Large-scale Information Systems Research Articles

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcome this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

Robodebt: A Socio-Technical Case Study of Public Sector Information Systems Failure

Large-scale public sector information systems (PSIS) that administer social welfare payments face considerable challenges. Between 2014 and 2023, an Australian government agency conceived and implemented the Online Compliance Intervention (OCI) scheme, widely referred to as Robodebt. The scheme's primary purpose was to apply digital transformation in order to reduce labour costs and increase recovery of overpayments. Among its key features were a simplified, but inherently erroneous, estimation method called income averaging, and a new requirement that welfare recipients produce documentation for income earned years earlier. Failure by welfare recipients to comply with mandates resulted in the agency recovering what it asserted to be overpayments. This article presents a case study of Robodebt and its effects on over 1 million of its clients. The detailed case study relies on primary data through Senate and other government hearings and commissions, and secondary data, such as media reports, supplemented by academic sources. Relevant technical features include (1) the reliance on the digital persona that the agency maintains for each client, (2) computer-performed inferencing from client data, and (3) automated decision-making and subsequent action. This article employs a socio-technical systems approach to understanding the factors underlying a major PSIS project failure, by focusing on the system's political and public service sponsors; its participants (users); the people affected by it (usees); and the broader economic, social, and political context. Practical and theoretical insights are presented, with the intention of highlighting major practical lessons for PSIS, and the relevance of an articulated socio-technical frame for PSIS.

The Role of Health Informatics Research: A Case of a Large-Scale Implementation in Norway.

Many see the role of health informatics research as informing the development and implementation of information technology in clinical practice. The aim of this study is to see if this role is realized in the ongoing implementation of a large-scale health information system in central Norway. By doing a document analysis of the planning documents for the implementation, we assess to what extend evidence from the scientific community is explicitly referenced and used in the implementation planning. We found that evidence available is not explicitly used, and that evidence required is not widely available.

A secure and lightweight cloud data deduplication scheme with efficient access control and key management

Data deduplication technology is extensively employed to enhance the storage efficiency of cloud servers by eliminating redundant files. Cloud users commonly encrypt their data prior to uploading it to the server. Conventional encryption algorithms, however, lead to the encryption of duplicated data from different users into distinct ciphertexts. Consequently, these ciphertexts must be stored in the cloud since the cloud server cannot identify such duplicated data. In this paper, we introduce a hybrid cloud-based secure deduplication scheme tailored for implementation on large-scale data systems. Specifically, our approach leverages ciphertext-policy attribute-based encryption (CP-ABE), which enables us to establish access control and key management via a private cloud server. Simultaneously, we leverage a public cloud server to cater to enterprises and groups seeking secure data storage. Notably, our approach ensures mutual zero-interaction verification between both public and private cloud servers through ElGamal encryption, thereby guaranteeing data unforgeability. The security assessment illustrates that our proposed approach ensures both data privacy and integrity. We also show that the approach resists brute-force attacks on the dictionary, prevents malicious users from deceiving cloud servers to return incorrect ciphertext, and achieves secure and efficient access control and key management. Furthermore, functional and performance evaluation underscores the superiority of our method over five other classical data deduplication schemes. Under the premise of having more comprehensive security settings, the performance of the scheme still maintains a good level at every stage.

Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure

Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.

Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure

Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.

Risk management in large-scale information system projects

This article deals with project management in information systems, whose relevance lies in the vital importance of these systems in modern companies. Information systems are essential for decision making and data management in today's interconnected world. Project management, on the other hand, coordinates elements such as scope, resources, costs, schedules and risks to achieve defined objectives. The systems development life cycle (SDLC) structures the process, encompassing phases such as scope definition, planning, execution, monitoring and closure. These phases are integrated with risk management, which identifies, evaluates and mitigates threats and opportunities. Mitigation strategies act before adversity, while contingency planning prepares for the unforeseen. That is why risk management is integrated throughout the project life cycle to anticipate and address challenges. The combination of both aspects is critical in a constantly evolving technology environment. In addition, organizational culture and communication play a critical role. A culture of awareness and accountability, transparency in communication and active stakeholder participation are essential. Training and continuous adaptation allow learning from past experiences and improving practices.

Managing large-scale projects: Unpacking the role of project memory

Drawing on ethnographic fieldwork conducted in a US-based firm engaged in a large-scale information system implementation project, this study presents an inductively developed framework of project memory incorporating three mnemonic co-orchestration practices: situated focusing, synchronizing discourse, and converging boundaries. Project memory is proposed to help manage challenges posed by large-scale projects as we observed in this study. This study offers insights into the role and practices of project memory and provides recommendations for managerial practice related to developing narrative and storytelling skills, strategically using meetings, and fostering a culture of cross-functional collaboration in large-scale projects.

