Life Cycle Data Research Articles

The Circular Digital Cockpit: Towards an actionable framework for life cycle circularity assessment and decision

Digital solutions such as IoT, machine learning, big data, and simulation platforms are increasingly integrated into industrial practices but are not fully leveraged for enhancing circularity in the value chain. To address this, the Circular Digital Cockpit (CDC) framework is proposed to aid manufacturers and public authorities in transitioning towards circularity. The CDC is structured to analyze and improve organizational activities, incorporating Data Science, Digital Enterprise Architecture, and Circular Economy Strategies. It comprises two main parts. The first, "Measure, Understand, Prioritize", focuses on transforming lifecycle data into circularity indicators suitable for various organizational roles, like product and process managers. This helps in prioritizing improvement objectives both individually and organization-wide. The second part, “Act – Imagine, Simulate, Decide, Deploy –, Monitor”, guides each actor in developing and implementing actions aimed at circularity and sustainability goals. This involves assembling a global action plan and employing the Theory of Change methodology to assess and refine these actions based on their impact on circularity and sustainability. The CDC's adaptability and potential in enhancing circular practices across various industries and territorial levels are tested in diverse scenarios, including the remanufacturing of heavy vehicles, the maintenance of medical devices, and the management of bio-waste and construction waste.

A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint.

The rotating machinery is continuously monitored in practical application. However, the historical life-cycle data cannot be always preserved due to the limited storage resource; meanwhile, the on-site computing platform cannot process a large number of monitoring samples. It brings a great challenge for the remaining useful life (RUL) prediction. Thus, continuous learning (CL) is introduced into RUL prediction model for achieving its knowledge accumulation and dynamic update. To improve the performance of continuous RUL prediction, this article presents a new RUL prediction methodology with a multistage attention convolutional neural network (MSACNN) and knowledge weight constraint (KWC). First, an improved multihead full-channel sight self-attention (MFCSSA) mechanism is proposed to capture the global degradation information across all channels. MSACNN is then constructed by embedding MFCSSA, squeeze-and-excitation (SE) mechanism, and convolutional block attention module (CBAM) into different stages of feature extraction, which enables it to capture the global degradation information and refine the feature representations progressively. The KWC mechanism based on the importance of weight parameters and gradient information is proposed and integrated into MSACNN to achieve the continuous RUL prediction task. The proposed KWC can effectively alleviate catastrophic forgetting in CL. Finally, the experimental results on the life-cycle bearing and gear datasets demonstrate that MSACNN has a higher accuracy than the existing prediction methods. Moreover, the KWC mechanism performs better than typical CL methods in retaining the previously learned knowledge while acquiring the new task knowledge. Therefore, the proposed methodology can be better applied to the continuous RUL prediction tasks than the advanced methods of the same kind.

An NLP-based framework for early identification of design reliability issues from heterogeneous automotive lifecycle data

Natural Language Processing is increasingly used in different areas of design and product development with varied objectives, from enhancing productivity to embedding resilience into systems. In this paper, we introduce a framework that draws on NLP algorithms and expert knowledge for the automotive engineering domain, to extract actionable insight for system reliability improvement from data available from the operational phase of the system. Specifically, we are looking at the systematic exploration and exploitation of automotive heterogeneous data sources, including both closed-source (such as warranty records) and open-source (e.g., social networks, chatrooms, recall systems) data, to extract and classify information about faults, with predictive capability for early detection of issues. We present a preliminary NLP-based framework for enhancing system knowledge representation to increase the effectiveness and robustness of information extraction from data, and discuss the temporal alignment of data sources and insight to improve prediction ability. We demonstrate the effectiveness of the proposed framework using real-world automotive data in a recall study for a vehicle lighting system and a particular manufacturer: four recall campaigns were identified leading to corrective actions by the warranty experts.

