Database Usage Research Articles

Convolutional neural network-based strategies for efficient content-based image retrieval

Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using deep learning. The paper starts with a thorough analysis of the current state of CBIR methods and the difficulties they face. Afterwards, it explores CNN’s design and operation, focusing on CNN’s capacity to learn hierarchical features from images autonomously. This paper also looks at how the model performs when it alters its hyperparameters, transfer learning techniques, and CNN topologies. The insights obtained from these experiments enhance the comprehension of the elements impacting CNN effectiveness in CBIR. Finally, our study shows that CNNs can change the game for image search by transforming CBIR systems. This research adds to the expanding body of information about using cutting-edge deep learning algorithms to make image retrieval more efficient and accurate.

Evaluation of the Usage of Graph Database in a Nutrition App for Determining Potential Nutrient Inhibition

Abstract In recent years, the popularity of veganism has surged, prompting a growing interest in its health implications. While vegan diets offer numerous benefits, they also pose challenges due to potential deficiencies in essential nutrients and the presence of anti-nutrients in plant-based foods. This paper addresses the need for a comprehensive understanding of nutrient interactions, particularly those involving antinutrients, by creating a Knowledge Graph. Utilizing Natural Language Processing techniques, essential nutritional entities were extracted from unstructured texts, and a custom Named Entity Recognition model was implemented. These data were then used to construct a Knowledge Graph using Neo4j, a leading graph database. Despite the potential of the developed Knowledge Graph to visualize nutrient interactions, limitations persist due to the scarcity of available data on antinutrient interactions. Nonetheless, the Knowledge Graph provides a promising avenue for exploring and understanding the complexities of nutrient interactions, facilitating future research and informing dietary choices.

Leveraging Databases for Effective Policing: A Strategic Approach to Modern Law Enforcement

The incorporation of databases in law enforcement has significantly revolutionized policing by improving operational efficiencies, enhancing decision-making, and advancing crime prevention strategies. This study explores how databases contribute to effective policing by assessing their utility in crime mapping, predictive analytics, criminal identification, and evidence management. It highlights critical challenges, including worries about data privacy, the necessity for collaboration between agencies, data falsification, errors in data handling, inadequate infrastructure, insufficiently trained personnel, and technological constraints. Additionally, the study offers recommendations for the ethical optimization of database usage, emphasizing the importance of balancing technological advancements with the protection of individual rights. By investigating these aspects, the research aims to provide insights into the best practices for leveraging database capabilities in policing while addressing ethical considerations and challenges.

Information seeking behavior of graduate business students: using a qualitative approach to determine the role of the library

Librarians interviewed 15 graduate business students to understand their information-seeking behavior. Google emerged as the primary research tool, though students encountered paywalls and credibility concerns. Their research skills were shaped by prior work experience, and the depth of research expertise varied according to their bachelor’s degrees. Students were more likely to seek assistance from classmates and professors than librarians. Faculty guidance and database usage varied significantly, impacting students’ use of library databases. Student’s ability to interpret financial and economic data was a significant issue. This study underscores the need for increased library instruction and promotion of specialized business databases.

Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain.

Though the rise of big data in the field of occupational health offers new opportunities especially for cross-cutting research, they raise the issue of privacy and security of data, especially when linking sensitive data from the field of insurance, occupational health or compensation claims. We aimed to validate a large, blinded synthesized database developed from the CONSTANCES cohort by comparing associations between three independently selected outcomes, and various exposures. From the CONSTANCES cohort, a large synthetic dataset was constructed using the avatar method (Octopize) that is agnostic to the data primary or secondary data uses. Three main analyses of interest were chosen to compare associations between the raw and avatar dataset: risk of stroke (any stroke, and subtypes of stroke), risk of knee pain and limitations associated with knee pain. Logistic models were computed, and a qualitative comparison of paired odds ratio (OR) was made. Both raw and avatar datasets included 162,434 observations and 19 relevant variables. On the 172 paired raw/avatar OR that were computed, including stratified analyses on sex, more than 77% of the comparisons had a OR difference ≤0.5 and less than 7% had a discrepancy in the statistical significance of the associations, with a Cohen's Kappa coefficient of 0.80. This study shows the flexibility and the multiple usage of a synthetic database created with the avatar method in the particular field of occupational health, which can be shared in open access without risking re-identification and privacy issues and help bring new insights for complex phenomenon like return to work.