Interactive fuzzy knowledge distance-guided attribute reduction with three-way accelerator

Attribute reduction with an accelerator is an efficient strategy to handle large-scale information systems, but it is rarely suitable for information systems with fuzzy decision (ISFD). Meanwhile, both three-way approximations (TA) and fuzzy knowledge distance (FKD) are two types of attractive uncertainty measures in ISFD. Therefore, it prompts us to deliberate on how to interact the merits of TA with FKD to develop an attribute reduction with a three-way accelerator for ISFD. Driven by this concern, the TA of fuzzy decision is used as prior knowledge to interact with FKD, then a model named ITA-FKD is constructed to explore the monotonicity of uncertainty in multi-granularity spaces and measure the significance of every attribute. A three-way accelerator is implemented to eradicate redundant objects and attributes with the aid of the ITA-FKD model. Subsequently, a filter-wrapper approximate attribute reduction with the unique accelerator is successfully devised. Ultimately, some comprehensive experiments with high-dimensional datasets containing biomedicine field exemplified the higher efficiency of the explored algorithm.

DNA barcoding data release for the Phoridae (Insecta, Diptera) of the Halimun-Salak National Park (Java, Indonesia).

Launched in 2015, the large-scale initiative Indonesian Biodiversity Discovery and Information System (IndoBioSys) is a multidisciplinary German-Indonesian collaboration with the main goal of establishing a standardised framework for species discovery and all associated steps. One aspect of the project includes the application of DNA barcoding for species identification and biodiversity assessments. In this framework, we conducted a large-scale assessment of the insect fauna of the Mount Halimun-Salak National Park which is one of the largest tropical rain-forest ecosystems left in West Java. In this study, we present the results of processing 5,034 specimens of Phoridae (scuttle flies) via DNA barcoding. Despite limited sequencing success, we obtained more than 500 clusters using different algorithms (RESL, ASAP, SpeciesIdentifier). Moreover, Chao statistics indicated that we drastically undersampled all trap sites, implying that the true diversity of Phoridae is, in fact, much higher. With this data release, we hope to shed some light on the hidden diversity of this megadiverse group of flies.

Blockchain-Based Security Configuration Management for ICT Systems

The world has become increasingly dependent on large-scale and distributed information and communication technology (ICT) infrastructures and systems in sectors such as energy, transport, banking, healthcare, water supply, and digital services, while their protection is considered of paramount importance and has already drawn remarkable attention from governments and key industry players. Establishing common approaches by leveraging existing frameworks and cyber security practices for improving the security postures of those systems is one of the major objectives for ensuring an adequate level of protection and avoiding the detrimental effects of disruptions on society and citizens. Configuration management (CM) is one of those common practices for establishing and maintaining the integrity and consistency of a system and its elements with regard to the function, performance, and status of technical and physical attributes, and it contributes to a desirable security posture throughout the lifecycle of a system. This study addresses the importance of CM, and while considering the corresponding frameworks, standards, and best practices, it proposes a permissioned blockchain-based approach, that inherits the benefits of the blockchain technology and ensures the integrity of the systems’ configuration across the complete lifecycle management of its products and services as an underlying model for mapping and integrating CM functions. Furthermore, this study briefly presents the benefits and challenges of the application of permissioned blockchain models and proposes a smart-contract-based role-based access control mechanism, in addition to presenting an operating concept based on brief but real-life lifecycle requirements of organizational configuration management.

Time-Resolved Hanbury Brown–Twiss Interferometry of On-Chip Biphoton Frequency Combs Using Vernier Phase Modulation

Biphoton frequency combs (BFCs) are promising quantum sources for large-scale and high-dimensional quantum information and networking systems. In this context, the spectral purity of individual frequency bins will be critical for realizing quantum networking protocols like teleportation and entanglement swapping. Measurement of the temporal autocorrelation function of the unheralded signal or idler photons comprising the BFC is a key tool for characterizing their spectral purity and in turn verifying the utility of the biphoton state for networking protocols. Yet the experimentally obtainable precision for measuring BFC correlation functions is often severely limited by detector jitter. The fine temporal features in the correlation function---not only of practical value in quantum information, but also of fundamental interest in the study of quantum optics---are lost as a result. We propose a scheme to circumvent this challenge through electro-optic phase modulation, experimentally demonstrating time-resolved Hanbury Brown--Twiss characterization of BFCs generated from an integrated 40.5-GHz ${\mathrm{Si}}_{3}{\mathrm{N}}_{4}$ microring, up to a $3\ifmmode\times\else\texttimes\fi{}3$-dimensional two-qutrit Hilbert space. Through slight detuning of the electro-optic drive frequency from the comb's free spectral range, our approach leverages Vernier principles to magnify temporal features, which would otherwise be averaged out by detector jitter. We demonstrate our approach under both continuous-wave and pulsed-pumping regimes, finding excellent agreement with theory. Our method reveals not only the collective statistics of the contributing frequency bins but also their temporal shapes---features lost in standard fully integrated autocorrelation measurements.

