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The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Digital service platforms provided by academic support departments in Macao assist academic staff and students in various areas such as registry, student affairs, academic activities, and research. As the number of undergraduate students increases and new departments are established, academic staff often face the challenge of dealing with paperwork that contains similar content but different formats. This situation results in redundancies and a waste of time. This paper presents our endeavors to simplify administrative procedures in higher education by automating restructured documentation and developing secondary file systems. The paper presents two case studies: Scenario One focuses on streamlining the publication system for academic staff who submit papers in different formats. At the same time, Scenario Two aims to simplify the daily paperwork process for academic staff. Both cases involve transforming the distribution of administrative documents, transitioning from a standardized form with guidelines to a customized form with concise tips. This approach allows academic staff to handle only the necessary information, which may not be available in the database or requires verification. The case studies serve to demonstrate the effectiveness of this administrative simplification. [ABSTRACT FROM AUTHOR]

Unikernels are specialized operating system (OS) kernels optimized for a single application or service, offering advantages such as rapid boot times, high performance, minimal memory usage, and enhanced security compared to general-purpose OS kernels. Unikernel applications must remain compatible with the runtime environment of general-purpose kernels, either through binary or source compatibility. As a result, many Unikernel projects have prioritized system call compatibility over performance enhancements. In this paper, we explore the design principles of Unikernel file systems and introduce a new file system tailored for Unikernels named ULFS (Ultra Lightweight File System). ULFS provides system call services akin to those of general-purpose OS kernels but achieves superior performance and security with significantly fewer system resources. Specifically, ULFS is developed as a lightweight file system embracing Unikernel design principles. It streamlines system calls, removes unnecessary locks, and omits permission checks for multiple users, utilizing a non-hypervisor architecture. This approach significantly reduces the memory footprint of the file system and enhances performance. Through measurement studies, we assess the performance and memory requirements of various file systems from major Unikernel projects. Our findings demonstrate that ULFS surpasses several existing Unikernel file systems, including Rumpvfs, Ramfs-u, Ramfs-q, 9pfs, and Hcfs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the personal information management (PIM) behaviours of social science faculty in Africa. The study examined the experiences and encounters of selected social scientists in Africa in organising and finding and re-finding of the information they previously created or stored. More specifically, the study sought to examine how faculty keep and refind information, the files and folders in which they store the information. Also, the study examined the nature and characteristics of faculty information spaces with particular respect to electronic documents including emails and paper documents. Design/methodology/approach: Sample survey research design and a mixed methods approach consisting of qualitative and quantitative were used. Data was collected using a discursive technique, an interview schedule and a questionnaire. Data analysis was conducted using factorial analysis of mixed data design, guided by a combination of category and codes identification using NVivo and Statistical Package for the Social Sciences (SPSS) 17. Principal component analysis (PCA) of factor analysis was executed to identify key components. Findings: Eleven issues, namely, time, infrastructure, importance of the information, folder/file management, document characteristics and organisational context played significant roles in the PIM behaviours of the respondents. Others were importance of the information, document overload, memory, workload and computer literacy. PCA extracted four major components, namely, document overload, time, computer literacy and importance of the information. Research limitations/implications: An expansion in the number of faculty involved in this study would probably yield a more reliable outcome. Extending the study to cover Africa would also yield a more applicable result. Practical implications: The key PIM issues identified in this study, namely, document overload, time, computer literacy and importance of the information should constitute the focus of continuous information literacy education aimed at improving PIM social scientists' faculty in Africa. Social implications: Improved PIM of social science faculty will result to improved research productivity and good health. Originality/value: PIM of social scientists has not been examined in the literature, and yet it is crucial for further understanding their learning and information behaviours, and improving their productivity. The design and administration of a questionnaire constructed based on codes extracted from qualitative and discursive sessions to the same respondents from whom the qualitative data was collected makes the findings very strong. A further deployment of factorial analysis of mixed data design to handle qualitative data makes the contribution of the study very significant. [ABSTRACT FROM AUTHOR]