Impacts of educational interventions of librarian instruction on health information seeking attitudes and behaviors in an employee wellness program.

Health literacy and its potential impacts on the wellbeing of patrons remain a highly regarded objective among health science and medical librarians when considering learning outcomes of patron communities. Librarians are positioned to champion literacy instruction activities. This study aimed to examine health information seeking attitudes and behaviors in an academic-based employee wellness program before and after health literacy workshops were developed and facilitated by an academic health sciences librarian. The intervention included instruction informed by Don Nutbeam's Health Literacy Framework and the Research Triangle Institute's Health Literacy Conceptual Framework. Sixty-five participants obtained through convenience sampling attended workshops and were invited to respond to pre- and post-session surveys. Using a quantitative quasi-experimental methodology, surveys collected health literacy indicators including preferred sources and handling practices of in-person and online health information. Findings indicated workshops influenced information seeking behaviors as participants documented a decrease in social media use for health and wellness information (-36%) and medical information (-13%). An increase in the usage of consumer health databases (like Medline Plus) was also indicated post-workshop for health and wellness information (18%) and medical information (31%). Favorable impacts are evident in this small-scale study; however, more research is needed to confirm the influence of these methods on larger and more diverse populations. Librarians should continue to develop and disseminate theory-informed tools and methods aimed at engaging various communities in constructive health information seeking practices.

The Association between Ovarian Cancer and the Incidence of Newly Developed Dry Eye Disease: A Nationwide Population-Based Study.

We aim to investigate the potential correlation between the presence of ovarian cancer and the development of dry eye disease (DED) via the usage of the Longitudinal Health Insurance Database (LHID) of Taiwan. A retrospective cohort study was executed, and patients with ovarian cancer were selected according to the diagnostic and procedure codes. One ovarian cancer patient was matched to four non-ovarian cancer participants which served as control group, and a total of 4992 and 19,968 patients constructed the ovarian cancer and control groups, respectively. The primary outcome in the current study is the development of DED according to the diagnostic and procedure codes. Cox proportional hazard regression was utilized to produce the adjusted hazard ratio (aHR) and related 95% confidence interval (CI) of DED between the two groups. There were 542 and 2502 DED events observed in the ovarian cancer group and the control group, respectively. The ovarian cancer group illustrated a significantly higher incidence of DED development than the control group after the adjustment of several confounders (aHR: 1.10, 95% CI: 1.01-1.21, p = 0.040). In the subgroup analysis stratified by age, ovarian cancer patients aged older than 60 years showed a higher incidence of DED compared to the non-ovarian cancer population (aHR: 1.19, 95% CI: 1.08-1.28, p = 0.011). In addition, ovarian cancer patients with a disease duration longer than five years also showed higher incidence of DED formation than the non-ovarian cancer population (aHR: 1.13, 95% CI: 1.04-1.22, p = 0.027). In conclusion, the presence of ovarian cancer is associated with higher incidence of subsequent DED, especially in those older than 60 years and with a disease interval of more than five years.

Glaucoma Detection Using Explainable AI and Deep Learning

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection. RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

Digital Criminal Biometric Archives (DICA) and Public Facial Recognition System (FRS) for Nigerian criminal investigation using HAAR cascades classifier technique