Provincial Maternal and Child Information System in Inner Mongolia, China: Descriptive Implementation Study.

After the implementation of 2- and 3-child policies, the rising proportion of high-age and high-risk pregnancies put enormous pressure on maternal and child health (MCH) services for China. This populous nation with an increasing population flow imperatively required the support of large-scale information systems for management. Municipal MCH information systems were commonly applied in developed cities of eastern provinces in China. However, implementation of provincial MCH information systems in relatively low-income areas is lacking. In 2020, the implementation of a regional maternal and child information system (RMCIS) in Inner Mongolia filled this gap. This paper aimed to demonstrate the construction process and evaluate the implementation effect of an RMCIS in improving the regional MCH in Inner Mongolia. We conducted a descriptive study for the implementation of an RMCIS in Inner Mongolia. Based on the role analysis and information reporting process, the system architecture design had 10 modules, supporting basic health care services, special case management, health support, and administration and supervision. Five-color management was applied for pregnancy risk stratification. We collected data on the construction cost, key characteristics of patients, and use count of the main services from January 1, 2020, to October 31, 2022, in Inner Mongolia. Descriptive analysis was used to demonstrate the implementation effects of the RMCIS. The construction and implementation of the RMCIS cost CNY 8 million (US $1.1 million), with a duration of 13 months. Between 2020 and 2022, the system recorded 221,772 registered pregnant women, with a 44.75% early pregnancy registry rate and 147,264 newborns, covering 278 hospitals and 225 community health care centers in 12 cities. Five-color management of high-risk pregnancies resulted in 76,975 (45.45%) pregnancies stratified as yellow (general risk), 36,627 (21.63%) as orange (relatively high risk), 156 (0.09%) as red (high risk), and 3888 (2.30%) as purple (infectious disease). A scarred uterus (n=28,159, 36.58%), BMI≥28 (n=14,164, 38.67%), aggressive placenta praevia (n=32, 20.51%), and viral hepatitis (n=1787, 45.96%) were the top factors of high-risk pregnancies (yellow, orange, red, and purple). In addition, 132,079 pregnancies, including 65,018 (49.23%) high-risk pregnancies, were registered in 2022 compared to 32,466 pregnancies, including 21,849 (67.30%) high-risk pregnancies, registered in 2020. The implementation of an RMCIS in Inner Mongolia achieved the provincial MCH data interconnection for basic services and obtained both social and economic benefits, which could provide valuable experience to medical administration departments, practitioners, and medical informatics constructors worldwide.

On-road driver facial expression emotion recognition with parallel multi-verse optimizer (PMVO) and optical flow reconstruction for partial occlusion in internet of things (IoT)

In recent years, many large-scale information systems in the Internet of Things (IoT) can be converted into interdependent sensor networks, such as smart cities, smart medical systems, and industrial Internet systems. The successful application of Facial Expression Recognition in the IoT will make our algorithms faster, more convenient, lower overall costs, providing better business practices, and enhance sustainability. Facial Expression Recognition (FER)is essential to effectively communicate between human and machines. Video facial expression detection is a crucial component for gauging the driver's mood for driver assistance system. The emotions of the driver have a significant role in dictating the behavior of the driver, according to several studies, which can lead to disastrous car crashes. However, criteria affecting the identification of driver emotions with the right kind of monitoring include changes in stance, lighting, and occlusions. Based on the restoration of the blurred facial region, the Driver Facial Expression Emotion Recognition (DFEER) system was developed to address these issues. One of the first things to do when dealing with a sequence of blurred faces is to calculate the optical fluxes between the frames. After that, an optical flow reconstruction using a trained PMVO is performed to fix the occlusion-induced damage (Parallel Multi-Verse Optimizer). The major solutions are randomly split into groups of occluded and non-occluded optical flows using a parallel technique, and the groups discuss their findings after a set number of iterations. After optical flows have been rebuilt, they are used immediately in the classification phase of expression prediction. In this study, Very Deep Convolution Networks (VGGNet) proposed a method for recognising human emotions. Both the CK+ (Cohn-Kanade database) and the KMU-FED (Keimyung University Facial Expression of Drivers) databases are used to carry out the assessments of the classification model's efficacy. Accuracy, recall, precision, and f-measure the effectiveness of the suggested strategy is then evaluated using the findings.