As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in terms of execution time and accuracy. Malicious email was filtered with MapReduce programming using the Naïve Bayes technique, which is a supervised machine learning method, in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied. According to the results of a comparison of the accuracy and predictive error rates of the two methods, the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method. [ABSTRACT FROM AUTHOR]

Cluster computing has become an inevitable part of data processing as the huge volume of data being produced from different sources like online social media, IoT, mobiledata, sensor data, black box data and so on increases in an exponentially fast manner. Distributed File System defines different methods to distribute, read and eliminate the files among different cluster computing nodes. It is found that popular distributed file systems such as Google File System and Hadoop Distributed File System store metadata centrally. This creates a chance for a Single Point of Failure that arises the need for backup and alternative solutions to recover the metadata on the failure of the metadata server. Also, the name node server is built using expensive and reliable hardware. For small and medium clusters, it is not cost effective to maintain expensive name node server. Even though cheap commodity hardware may substitute the name node functionality, they are prone to hardware failure. This paper proposes a novel distributed file system to distribute files over a cluster of machines connected in a Peer-to-Peer network. The most significant feature of the file system is its capability to distribute the metadata using distributed consensus, using hash values. Although the distributed metadata is visible to the public, the methodology ensures that it is immutable and irrefutable. As part of the in-depth research, the proposed file system has been successfully tested in the Google Cloud Platform. Also, the basic operations like read, write, and delete on Distributed File System with distributed metadata are compared with that of Hadoop Distributed File System based on distribution time on the same cluster setup. The novel distributed file system provides better results compared to the existing methodologies. [ABSTRACT FROM AUTHOR]

Recently, decentralized file systems are widely used to overcome centralized file systems' load asymmetry between nodes and the scalability problem. Due to the lack of a metadata server, decentralized systems require more RPC requests to control metadata processing between clients and servers, which adversely impacts the I/O performance and traffic imbalance by increasing RPC latency. In this paper, we propose an efficient I/O scheme to reduce the RPC overhead in decentralized file systems. Instead of sending a single RPC request at a time, we enqueued the RPCs in the global queue and merged them into larger RPC requests, thus avoiding excessive RPC latency overheads. The experimental results showed that our scheme improves write and read performance by up to 13% and 16%, respectively, compared with those of the original. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Document clustering is frequently used in applications of natural language processing, e.g. to classify news articles or create topic models. In this paper, we study document clustering with the common clustering pipeline that includes vectorization with BERT or Doc2Vec, dimension reduction with PCA or UMAP, and clustering with K-Means or HDBSCAN.We discuss the interactions of the different components in the pipeline, parameter settings, and how to determine an appropriate number of dimensions. The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. Hence, we recommend configuring UMAP so as to optimize its time efficiency. According to our topic model evaluation, the combination of BERT and UMAP, also used in BERTopic, performs best. A topic model based on this pipeline typically benefits from a large number of clusters. [ABSTRACT FROM AUTHOR]

Existing nonvolatile memory (NVM)-based file systems can fully leverage the characteristics of NVM to obtain better performance than traditional disk-based file systems. It has the potential capacity to efficiently manage metadata and perform fast metadata operations. However, most NVM-based file systems mainly focus on managing file metadata (inode), while pay little attention to directory metadata (dentry), which also has a noticeable impact on the file system performance. Besides, the traditional journaling technique that guarantees metadata consistency may not yield satisfactory performance on NVM-based file systems. To solve these problems, in this article we propose a fast and low overhead metadata operation mechanism, called FLOMO. It first adopts a novel slotted-paging structure in NVM to reorganize dentry for efficiently performing dentry operations, and utilizes the red–black tree in DRAM to accelerate dentry lookup and the search process of dentry deletion. Moreover, FLOMO presents a selective journaling scheme for metadata updates, which partially logs the changes related to dentry in the proposed slotted page, thereby, mitigating the redundant journaling overhead. To verify FLOMO, we implement it in a typical NVM-based file system, the persistent memory file system (PMFS). Experimental results show that FLOMO accelerates the metadata operations in PMFS by 34.4% $\sim 59$ %, and notably reduces the journaling overhead for metadata, shortening the latency by 59% on average. For real-world applications, FLOMO has higher throughput compared with PMFS, PMFS without journal, and NOVA, achieving up to $2.1\times $ , $1.1\times $ , and $1.3\times $ performance improvement, respectively. [ABSTRACT FROM AUTHOR]