The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact

Although there exists a rich literature on data lifecycle, a common framework for data lifecycle depicts reuse as the last stage. However, this framework fails to explain the complex lifecycle of machine learning (ML) data sets, which can have many different afterlives. Data sets for ML can be expanded to supplement previous research, and researchers can concatenate multiple data sets to develop new models. This study discusses ML dataset reuse through the lens of the data–information–knowledge–wisdom pyramid. In social science research, researchers might reuse data to analyse a new research question that is still in the context of the data domain. By contrast, research practices in ML, where researchers layer multiple data sets for training purposes, require us to ask whether the existing data lifecycle model, ending with ‘reuse’, is appropriate for explaining such an iterative and layered lifecycle. This study introduces one case of merging computer vision data set and natural language processing data set and two cases of applying ML models from outside of the ML community (hate speech detection and politeness detection) to justify a framework for a ML dataset lifecycle. Last but not least, this study proposes a ML dataset lifecycle and provides case examples to describe each stage.

Accelerated Life Testing Dataset for Lithium-Ion Batteries with Constant and Variable Loading Conditions

The development of new modes of transportation, such as electric vertical takeoff and landing (eVTOL) aircraft and the use of drones for package and medical delivery, has increased the demand for reliable and powerful electric batteries. The most common batteries in electric-powered vehicles use Lithium-ion (Li-ion). Because of their long cycle life, they are the preferred choice for battery packs deployed over a lifespan of many years. Thus, battery aging needs to be well understood to achieve safe and reliable operation, and life cycle experiments are a crucial tool to characterize the effect of degradation and failure. With the importance of battery durability in mind, we present an accelerated Li-ion battery life cycle data set, focused on a large range of load levels, for batteries composed of two 18650 cells. We tested 26 battery packs grouped by: (i) constant or random loading conditions, (ii) loading levels, and (iii) number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously-aged packs were assembled to second-life packs. The goal is to provide the PHM community with an additional data set characterized by unique features. The aggressive load profiles create large temperature increases within the cells. Temperature effects becomes therefore important for prognosis. Some samples are subject to changes in amplitude and number of load levels, thus approaching the level of variability encountered in real operations. Reassembling of survival cells into new packs created additional data that can be used to evaluate the performance of recommissioned batteries. The data set can be leveraged to develop and test models for state-of-charge and state-of-health prognosis. This paper serves as a companion to the data set. It outlines the design of experiment, shows some exemplifying time-series voltage curves and aging data, describes the testbed design and capabilities, and also provides information about the outliers detected thus far. Upon acceptance, the data set will be made available on the NASA Ames Prognostics Center of Excellence Data Repository.

Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit

The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle.

Operational Efficiency and Environmental Impacts of Food Service Establishments in Phuket, Thailand

The expansion of global tourism development has led to an increase in environmental burdens. This study aimed to assess the operational performance and the environmental impacts associated with food service establishments in Phuket, an international tourist island in Thailand. A joint application of life cycle assessment and data envelopment analysis was employed to evaluate environmental burdens and calculate the efficiency scores encompassing several inputs and outputs of each food service establishment. There are several characteristics of food service establishments that affect their operational and environmental performance. The results showed that location, opening hours, and surrounding conditions are key factors driving the performance of air-conditioned food service establishments, while the performance of non-air-conditioned ones is mainly decided by opening hours. However, these factors are not significant for the performance of street food service establishments. It is advised to carefully consider the characteristics of the included food service establishments based on their products and services to enhance their performance. As an effort to achieve a balanced connection between environmental quality and economic growth, it is essential to evaluate both operational and environmental performance. Attaining maximum operational efficiency does not necessarily ensure the same for eco-efficiency.

RESTRICTED DATA MANAGEMENT: THE CURRENT PRACTICE AND THE FUTURE.