Unfolding material constraints and opportunities in biodiversity citizen science information practices

The purpose of this paper is to outline an approach for understanding how information practices in biodiversity citizen science are enabled and constrained through participants’ interactions with material objects such as cameras, artificial intelligence-supported smartphone applications and information systems. The approach draws on the theory of objectual practice; how knowledge is constructed iteratively through interaction with epistemic objects. Epistemic objects are understood as objects sustained by projections of knowledge, open for interpretation and question-generation, rather than clearly defined things. The approach is empirically contextualised through a study of biodiversity citizen science activities in relation to a bioblitz, a short-term, intensive period of species observations in a given geographical setting. Three empirical units of analysis are to be studied: binoculars, cameras, and loupes for observing species; field guides, identification keys and smartphone applications for identifying species; and standardising, large-scale information systems for reporting identified observations. The approach opens up for studying how material objects enable and constrain biodiversity citizen science information practices. The objectual practice approach in relation to a multifaceted empirical setting such as biodiversity citizen science extends sociomaterial aspects to citizen science studies by enabling practice-oriented observation and trace investigations of large-scale phenomena.

Mathematical Modeling of Static Data Attribute Encryption Based on Big Data Technology

Attribute encryption is an effective one to many network communication technologies, which supports flexible access control strategies and is very suitable for fine-grained access control in large-scale information systems. In order to improve the attributes of static data, encryption technology can provide a reliable technical guarantee for network security. This paper presents a mathematical modeling method of static data attribute encryption based on big data technology. The big data redundancy elimination algorithm based on similarity calculation is analyzed. By using static data attribute encryption based on big data technology, the length of encrypted data packets will not increase, and partial redundancy of fragments can be eliminated, which can greatly improve the efficiency of the system. The attribute-based encryption mechanism uses attributes as public keys, and the decryption user is a group; so, the encryption efficiency is very high. It can realize efficient encryption and decryption, as well as flexible access control based on user attributes. This scheme can reflect the importance of attributes; so, it is more practical.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades, there is still a lack of progress in information systems dynamics (ISD), owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems. The value of ISD lies in its ability to guide the design, development, application, and evaluation of large-scale information system-of-systems (SoSs), just as mechanical dynamics theories guide mechanical systems engineering. This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space, improving a mathematical theory for information measurement, and proposing a dynamic configuration model for information systems. In this way, it establishes a basic theoretical framework for ISD. The proposed theoretical methodologies have been successfully applied and verified in the Smart Court SoSs Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization. The proposed ISD provides an innovative paradigm for the analysis, design, development, and evaluation of large-scale complex information systems, such as electronic government and smart cities.

Neither opaque nor transparent: A transdisciplinary methodology to investigate datafication at the EU borders

In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders.

A New Pattern Framework and Innovative Practices in the Smart Court System-of-Systems Engineering Project of China

Developing large-scale complex information systems, such as the Smart Court system-of-systems (SoSs) of China, is a worldwide engineering challenge. This paper, from a methodological perspective, aims to expound the theoretical construction and practical progress of Smart Court system-of-systems engineering (SoSE) of China. The concept and key task requirements of SoSE are explored, technical difficulties faced by the Smart Court SoSE are analyzed, and a “two-track parallel, six-ring linkage” pattern framework is proposed for the progressive collaboration SoSE of large-scale autonomous information systems. Based on the theories including a universal information model, information metric system, and dynamic configurations of information systems, a key evaluation indicator system for an information SoSs is proposed. To satisfy the SoSE design requirements, an overall design method based on information relationships and its enabling tool are proposed, and a reference model of the Smart Court SoSs is designed to provide a top-level reference for the system development and integration of the Smart Court. Moreover, the development and collaborative integration of the autonomous and backbone systems in the Smart Court SoSE are presented in a comprehensive manner. The nationwide application and promotion of the Smart Court SoSs support the upgrade and transformation of the conventional judicial operation pattern of people’s courts in China. Through continuous analyses of the quality and effectiveness of the Smart Court based on the key evaluation indicators, targeted improvement can be conducted to further enhance the SoSs capabilities, thereby contributing to the progress of judicial civilization in the information age.

The architecture and creation principles of the key management systems in the large-scale information and telecommunication systems

The architecture and creation principles of the key management systems in the large-scale information and telecommunication systems