In Nigeria, there are many different security concerns and thus crimes have increased despite the fact that there are stringent laws and punishments in place to deter them, making it appear as though the authorities are unable to stop it. In order to identify criminals and conduct investigations, it is imperative that a facial recognition system be connected to a constantly updated digital library. The focus of this paper is to develop an automatic criminal investigation system that can identify criminals based on their faces and produce real-time digital archives about them. However, as an object detection method and facial recognition model, the new system is built on the Haar Cascades Classifier technique in the OpenCV package. Additionally, appropriate programming languages that may provide the needed results were investigated. Python 3.6 was used with the Django 4.2 framework, OpenCV-Python, and Dlib for language execution. Due to Django's ORM, support for numerous databases, and usage of the SQLite3 database, a straightforward database was employed for lightweight applications. The 12 factor app idea was used to construct the DICA-FR system's essential skills. Face detection was applied to the image using the Haar method during processing, and during post-processing, the discovered face was compared with well-known criminal face encodings for matching purposes. Results demonstrated that DICA-FRS could effectively replace human systems since it can recover faces from the furthest distances, display the name of the offender, and sound an alert on the DICA web app's output screen. The DICA system is a working prototype of a system that might be used in the criminal investigative process in Nigeria.

Influence of organizational support on the use of legal databases among lawyers in Ibadan, Oyo State, Nigeria

The emergence of information and communication technology did not exclude the legal profession from its disruptive impact, given rise to the availability of legal databases for legal researches therefore understanding understudying the factors that influences the use of these legal databases is crucial. This study at the organizational level investigated the organizational supports as factor that influences that use of legal databases among lawyers in Ibadan, Oyo State, Nigeria. The study adopted the descriptive survey design. Using Taro Yamane, the study arrived at a sample of 333 out of a population of 2000 lawyers in Ibadan, Oyo State, Nigeria. Questionnaire was used as instrument for data collection. Descriptive and inferential statistics were used to analyze the research questions and hypotheses respectively. The hypotheses were tested using p-values at 0.05 level of significance. The study found a moderate usage (Mean=2.8) of legal databases among lawyers. The test of hypothesis found that organization factors has a positive significant influence on use of legal databases (AdjR 2 =15.3, p=0.001 < 0.05). The study concluded that the place of organization supports in terms of provision of funds, training and retraining, mentoring are inevitable in enhancing quality usage of legal databases for optimum legal researches. It was recommended that law firms, and law schools should increase the frequency and depth of training programmes focused on the use of legal databases. Law firms should establish a training programme aimed at improving the 21st century information literacy skills of lawyers.

Offloaded Data Processing Energy Efficiency Evaluation

The growing popularity of mobile and cloud computing raises new challenges related to energy efficiency. This work evaluates four various SQL and NoSQL database solutions in terms of energy efficiency. Namely, Cassandra, MongoDB, Redis, and MySQL are taken into consideration. This study measures energy efficiency of the chosen data storage solutions on a selected set of physical and virtual computing nodes by leveraging Intel RAPL (Running Average Power Limit) technology. Various database usage scenarios are considered in this evaluation including both local usage and remote offloading. Different workloads are benchmarked through the use of YCSB (Yahoo! Cloud Serving Benchmark) tool. Extensive experimental results show that (i) Redis and MongoDB are more efficient in energy consumption under most usage scenarios, (ii) remote offloading saves energy if the network latency is low and destination CPU is significantly more powerful, and (iii) computationally weaker CPUs may sometimes demonstrate higher energy efficiency in terms of J/ops. An energy efficiency measurement framework is proposed in order to evaluate and compare different database solutions based on the obtained experimental results.

Flow regionalization using precipitation data from different bases as a predictive variable

In studies of flow regionalization, the uncertainties associated with the precariousness or non-existence of flow and precipitation data are problems faced by researchers and managers of water resources, especially in emerging countries such as Brazil. Given this, the usage of precipitation databases obtained by satellites has become more prevalent, allowing accurate precipitation estimates in regions where punctual data obtained from rain gauges are precarious. Therefore, the objective of this study was to compare different precipitation databases, used as predictive variables, in flow regionalization studies. The study area considered was the hydrographic basin of the Paranaíba River. Flow data from streamflow gauges present in the study area and precipitation data obtained from rain gauges were used, which were interpolated by the simple kriging method, the TRMM satellite and WorldClim. The study area was divided into four homogeneous regions, and only for region 4 the generated regionalization equations have some use restriction, since some adjustments proved to be unsatisfactory. The best fits of the regionalization equations were obtained using the precipitation data from the rain gauges interpolated by simple kriging, however, the use of precipitation data from TRMM and WorldClim as predictive variables of the flow provided similar results. Therefore, it can be considered that the alternative databases (TRMM and WorldClim) used in the study are presented as an option to replace the data observed in the rain gauges, resulting in a gain of time by the researchers and management bodies in the studies of flow regionalization.

