Database Environment Research Articles

Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment.

Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated learning (FL) has been introduced to address these issues. However, FL faces challenges such as vulnerability to attacks, non-IID data, reliance on a central server, high communication overhead, and suboptimal model aggregation. Furthermore, FL is not optimized for realistic hospital database environments, where data are dynamically accumulated. To overcome these limitations, we propose federated influencer learning (FIL) as a secure and efficient collaborative learning paradigm. Unlike the server-client model of FL, FIL features an equal-status structure among participants, with an administrator overseeing the overall process. FIL comprises four stages: local training, qualification, screening, and influencing. Local training is similar to vanilla FL, except for the optional use of a shared dataset. In the qualification stage, participants are classified as influencers or followers. During the screening stage, the integrity of the logits from the influencer is examined. If the integrity is confirmed, the influencer shares their knowledge with the others. FIL is more secure than FL because it eliminates the need for model-parameter transactions, central servers, and generative models. Additionally, FIL supports model-agnostic training. These features make FIL particularly promising for fields such as healthcare, where maintaining confidentiality is crucial. Our experiments demonstrated the effectiveness of FIL, which outperformed several FL methods on large medical (X-ray, MRI, and PET) and natural (CIFAR-10) image dataset in a dynamically accumulating database environment, with consistently higher precision, recall, Dice score, and lower standard deviation between participants. In particular, in the PET dataset, FIL achieved about a 40% improvement in Dice score and recall.

IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments

Periodic high-utility itemset (PHUI) mining can extend beyond the conventional approach of high-utility itemset mining by uncovering recurring customer purchase behaviors common in real-life scenarios (e.g., buying apples and oranges every three days or weekly). Such behaviors, particularly in market basket databases, signify stable patterns that ensure long-term profitability. Existing PHUI mining algorithms assume a static database and incur significant costs when handling incremental databases, as each batch of new transactions necessitates reprocessing the entire dataset. To overcome this challenge, we introduce the Incremental Periodic High-Utility Itemset Miner (IPHM), a method for efficiently extracting periodic high-utility itemsets in incremental database environments. We propose an innovative incremental utility-list structure tailored for incremental database scenarios. Effective pruning strategies are employed to expedite the construction and update of incremental utility-lists and to discard unpromising candidates. As demonstrated by the experimental results, the algorithm is efficacious and efficient, highlighting its practical applicability in dynamic data environments. The algorithm shows a remarkable ability to quickly adapt to database changes, making it highly suitable for applications in market basket analysis where frequent updates are common.

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

Comparing the Centers for Medicare and Medicaid Services (CMS) enrollment data death dates to the National Death Index (NDI).

In the United States, the National Death Index (NDI) is the most complete source of death information, while epidemiologic studies with mortality outcomes often rely on U.S. Medicare data for outcome ascertainment. The purpose of this study was to assess the agreement of death information between the Centers for Medicare & Medicaid Services (CMS) Medicare enrolment data and NDI. Using Medicare and NDI data from 1999 through 2016, we identified Medicare beneficiaries who were reported dead in the CMS Medicare enrolment database (EDB) and Common Medicare Environment (CME), linked these beneficiaries to the NDI using CMS Health Insurance Claim number, and compared death dates between the two data sources. To assess agreement between our data sources, we calculated kappa scores; where a kappa of 1 indicates perfect agreement and a kappa of 0 indicates agreement equivalent to chance. We also examined CMS to NDI linkage and death date matching for stability over time. Of the 36 785 640, Medicare beneficiaries reported dead in CMS enrollment data from 1999 to 2016, 97.5% were linked to the NDI. A kappa score of 0.98 showed a near perfect agreement between NDI and CMS reported deaths. The percentage of linked cases exactly matching on death dates increased from 94.8% in 1999 to 99.4% in 2016. Our findings suggest strong concordance between death dates as recorded by CMS enrollment data and the NDI in the entire Medicare population.

On the Databases of Protein Biomarkers of Human Exposure to Environmental Hazards: A Literature Review

Introduction: Both environmental and occupational exposure to hazardous chemicals is a public health challenge since it can induce oxidative stress, lipid peroxidation, and protein modifications. Creation of a database linking work-related risk factors and adverse human health outcomes based on protein biomarkers will contribute to the development of effective preventive and therapeutic approaches. Objective: To conduct a systematic review of literature to identify databases containing information about human protein biomarkers of exposure to environmental risk factors. Materials and methods: We examined Russian and English-language publications containing information about existing protein biomarker databases issued in 2003–2023 and found in Elibary.ru, PubMed, Google Scholar, Scopus, and Research Gate using the following keywords: database, adverse effect, biomarkers, proteins, and occupational environment. Forty of 300 papers initially selected contained systematized data of research aimed at identifying biomarkers of occupational exposures and were therefore chosen for the review. Results: Eight information sources have been found to match our search criteria. We have established that the databases are divided into two types by availability and accessibility of the information of interest. Type I databases contain research findings related to the impact of occupational exposures on the content of biomarkers (proteins) but have limited access. Type II databases are open to access, but they do not contain direct information about protein biomarkers associated with occupational exposures. Conclusion: The existing databases either contain unsystematized data on protein biomarkers of adverse human health effects or are closed to access. Thus, the task of creating such publicly available information sources deems relevant.

Proposed framework for enhancing integrity technique using distributed query operation

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.

