against the existing systems. Metrics such as fingerprint identification accuracy, average matching time, and system efficiency will be measured and compared to demonstrate the effectiveness of the proposed enhancements.

An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring.

This study aimed to assess the performance, in terms of precision and accuracy, of a prototype (called "P.ALP"-Ph.D. Air Quality Low-cost Project) developed for monitoring PM2.5 concentration levels. Four prototypes were co-located with reference instrumentation in four different microenvironments simulating real-world and working conditions, namely (i) office, (ii) home, (iii) outdoor, and (iv) occupational environments. The devices were evaluated for a total of 20 monitoring days (approximately 168 h) under a wide range of PM2.5 concentrations. The performances of the prototypes (based on the light-scattering working principle) were tested through different statistical methods. After the data acquisition and data cleaning processes, a linear regression analysis was performed to assess the precision (by comparing all possible pairs of devices) and the accuracy (by comparing the prototypes against the reference instrumentation) of the P.ALP. Moreover, the United States Environmental Protection Agency (US EPA) criteria were applied to assess the possible usage of this instrumentation, and to evaluate the eventual error trends of the P.ALP in the data storage process, Bland-Altman plots were also adopted. The outcomes of this study underlined that the P.ALP performed differently depending on the microenvironment in which it was tested and, consequently, on the PM2.5 concentrations. The device can monitor PM2.5 variations with acceptable results, but the performance cannot be considered satisfactory at extremely low and remarkably high PM2.5 concentrations. Thanks to modular components and open-source software, the tested device has the potential to be customized and adapted to better fit specific study design needs, but it must be implemented with ad hoc calibration factors depending on the application before being used in field.

Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research

Abstract Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations. Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 920.

Analysis of Knowledge Management Strategies for Handling Cyber Attacks with the Computer Security Incident Response Team (CSIRT) in the Indonesian Aviation Sector

Cyber attacks are one of the genuine threats that have emerged due to the evolution of a more dynamic and complex global strategic environment. In Indonesia, several cyber attacks target various government infrastructure sectors. The National Cyber and Crypto Agency (BSSN) predicts Indonesia will face approximately 370.02 million cyber attacks in 2022. The majority of cyber attacks target the government administration sector. The National Cyber and Crypto Agency (BSSN) officially formed a Computer Security Incident Response Team (CSIRT) to tackle the rampant cybercrime cases. CSIRT is an organization or team that provides services and support to prevent, handle, and respond to computer security incidents. The current CSIRT does not have a data storage process and forensic preparation. CSIRT will repeat the procedure, and so on. This is a repeating procedure; the attack will occur once, and only a technical problem will arise. Therefore, the research entitled "Analysis of Knowledge Management Strategies for Handling Cyber Attacks with the Computer Security Incident Response Team (CSIRT)" is expected to implement this Knowledge Management Strategy to manage existing knowledge so that it can make it easier for the CSIRT team to handle cyber attacks that occur.

Kulla‐RIV: A composing model with integrity verification for efficient and reliable data processing services

AbstractThis article presents the design and implementation of a reliable computing virtual container‐based model with integrity verification for data processing strategies named the reliability and integrity verification (RIV) scheme. It has been integrated into a system construction model as well as existing workflow engines (e.g., Kulla and Makeflow) for composing in‐memory systems. In the RIV scheme, the reliability (R) component is in charge of providing an implicit fault tolerance mechanism for the processes of data acquisition and storage that take place in a data processing system. The integrity verification (IV) component is in charge of ensuring that data transmitted/received between two processing stages are correct and are not modified during the transmission process. To show the feasibility of using the RIV scheme, real‐world applications were created by using different distributed and parallel systems to solve use cases of satellite and medical imagery processing. This evaluation revealed encouraging results as some solutions that assumed the cost (overhead) of using the RIV scheme, for example, Kulla (the Kulla‐RIV solution), achieve better response times than others without the RIV scheme (e.g., Makeflow) that remain exposed to the risks caused by to the lack of RIV strategies.

Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing

AbstractTo solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy‐preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and ak‐anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc‐based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system.

Effective Data Management Using Iterative Approach in Data Systems

This study explored memory management in large datasets from a user-centric perspective, filling a research gap often overlooked. While previous projects primarily aimed to improve data maintenance techniques, this research sought to scrutinize the process of data storage and management within these systems. The primary objective was to identify and analyze the issues encountered throughout the stages of the public tendering process and present potential solutions. The existing system faced application performance issues, bid submission delays, and complexities in bid evaluation, often due to a lack of clarity in the scope of work. In response, the proposed system introduces a user-friendly auction platform with enhanced data management capabilities, catering to sellers, bidders, and merchants. It streamlines sensitive data handling, bidding records, and transactions while employing a divide-and-iterate approach for improved efficiency. This study's contribution lies in addressing the critical challenges in online bidding processes and offering innovative solutions for enhanced performance and data management, with future potential for blockchain and smart contract integration.

My Insurance Agent App

In today’s world life is full of risks so everyone wants to make himself secure, everyone wants to keep them on the safe side policy plays a vital role and provides them insurance about their money security, health security, accidental security, etc. This system has been proposed to maintain the record of the policyholder, Customer details, policy detail. My Insurance Agent App is Web Base Application implemented in java platform using MySql as backend application. The main aim of the project is any customer they can access the system through website at any time without going to the company. This project is to develop a web application for insurance company to atomize work procedure, using this system agents and policy holders can know details about present policies, schemes, policy specifications, terms and conditions on policy, policy registration by the customers. Agents commission is based upon customer policy registration and target agent achieves for every month or year. In present system there is no need of human interference in calculating any details. Total work is done using management system which will save time and less paper work and even human resource. The web based application is used within their organization under the distributed accessibility to check the status of the customers who have taken new policies and their proper track and reminding of policy premium payments. The major problem under the manual process is to keep track of the existing insurance policy holders, and remaining them at regular intervals about their policy premium payments. In order to render the ordinary services also it takes a great lot of time in just searching through the registers for the existing customer’s base. If the process of data storage can be automated and follow up of search can be increased, there is always a heavy chance of getting extra customers in turn, which can increase the profits ratio upon the organization

Camera trap sampling protocols for open landscapes: The value of time‐lapse imagery

AbstractCamera traps (CT) have been used to study a wide diversity of wildlife around the world. However, despite their widespread use, standardized protocols are lacking, potentially leading to reduced efficiency and inhibiting study comparisons, generalizability, and repeatability. While there are general guidelines and considerations researchers should be aware of when designing a CT survey, studies have shown the vital importance of selecting sampling schemes and camera settings tailored to specific characteristics of the wildlife system of interest. For many species and regions, optimal sampling protocols have not been thoroughly evaluated, especially in vast open landscapes. We used CT data on barren‐ground caribou (Rangifer tarandus) in the open landscape of arctic Alaska as a case study to evaluate and quantify the influence of camera trigger type (i.e., motion detection vs. time‐lapse) and time‐lapse interval on data generation to inform sampling protocols for future CT research in this system or others like it. Comparing camera trigger types, we found 5 min interval time‐lapse generated seven‐times more images containing caribou compared to motion detection. However, the detection rate of motion detection was over 11‐times greater than time‐lapse resulting in more efficient data collection with respect to camera battery life, data storage, and data processing time. Exploring the effect of time‐lapse interval length, we found detections were highly sensitive to interval length with a 30 min interval producing 33.7% fewer images containing caribou and identifying 22.2% fewer trap days containing caribou compared to a 5 min interval. Our results provide insight into effective CT sampling protocols for open landscapes and highlight the importance of critically evaluating and selecting camera settings that account for characteristics of the study system to ensure adequate data is generated efficiently to address study objectives.