Infinite Archives, Infinite Possibilities: Learning Research and Databases with Archive of our Own

This article will discuss the importance of acknowledging the information practices of subcultural groups within library instruction and fostering an inclusive learning environment with the implementation of a workshop by comparing research databases with the popular fanfiction website, Archive of Our Own. By incorporating AO3 into library instruction, students’ interests and prior experiences were engaged by utilizing the principles of subcultural capital. The workshop utilized students’ knowledge of information searching from their personal lives and their interests to highlight similarities with academic research using examples such as filters, keywords, and author searching. This allowed students to develop skills to search precisely for materials in a familiar environment. Further, the success of the workshop demonstrates how librarians can incorporate fan’s informational behaviors, and access practices into instructional content about research and database usage, making research less intimidating and more accessible to students.

Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries

Existing visual abstraction of a property graph query by representing it as a labeled atomic graph (LAG) has great potential to democratize the usage of property graph databases as it enables user-friendly visual query formulation without demanding the need to learn a property graph query language e.g., Cypher. Unfortunately, existing LAG-based query interfaces do not embrace HCI principles and psychology theories to inform their design and as a result may have adverse impact on their usability and aesthetics. In this paper, we depart from the classical theory- and principles-oblivious LAG abstraction to present a novel theory-informed visual abstraction called labeled composite graph (LCG) to address this limitation. It realizes a novel and extensible visual shape definition language called VEDA to create and maintain an LCG systematically, guided by a variety of theories and principles from HCI, visualization and psychology. We build a novel LCG-based visual property graph query interface for Cypher called SIERRA and demonstrate through a user study its superiority to an industrial-strength LAG-based query interface for property graphs w.r.t. usability, aesthetics and efficient query formulation.

Investigation of the Effectiveness of Usage of Graph Databases for Big Data Analysis

The purpose of research. The purpose of this work is to study graph models of databases and develop a methodology for comparative analysis of database models. The theoretical and methodological basis of the study was the fundamental scientific works of domestic and foreign authors in the field of basic problems of database theory, algorithm theory, graph theory, data processing structures and methods.Methods. The paper uses methods of structural, comparative and content analysis, as well as statistical methods of information processing and methods of graph theory. As a result of the conducted research, the authors justified the features, advantages and disadvantages of using a graph data model.Results. The relevance of this study is due to the intensive development of information technologies intended for the economic development of the country, the pandemic and the geopolitical situation in the world. These prerequisites orient researchers to use new methods of data processing and analysis. However, it is possible to optimize big data processing processes not only with the help of powerful new algorithms, but also with the use of fundamentally different data structures and models other than relational.The paper presents applied examples of using the graph model of databases in various subject areas. A method of comparative analysis of data models in relation to big data analysis has been developed. The main points of data model design are highlighted: system scaling, compliance with requirements and standards, the ability to change data model structures, language complexity, performance and data processing speed. The proposed technique made it possible to numerically evaluate the effectiveness of graph models. Conclusion. The theoretical significance of the research consists in the development of methodological and technological approaches to the analysis of big data and the formation of structures and databases. The practical results of the study can be useful to large IT companies, as well as to the financial, logistics and commercial sectors, where the problem of big data analysis and research is most acute.