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain's decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions. [ABSTRACT FROM AUTHOR]

This annotation describes the HMML Authority File (HAF), an open access database of authority records used by the Hill Museum & Manuscript Library that currently focuses on name authorities related to the Eastern Christian and Islamic manuscript traditions, but will eventually include all of HMML's authority files. It explains the project's history, describes how the file is populated, and discusses the best methods for searching and accessing the rich data stored in the file. As the file is a Beta project, the annotation also discusses its shortcomings and their intended solutions, as well as other future directions. [ABSTRACT FROM AUTHOR]

In this article, the author offers suggestions for writing a cover letter for an academic position. It mentions cover letter as a genre of academic writing means recognizing that it presents an opportunity to make an argument and cover letters show that the applicant has considered the needs of the hiring institution. It also mentions tempting to take a cover letter off the shelf and out of the electronic files.

Compared with traditional files, electronic personnel files have the characteristics of the economy, environmental protection, convenience, and sharing and are gradually replacing traditional paper files. However, the development of electronic archives is still in its infancy, and there are still many problems, including the professional quality of personnel, information management, and the security of electronic archives storage. As an emerging technology, blockchain technology has the characteristics of decentralization, immutability, and traceability. This paper applies blockchain technology in the management of electronic archives, overcomes the internal distortion and insecurity of electronic archives, and designs an electronic archives management module based on blockchain technology. Through the application in some schools, the effectiveness and practicability of the algorithm are proved. [ABSTRACT FROM AUTHOR]

Cloud computing plays a significant role in effective data handling based on the increase in data usage in various realtime applications. Data auditing is performed on certain files and the authenticator with deduplication. It addresses the problem of key management to deduce the file content based on the malicious activities performed on the cloud. So, based on effective auditing of the integrity of the data and authenticator, the data is checked properly and minimizes the overhead of cloud storage overhead. In this work, the cloud audit and authenticator approach is proposed based on a certain file system, which makes the malicious user get authenticate the data auditing verification as the existing algorithm has low security based on entropy. They propose a data auditing approach that integrates with file management and the authenticator of data deduplication. The proposed approach performs the authenticator process and new form of file tag, which helps guarantee effective security based on the random generation of message key. The proposed approach achieves minimum computational overhead based on the authenticator and data block generation in the performance analysis. Then the security verification is performed on various attacks, such as brute force attack, a man-in-the-middle attack, etc., to check whether the approach is safe or unsafe against the attacks. [ABSTRACT FROM AUTHOR]

Mobile network operators store an enormous amount of information like log files that describe various events and users' activities. Analysis of these logs might be used in many critical applications such as detecting cyber attacks, finding behavioral patterns of users, security incident response, and network forensics. In a cellular network, call detail records (CDRs) is one type of such logs containing metadata of calls and usually includes valuable information about contacts such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call), and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper's primary goal is to study subscribers' behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and neural network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies. [ABSTRACT FROM AUTHOR]

In file systems, a single write system call can make multiple modifications to data and metadata, but such changes are not flushed in an atomic way. To retain the consistency of file systems, conventional approaches guarantee crash consistency in exchange for sacrificing system performance. To mitigate the performance penalty, non-volatile memory (NVM) technologies are believed to be a good candidate for this purpose owing to their low latency and byte-addressibility. However, none of the prior proposals that exploit NVM provide both scalability and strict ordering of modifications. In this article, we propose montage, a crash consistency framework for file systems that consists of two parts. First, montage splits NVM space into multiple staging areas and synchronizes the flushing of modifications these to the storage device. Second, montage uses the pipelined architecture to speed up data flushing to storage. We apply montage to two journaling file systems (ext4 and JFS) and evaluate them on a multicore server with high-performance storage. The evaluation results demonstrate that our design exhibits better performance by a wide margin than recent NVM-based journaling file systems. [ABSTRACT FROM AUTHOR]