Many organizations across the world that manage restricted data have adapted the Five Safes framework (safe data, safe projects, safe people, safe setting, safe output) for their management of restricted and confidential data. While the Five Safes have been well integrated throughout the data life cycle, organizations encounter several challenges with regard to safe data management. In this paper, we review current practices for restricted data management, and discuss challenges and future directions. We focus on data use agreements, disclosure risk review, and training. In the future, organizations managing restricted data may need proactively to take into consideration reducing inequalities in access to scientific data, preventing unethical use of data, and managing various types of data.

Protecting Sensitive Data Early in the Research Data Lifecycle

How do researchers in fieldwork-intensive disciplines protect sensitive data in the field, how do they assess their own practices, and how do they arrive at them? This article reports the results of a qualitative study with 36 semi-structured interviews with qualitative and multi-method researchers in political science and humanitarian aid/migration studies. We find that researchers frequently feel ill-prepared to handle the management of sensitive data in the field and find that formal institutions provide little support. Instead, they use a patchwork of sources to devise strategies for protecting their informants and their data. We argue that this carries substantial risks for the security of the data as well as their potential for later sharing and re-use. We conclude with some suggestions for effectively supporting data management in fieldwork-intensive research without unduly adding to the burden on researchers conducting it.

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, , and Recall, confirming the effectiveness of the proposed method.

Empowering smart city governance through decentralized blockchain solutions for security and privacy in IoT communications

This paper highlights the benefits and challenges of smart cities, which leverage technology to offer a wide range of services to citizens. While these services have the potential to greatly improve the quality of life in metropolitan areas, they also raise significant privacy and security concerns. The study emphasizes the need to employ "privacy by design" principles to ensure that personal data is protected throughout the entire lifecycle of data, and data owners have the self-control to manage their data according to their preferences. Smart contracts, built on blockchain technology, offer a secure and transparent way of conducting transactions, particularly in e-governance, and automating processes. By leveraging these technologies, smart cities can address the privacy and security challenges they face while continuing to offer cutting-edge services to their inhabitants. Ultimately, the study emphasizes the importance of a proactive approach to privacy and security in the development and implementation of smart cities.

Collaboration System for Multidisciplinary Research with Essential Data Analysis Toolkit Built-In

Environmental research calls for a multidisciplinary approach, where highly specialized research teams collaborate in data analysis. Nevertheless, managing the data lifecycle and research artifacts becomes challenging because the project teams require techniques and tools tailored to their study fields. Another pain point is the unavailability of essential analysis and data representation formats for querying and interpreting the shared results. In addition, managing progress reports across the teams is demanding because they manage different platforms and systems. These concerns discourage the knowledge-sharing process and lead to researchers’ low adherence to the system. A hybrid methodology based on Design Thinking and an Agile approach enables us to understand and attend to the research process needs. As a result, a microservices-based architecture of the system, which can be deployed in cloud, hybrid, or standalone environments and adapt the computing resources according to the actual requirements with an access control system based on users and roles, enables the security and confidentiality, allowing the team’s lead to share or revoke access. Additionally, intelligent assistance is available for document searches and dataset analyses. A multidisciplinary researchers’ team that uses this system as a knowledge-sharing workspace reported an 83% acceptance.

Multi-source and multimodal data fusion for improved management of a wastewater treatment plant

Wastewater treatment plants (WWTPs) are complex systems presenting stochastic, non-linear, and non-stationary behavior, which makes their operational management very challenging. In this context, data collected from distributed sources across the plant play a central role in the optimized operation and control of WWTPs. However, even when available, the use of collected data is far from trivial due to the coexistence of asynchronous measurements, data with different granularity, measurements of different quality (precision, accuracy), multimodal sources (sensors, spectra, images, hyphenated instrumentation), among other aspects related to the data life cycle. Such heterogeneity in process data characteristics hinders the application of most off-the-shelf data analytics methods. Flexible solutions able to cope with the complexity of systems and of the data they generate are therefore necessary to overcome these limitations and enable an effective analysis and operation of WWTPs. In this article, data-fusion approaches for handling multiple heterogeneous sources of process data are developed and comparatively tested. Priority is given to solutions that can flexibly be adapted to different specific operational contexts. The methodologies are tested on an industrial case study (WWTP), where the concentration of a toxin in the effluent stream is to be predicted from available heterogeneous data. Single- and multi-source modeling approaches are contemplated and a nested cross-validation method was developed to handle the time-series nature of the models. Bayesian fusion synergistically combines data from different sources considering their uncertainty, standing out among the methodologies tested as offering a good balance in terms of accuracy (RMSEP = 1.34), stability (prequential IQR = 0.034), and flexibility (to accommodate missing and new sources).

