Data Management Challenges Research Articles

Towards FAIR Data Access

Towards FAIR Data Access

Digital Humanities Solutions for pan-European Numismatic and Archaeological Heritage Based on Linked Open Data

This paper discusses current challenges in archaeological cultural heritage data management and presents the interdisciplinary research project DigiNUMA. The project investigates solutions in data harmonisation and dissemination of pan-European cultural heritage through an interdisciplinary and cross-sectoral project in Digital Humanities, semantic computing, participatory heritage, museum collections management and archaeological/numismatic studies. Using Finnish and English numismatic data as a test case, DigiNUMA creates ontological infrastructure and a proof-of-concept data model for finely-grained Linked Open Data (LOD) harmonisation across national and international databases for cultural heritage data, and tests it through a broad suite of Digital Humanities analyses.

Statistical Analysis of Misfiling Causes in Higher Education: A Test Construction

Introduction: Misfiling remains a substantial challenge in data management in various institutions of higher learning. It has been asserted that one in every ten documents or files stored becomes immediately lost forever, due to inadequate filing techniques and practices. The results of this canker primarily are a high-profile failure in accountability and a retrogression in the development of the institution. There is paucity in scales developed for measuring misfiling. This study developed a scale called the data misfiling assessment scale (DMAS) to measure the various factors responsible for misfiling in institutions of higher learning and assessed them in terms of other relevant variables.
 Methodology: A questionnaire was designed and administered in a cross-sectional study conducted among all administrative assistants who were tasked with the utmost responsibility for filing in the offices of the University of Health and Allied Sciences (UHAS), Ho in the Volta Region of Ghana. The various components of the causes of the misfiling scale were found using factor analysis and were confirmed with confirmatory factor analysis.
 Results: The study revealed a highly reliable scale (Cronbach’s alpha of 0.85) of thirteen misfiling items sub-classified into four factors of misfiling namely; internal factors of misfiling, misfiling due to methods and tools, external factors of misfiling, and other factors of misfiling, with Cronbach’s alpha of 0.85, 0.77, 0.62, and 0.60, respectively. The four-factor structure of the DMAS was confirmed with CFA. A strong positive relationship was observed between internal and external factors of misfiling (p-value = 0.015) and between internal and other factors of misfiling (p-value = 0.032). External factors of misfiling also strongly correlated positively with other factors of misfiling (p-value = 0.025). Per the scale, participants who have file references in their offices were less likely to face other factors of misfiling (p-value = 0.008). Also, those working in offices with filing policies were less likely to experience internal and external factors of misfiling.
 Conclusion: With the help of this scale, factors responsible for misfiling and by extension, challenges with data management can be investigated and appropriate actions are taken for accurate data management in institutions of higher learning.

Genetic-based optimization in fog computing: Current trends and research opportunities

Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a comprehensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a taxonomy of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, workflow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

Internet of Things data management: A systematic literature review, vision, and future trends

SummaryThe Internet of Things (IoT) is expected to connect devices with unique identifiers over a network to create an equilibrium system with high speeds and volumes of data while presenting an interoperability challenge. The IoT data management system is indispensable for attaining effective and efficient performance because IoT sensors generate and collect large amounts of data used to express large data sets. IoT data management has been analyzed from various perspectives in numerous studies. In this study, a Systematic Literature Review (SLR) method was used to investigate the various topics and key areas that have recently emerged in IoT data management. This study aims to classify and evaluate studies published between 2015 and 2021 in IoT data management. Therefore, the classification of studies includes five categories, data processing, data smartness application, data collection, data security, and data storage. Then, studies in each field are compared based on the proposed classification. Each study investigates novel findings, simulation/implementation, data set, application domain, experimental results, advantages, and disadvantages. In addition, the criteria for evaluating selected articles for each domain of IoT data management are examined. Big data accounts for the highest percentage of data processing fields in IoT data management, at 34%. In addition, fast data processing, distributed data, artificial intelligence data with 22%, and data uncertainty analysis account for 11% of the data processing field. Finally, studies highlight the challenges of IoT data management and its future directions.

GEN Interview—Overcoming Challenges in Biological Data Management

GEN Interview—Overcoming Challenges in Biological Data Management

Analysis of critical success factors for blockchain technology implementation in healthcare sector

PurposeRecently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.Design/methodology/approachThe paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.FindingsBased on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.Originality/valueThis study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

The secondUSpresidential social media transition: How private platforms impact the digital preservation of public records

AbstractA second presidential social media transition in the United States occurred as Joe Biden took office on January 20, 2021. In the years since Barack Obama pioneered the use of platforms like Facebook and Twitter while President, Donald Trump shaped his Presidency around the use of Twitter, primarily through a personal account created before entering politics. In this paper, we examine Donald Trump's use of Twitter during his presidency as a lens through which to understand the ongoing archival preservation and data management challenges posed by social media platforms. The blurred lines between public and private records, deleting tweets, and the preservation issues that appeared after his suspension from Twitter and other platforms following the January 6, 2021 insurrection at the US Capitol all highlight an urgent, ongoing need by archivists, digital preservationists, and information scholars to consider how we might collect and manage social media records in an ever‐changing information landscape. This paper draws primarily on publicly available information from existing preservation initiatives to analyze the state of digital preservation for presidential records. Our findings highlight how both public and private entities manage and provide access to Donald Trump's tweets, pointing to broader implications for social media data preservation.