Impact of TLS Overhead on Segmented Network for Cloud Database Systems

Cloud database serves flexible, affordable, and scalable database system. Even the cloud database is secure with Transport Layer Security (TLS), but the performance overhead that TLS introduces when executing operations on one of the major No SQL databases: Mongo DB in terms of latency. To explore TLS performance overhead for Mongo DB, we performed two tests simulating common database usage patterns. We first investigated connection pooling, where an application uses a single connection for many database operations. Then, we considered one request per connection in which an application opens a connection, executes a process, and immediately closes the connection after completing the operation. Our experimental result shows that applications that cannot endure significant performance overhead should be deployed within a properly segmented network, rather than enabling TLS. Applications using TLS should use a connection pool rather than a connection-per-request.

Automation of Life Cycle Assessment—A Critical Review of Developments in the Field of Life Cycle Inventory Analysis

The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation.

Use of Online Databases by Undergraduate Students at a Health Sciences College in Malawi

Online databases, which are more recent electronic information advances have influenced the delivery of information services in institutions of higher learning. This major worry regarding resource under-utilization prompted this research in order to understand participants’ use of these online databases as well as the reasons for such patterns at Saint John of God College of Health Sciences in Malawi. To investigate the utilization of online databases by students at Saint John of God College of Health Sciences in Malawi, this quantitative study employed questionnaires to collect the data. According to the findings, enhancing users’ knowledge and awareness of online databases has the ability to increase online database usage levels. Online databases are beneficial to students’ progress in completing diverse educational tasks. Finally, the study identified factors that may inspire or dissuade users to use online databases.

Опыт и перспективы автоматизации управления перевозочным процессом скоростного транспорта городских агломераций

The article analyses the technological process of transportation process organization and its control in different systems of urban agglomeration rapid-transit transport. It is presented the generalization of organization technological schemes of transportation process at the compilation of normative documents-schedules of: train traffic, rolling stock turnover, work of locomotive teams. Common features, allowing to share automation and digitization positive experience from one transport systems to another, are revealed. As a typical example for urban agglomeration rapid-transit transport, the work of Moscow Central Ring in data flow diagram notation is considered. It has been shown that the conditions of traffic planning on Moscow Central Ring are analogous to active ones on the subway ring lines. As generalizing notions, there are: mixing, non-parallelism, zoning, non-autonomy. Corresponding illustrative examples are given. Congested experience in the sphere of control automation for transportation process of rapid-transit transport of urban agglomeration is considered on the examples of railway section Nizhniy Novgorod – Uren’ as well as Kaluzhsko-Rizhskaya line of Moscow subway (electrodepots “Kaluzhskoye” and “Sviblovo”). The article describes initial data sets for to perform train traffic schedule, the purpose of its performance has been formulated, limitations, reflecting the links between objects, inside the set of given resources, and limitations, being defined by rules of passenger service, have been revealed. Analysis, pursued in the article, has shown the perspective directions of automated transport systems development on knowledge accumulated bases. As a result of the application of complex approach to the solution of automated control tasks at the use of artificial intelligence technologies and big databases usage, it’s planned to increase the efficiency usage for given resources set, train traffic schedule implementation percentage and others; to reduce information transfer error number as well as those, appeared as a result of negative human factor influence and so on.

“Nothing new under the sun”; do we need new searching protocols to establish the patentability of industry 4.0 inventions?

Efficient patentability searching demands that the searcher has the ability to identify efficiently the best sources of likely prior art for each invention being searched. This applies both within industry, at the point of drafting a patent application, and within patent offices conducting searches to support substantive examination. Industry 4.0 inventions are characterised by a particular nature and combination of technologies, notably the inclusion of aspects of Information and Communication Technologies (ICT). The default information sources currently used to establish patentability of ‘conventional’ inventions may be inappropriate for this new class of invention. This is partly due to the different structures of publication and methods of dissemination of research results found within computer science professions. It is suggested that searching in the secondary or tertiary literature may be a more fruitful approach to establish patentability in these areas of technology. However, expertise in the use of these types of source has been substantially lost, as the information industry has concentrated on production of full-text primary sources. Database production, database usage and search protocols all need to be reassessed if they are to meet the challenges of searching Industry 4.0 inventions.