Environmental impacts of earth observation data in the constellation and cloud computing era

Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade reaching the current population of 1193 (January 2023). Consequently, EO data volumes have mushroomed and data storage and processing have migrated to the cloud. Whilst attention has been given to the launch and in-orbit environmental impacts of satellites, EO data environmental footprints have been overlooked. These issues require urgent attention given data centre water and energy consumption, high carbon emissions for computer component manufacture, and difficulty of recycling computer components. Doing so is essential if the environmental good of EO is to withstand scrutiny. We provide the first assessment of the EO data life-cycle and estimate that the current size of the global EO data collection is ~807 PB, increasing by ~100 PB/year. Storage of this data volume generates annual CO2 equivalent emissions of 4101 t. Major state-funded EO providers use 57 of their own data centres globally, and a further 178 private cloud services, with considerable duplication of datasets across repositories. We explore scenarios for the environmental cost of performing EO functions on the cloud compared to desktop machines. A simple band arithmetic function applied to a Landsat 9 scene using Google Earth Engine (GEE) generated CO2 equivalent (e) emissions of 0.042–0.69 g CO2e (locally) and 0.13–0.45 g CO2e (European data centre; values multiply by nine for Australian data centre). Computation-based emissions scale rapidly for more intense processes and when testing code. When using cloud services such as GEE, users have no choice about the data centre used and we push for EO providers to be more transparent about the location-specific impacts of EO work, and to provide tools for measuring the environmental cost of cloud computation. The EO community as a whole needs to critically consider the broad suite of EO data life-cycle impacts.

Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations.

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.

PACMMOD Volume 1, Issue 3: Editorial

We are excited to introduce this new issue of PACMMOD (Proceedings of the ACM on Management of Data). PACMMOD is a new journal, concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. Articles published at PACMMOD address data challenges at various stages of the data lifecycle, from modeling, acquisition, cleaning, integration, indexing, querying, analysis, exploration, visualization, interpretation, and explanation. They focus on dataintensive components of data pipelines, and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems), operating within accuracy, privacy, fairness, and diversity constraints. Articles reporting deployed systems and solutions to data science pipelines and/or fundamental experiences and insights from evaluating real-world data engineering problems are especially encouraged.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

InterMat: A Blockchain-Based Materials Data Discovery and Sharing Infrastructure

Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data.

Digital Twin for Intelligent Network: Data Lifecycle, Digital Replication, and AI-Based Optimizations

Digital Twin for Intelligent Network: Data Lifecycle, Digital Replication, and AI-Based Optimizations

Hurricane: a Dataflow-oriented Data Service for Smart Cities Applications

The concept of Smart Cities has gained relevance, especially in the last decade, due to the availability of data associated with cities, e.g., car traffic, public transportation, crime data, etc. The purpose of using these data is to improve the services offered to the citizens. Most of these applications manipulate spatiotemporal data. These data are processed in a dataflow that starts with the collection, integration, and aggregation and ends with visualization. This way, specialized data services for smart city applications are most welcome. However, many of the existing data services in this context, are either specific to a particular application/domain or do not consider the entire data life cycle. In this article, we present Hurricane, a dataflow-oriented data service for smart city applications. Hurricane executes multiple dataflows to gather, pre-process, integrate, and public data. Hurricane was evaluated with an application in the area of public security and results reinforced the importance of this type of data service.