The proprietary software investments in the data integration field are incrementing, and the progresses are visible in the possibility to directly open in a GIS environment a 3D software data format. Still, this is limited to the integration between the proprietary data formats and standards, ArcGIS environment shapefile multipatch and Revit 3D model, by using a proprietary software (ArcGIS). This study takes advantage of the lesson-learnt results in the proprietary data integration field, wanting to replicate a similar result using the IFC open standard, which is not directly openable by a GIS interface and needs to overcome a conversion that in most of the cases leads to semantic and geometric losses. So, an IFC-to-shapefile data conversion was performed, stressing (i) the way information is stored in the attribute table to query the geometries and perform geoprocessing, by (ii) implementing workarounds to keep the Revit instances' shared parameters in the IFC file, (iii) meanwhile having a high Level of Detail of the HBIM. The research performed the IFC-to-shapefile data conversion through FME (Feature Manipulation Engine), benefitting of the flexibility of the shapefile format and of the IFC' possibility to keep a high LOD in the export phase. Both allowed to properly query and manage the elements of an HBIM in a GIS (ArcGIS environment), and, using relational attributes table, retrieve the information contained in each Revit instance' property panel, as the shared parameters that implement the BIM Level of Information (LOI). [ABSTRACT FROM AUTHOR]

The IEEE P2731 working group is in the process of developing a functional model so that virtually any BCI system could be described according to it. In this paper, we want to stimulate the discussion for the definition and selection of the information that should be stored in a file for effectively allowing the sharing of BCI data and tools amongst researchers. Establishing these requirements and procedures will accelerate BCI development and lay the foundation for accessible and scalable BCI technology as well as provide the foundations for the definition of a standard file format. [ABSTRACT FROM AUTHOR]

Throughout the ages, safely preserving and transmitting data that have extraordinary importance for humanity has increased its importance with rapid advances in computer technology. Steganography stores hidden data within the files, which are unnoticed by third parties, so it provides secure transmission of data to the receiver. In this study, a steganography-based GUI design has been carried out, which ensures that the agricultural data is safely stored and communicated to the other party. We used LSB one-bit, two-bit, three-bit substitution and PVD algorithms with GUI for stages of agricultural data hiding and extracting at cover images. We also provided extra security using the embedded key and shifting operations on the hidden data before hiding data the cover image. In short, we confused the hidden data in the cover image so that malicious people can't understand. In experimental studies, performance analysis was evaluated by comparing various criteria as similarity ratio (Structural Similarity Index Measure, SSIM), stego image quality (Peak Signal-to-Noise Ratio, PSNR) and data hiding capacity (Payload). [ABSTRACT FROM AUTHOR]

Mitchell-Schuitevoerder, Rosemary 2020 A Project-Based Approach to Translation Technology London and New York Routledge xxxv, 164 9780367138844 I A Project-Based Approach to Translation Technology i is probably the most updated, adaptable, and accessible resource for translators to effectively implement translation technologies in every step of the translation workflow. Chapter 1 covers the basic principles of CAT tools and discusses how translators, language service providers (LSPs) and clients interact with these tools. Mitchell-Schuitevoerder not only mentions computer literacy skills as essential for learning about CAT tools but specifically highlights how these skills can ensure a successful CAT tool operation. Chapter 3 deals with the integration of machine translation (MT) into CAT tools and focuses on the efficient use of MT in the translation workflow. [Extracted from the article]

HDFS is a popular open-source system for scalable and reliable file management, which is designed as a general-purpose solution for distributed file storage. While it works well for medium or large files, it will suffer heavy performance degradations in case of lots of small files. To overcome this drawback, we propose here a system to enhance HDFS with a distributed true full-text search system SAES of 100% recall and precision ratios. By indexing the meta data of each file, e.g., name, size, date and description, files can be quickly accessed by efficient searches over metadata. Moreover, by merging many small files into a large file to be stored with better space and I/O efficiencies, the negative performance impacts caused by directly storing each small file individually are avoided. An experimental study is conducted for function and performance tests on both realistic and artificial data. The experimental results show that the system works well for file operations such as uploading, downloading and deleting. Moreover, the RAM consumption for managing massive small files is dramatically reduced, which is critical for good system performance. The proposed system could be a potential storage solution for massive small files. [ABSTRACT FROM AUTHOR]