Data management for production quality deep learning models: Challenges and solutions

Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of data cannot be compromised. This study aims to explore the data management challenges encountered by practitioners developing systems with DL components, identify the potential solutions from the literature and validate the solutions through a multiple case study. We identified 20 data management challenges experienced by DL practitioners through a multiple interpretive case study. Further, we identified 48 articles through a systematic literature review that discuss the solutions for the data management challenges. With the second round of multiple case study, we show that many of these solutions have limitations and are not used in practice due to a combination of four factors: high cost, lack of skill-set and infrastructure, inability to solve the problem completely, and incompatibility with certain DL use cases. Thus, data management for data-intensive DL models in production is complicated. Although the DL technology has achieved very promising results, there is still a significant need for further research in the field of data management to build high-quality datasets and streams that can be used for building production-ready DL systems. Furthermore, we have classified the data management challenges into four categories based on the availability of the solutions.

IoT Data Analysis Using Different Clustering Algorithms: A Survey

There has been a major change in the rise of data creation and consumption due to the quick development of contemporary technology and the advent of the Internet of Things (IoT). With this shift comes a significant challenge in data management, with the goal of extracting useful information and patterns from the vast amount of unstructured IoT data. However, due to some limitations, we are still encountering issues and challenges in obtaining patterns and meaning information, and thus formulating a research agenda to get a sense of the challenges that one might encounter while conducting research on clustering of this massive chunk of unstructured IoT data. In light of these considerations, this study looks at some clustering methods that may be utilized when working with unstructured IoT data.

Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study.

BackgroundData journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support.ObjectiveTo apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service.MethodsAn incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges.ResultsResults were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email.ConclusionsOverall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks.

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

Organizing Large Data Sets for Efficient Analyses on HPC Systems

Upcoming exascale applications could introduce significant data management challenges due to their large sizes, dynamic work distribution, and involvement of accelerators such as graphical processing units, GPUs. In this work, we explore the performance of reading and writing operations involving one such scientific application on two different supercomputers. Our tests showed that the Adaptable Input and Output System, ADIOS, was able to achieve speeds over 1TB/s, a significant fraction of the peak I/O performance on Summit. We also demonstrated the querying functionality in ADIOS could effectively support common selective data analysis operations, such as conditional histograms. In tests, this query mechanism was able to reduce the execution time by a factor of five. More importantly, ADIOS data management framework allows us to achieve these performance improvements with only a minimal amount of coding effort.

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE.

The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer.

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost.

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.

Failure Prediction in Robotic Spot-Welding Applications – Challenges in Data Management

Even though the automotive industry is a highly optimized production sector, robotic spot-welding processes are still characterized by downtimes caused by tool contamination, cable breaks, communication errors of safety elements or errors due to clamping devices – to name a few. With the increasing developments in digitization and IIoT integration, a large amount of data can be recorded to identify the causes of the mentioned errors. However, data management is a highly challenging task – mainly due to the reasons of data quality, volume and harmonization. This paper presents an overview of the different data sources of modern production facilities and challenges in data understanding. The article focuses especially on the challenges for the needed IT infrastructure to harmonize and use different data sources and volumes within a data management pipeline for applying machine-learning models in order to be able to predict production downtimes in robotic spot-welding applications based on available data and data sources.

3DPro: Querying Complex Three-Dimensional Data with Progressive Compression and Refinement.

Large-scale three-dimensional spatial data has gained increasing attention with the development of self-driving, mineral exploration, CAD, and human atlases. Such 3D objects are often represented with a polygonal model at high resolution to preserve accuracy. This poses major challenges for 3D data management and spatial queries due to the massive amounts of 3D objects, e.g., trillions of 3D cells, and the high complexity of 3D geometric computation. Traditional spatial querying methods in the Filter-Refine paradigm have a major focus on indexing-based filtering using approximations like minimal bounding boxes and largely neglect the heavy computation in the refinement step at the intra-geometry level, which often dominates the cost of query processing. In this paper, we introduce 3DPro, a system that supports efficient spatial queries for complex 3D objects. 3DPro uses progressive compression of 3D objects preserving multiple levels of details, which significantly reduces the size of the objects and has the data fit into memory. Through a novel Filter-Progressive-Refine paradigm, 3DPro can have query results returned early whenever possible to minimize decompression and geometric computations of 3D objects in higher resolution representations. Our experiments demonstrate that 3DPro out-performs the state-of-the-art 3D data processing techniques by up to an order of magnitude for typical spatial queries.

Emerging Concepts Using Blockchain and Big Data

Blockchain and big data are the emerging technologies that are on the highest agendas of the firms. These are significantly expected to transform the ways in which the business as well as the firm run. These are on the verge of increasing the expectation of the distributed ledgers, which would keep the firms away from struggling challenges. The concept of big data and Blockchain has been used up by various other concepts that would help secure and interpret the information. The ideal solutions offered by these technologies shall address the challenges of big data management as well as for analytics. In addition to that, Blockchain provides its own consensus method, which is the primary means to create an audit trail. This enables users to verify all transactions. The audit trail is a means of verifying the correctness and integrity of every transaction, regardless of who owns the asset. The Blockchain can also verify that different parties of a transaction are following an agreement and not breaking the agreement. Moreover, there have been continuous arguments in the concept of Blockchain at which the bitcoin is fundamental and there are several popular blockchain approaches developed which would deliver performance, security as well as privacy. Apart from this, the use of Blockchain plays a major role in adding an extra data layer for the big data analytics process. Big data is considered secure which cannot be further forged with the network architecture. The current paper shall be discussing the emerging concepts that are using Blockchain as well as big data.

Izazovi upravljanja podacima u Industriji 4.0

The paper was created by reviewing the literature and business practices, which include the challenges of big data management in Industry 4.0. 30 scientific papers by eminent authors were analyzed, which emphasize the importance of Big Data and propose the implementation of one of the data management methods. Significant results of improvement through the application of new technologies have been observed in all industries. The aim of this paper is to summarize the experience of the company and include solutions for the most efficient data management in business practice, starting from data collection and storage to making the most important decisions.