The Program is a mediator as all IDEs are mediator programs, in the sense that IDEs like Eclipse, Eric IDE, and net beans are considered as a mediator between a programmer and Interpreter or the programming language compiler. In fact, in this paper we can say that all things the programs do can be done with a simple text editor and we can see the Program output by direct contact with interpreter or compiler. The only difference between the Program provided by me and other IDEs is that the environment is special for building the extension. It means that instead of being an interface between user and interpreter or compiler, standards provided by other applications are usually different. The program tries to fill in the absence of an Efficient IDE in the field of building an extension. On the other hand, it reduces the cost of extension standard providers for presenting an IDE specific to that standard. In fact, in the second target the program provided will be a platform that has the ability to mount the next standards. Standard providers should only conduct a Part of coding. A constant part is common in all standards. We should find a solution for them in all IDEs, such as file management and information management that have been implemented in constant part of the IDE. The variable and non-similar part is implemented as classes. If needed to implement, it should be inherited from them and some of their functions must be overridden. [ABSTRACT FROM AUTHOR]

Various techniques have been used in distributed file systems for data availability and stability. Typically, a method for storing data in a replication technique-based distributed file system is used, but due to the problem of space efficiency, an erasure-coding (EC) technique has been utilized more recently. The EC technique improves the space efficiency problem more than the replication technique does. However, the EC technique has various performance degradation factors, such as encoding and decoding and input and output (I/O) degradation. Thus, this study proposes a buffering and combining technique in which various I/O requests that occurred during encoding in an EC-based distributed file system are combined into one and processed. In addition, it proposes four recovery measures (disk input/output load distribution, random block layout, multi-thread-based parallel recovery, and matrix recycle technique) to distribute the disk input/output loads generated during decoding. [ABSTRACT FROM AUTHOR]

The infectious disease prevention information system has the problems that data is difficult to circulate and share between monitoring systems.To solve these problems,this paper proposed an infectious disease data sharing model based on delegated proof of stake consensus algorithm combined with blockchain technology.By virtue of the characteristics of the blockchain,such as decentralization,non-tampering and collective maintenance,it used double-chain as the blockchain architecture,which improved the work efficiency.Combined with inter-planetary file system,it obtained large capacity storage space,which solved the space problem faced by block data storage and ensured the stability and sharing security of data storage.This paper proposed a consensus algorithm for disease prevention and control to realize the efficient operation.In addition,by comparing with other data sharing models,the advantages of this model in data storage and sharing security are highlighted. [ABSTRACT FROM AUTHOR]

Summary: In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded. [ABSTRACT FROM AUTHOR]

As a popular consortium blockchain platform, Hyperledger Fabric has received increasing attention recently. When executing transactions on such platform, it usually costs a lot of time and hardly to achieve high efficiency. Although efficiently handling transactions can be leveraged to support various use-cases, it presents significant challenges as data on Hyperledger Fabric is organized on file-system and exposed via limited API. We tackle the problem in two ways: conditional queries and data storage. In this paper, we propose the following novel methods. To improve the performance of conditional queries on Hyperledger Fabric, we use all attributes of the query to create composite keys before executing it. In order to achieve further performance improvements, we build an index called AUP in the second method, where we also study the update of AUP during transactions. To speed up data storage on Hyperledger Fabric, We create a cache for the data in the block header. The extensive experiments conducted on the real-world dataset demonstrate that the proposed methods can achieve high performance in terms of efficiency and memory cost. Finally, We implement a prototype system. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation seamlessly. Moreover, it's equipped with newly proposed, more efficient feature screening rules, which substantially accelerate the computation. Benchmarking experiments show that our biglasso package, as compared to existing popular ones like glmnet, is much more memory-and computation-efficient. We further analyze a 36 GB simulated GWAS data set on a laptop with only 16 GB RAM to demonstrate the out-of-core computation capability of biglasso in analyzing massive data sets that cannot be accommodated by existing R packages. [ABSTRACT FROM AUTHOR]