Enhancing MongoDB query performance through index optimization

This article delves into the critical aspect of enhancing query performance in MongoDB through meticulous index optimization. It begins with an introduction to MongoDB's unique document-oriented data storage approach and its inherent scalability, which sets the stage for understanding the importance of efficient query processing. The discussion progresses to highlight the pivotal role of indexes in MongoDB, emphasizing their function in expediting data retrieval and the necessity for their optimization to ensure peak database performance. A detailed exploration is provided on the methodologies for identifying fields suitable for indexing, considering factors such as query frequency and the specific use of fields in query operations. The article further elaborates on the selection of optimal index types, tailored to the diverse needs of varying data and query scenarios, thereby underscoring the versatility of MongoDB's indexing capabilities. Management of index size is discussed as a critical component of optimization, addressing the balance between index efficiency and resource consumption. The utilization of MongoDB's query planner is showcased as a powerful tool for achieving an in-depth understanding of query execution and identifying potential optimizations. In conclusion, the article encapsulates the essence of continuous index management and the strategic use of MongoDB's analytical tools to maintain and enhance database performance. It underscores the ongoing nature of optimization efforts required to keep pace with evolving data patterns and application demands, ultimately ensuring a responsive, efficient, and scalable database environment.

The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxaand classifications reconsidered.

UNITE (https://unite.ut.ee) is a web-based database and sequence management environment for molecular identification of eukaryotes. It targets the nuclear ribosomal internal transcribed spacer (ITS) region and offers nearly 10 million such sequences for reference. These are clustered into ∼2.4M species hypotheses (SHs), each assigned a unique digital object identifier (DOI) to promote unambiguous referencing across studies. UNITE users have contributed over 600000 third-party sequence annotations, which are shared with a range of databases and other community resources. Recent improvements facilitate the detection of cross-kingdom biological associations and the integration of undescribed groups of organisms into everyday biological pursuits. Serving as a digital twin for eukaryotic biodiversity and communities worldwide, the latest release of UNITE offers improved avenues for biodiversity discovery, precise taxonomic communicationand integration of biological knowledge across platforms.

Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach

For bionic underwater robots, it is a great challenge for depth control due to model uncertainty and strong nonlinearity. To this end, we propose a data-driven model predictive control (MPC) approach using reinforcement learning (RL) for robotic penguin depth control. First, by imitating the underwater mode of the biological penguin, a robotic prototype with a tendon-driven head, two degrees of freedom wings, and a tendon-driven tail was designed. Then, a data-driven MPC framework is proposed considering the structure and motion properties of the robotic penguin. Specially, a data-based learning environment is constructed using a motion capture system, computational fluid dynamics, and a back-propagation neural network. Meanwhile, to maximize the benefits of the controller while ensuring safety and stability, a data-driven MPC using the RL scheme is applied to approximate the optimal policy. Combined with an appropriate reward design and periodic training, the closed-loop controller performance is significantly improved, and the validity of the proposed framework is finally tested by extensive simulations and experiments. Notably, this work will provide valuable insights into the learning-based motion control of bionic underwater robots.

Database security

In the modern technological tapestry, the security of database systems has burgeoned into a prominent concern for institutional frameworks. This urgency is invigorated by a dual confluence: the shifting industry paradigm which underscores the primacy of expansive data collections, coupled with the proliferation of legislative frameworks that zealously guard the sanctity of individual consumer data. The core aim of this discourse is to furnish a panoramic understanding of indispensable measures to bolster database security, with an amplified emphasis on countering SQL injection threats. The introductory segment delineates essential fortification strategies and succinctly touches upon optimal practices for shaping a database environments network topography and error mitigation methodologies. Subsequent to this panoramic insight, the discourse pivots to spotlight a diverse array of methodologies to discern and neutralize SQL injection forays.

Analysis of Software Aging in a Database Environment

Analysis of Software Aging in a Database Environment

A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

LINKS. A System for Historical Family Reconstruction in the Netherlands

LINKS stands for 'LINKing System for historical family reconstruction' and is a software system to link nominal data from the Dutch archives and ultimately reconstruct historical individuals and families. We present the background and philosophy of this matching system and explain its data structure and functioning. Currently the core data of the LINKS system consists of indexed civil certificates. These certificates are available from 1812 — the start of the Dutch Vital Registration — until the year they are confidential based on privacy laws. For more than 20 years, thousands of volunteers have been working to build this index, which contains not only the names of newborn, married and deceased persons, but also the names of their parents, places of birth, ages and sometimes their occupational titles. The software system LINKS includes the standardization of all input before linking, nominal record linkage procedures and identification of all unique persons involved in the system. All processes are repeatable and a strict distinction is maintained between source data, standardized, linked and enriched data and released data. Moreover, LINKS also informs archives about all kinds of errors and inconsistencies found during the cleaning and matching process. We will discuss two matching systems, the first is the original querying system that runs within a MySQL database environment and the second is a newly developed system, called burgerLinker, which is based on knowledge graphs and which is designed as a system that can be used independently from LINKS and is made available as open source software. Finally, we present the most important releases of LINKS data so far: two national releases that link birth and parental marriage certificates, creating families and pedigrees and an integrated dataset of persons, families and family trees in four provinces.

A combinatorial approach implementing new database structures to facilitate practical data curation management of QTL, association, correlation and heritability data on trait variants

A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as 'trait variants'. This has helped us to streamline the management and curation of such trait information in our database environment. Database URL https://www.animalgenome.org/PGNET/.

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges.

We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WSNs energy efficiently with only a small amount of memory in each sensor node. It uses a synopsis of skyline attribute value ranges, which is a very compact data structure. The range synopsis is used both in the search of anchor points for skyline filtering and in 2-way semijoins for join filtering. We describe the structure of a range synopsis and present our protocol. To optimize our protocol, we solve some optimization problems. Through implementation and a set of detailed simulations, we show the effectiveness of our protocol. The range synopsis is confirmed to be compact enough for our protocol to work well with the limited memory and energy in each sensor node. For the correlated and random distributions, our protocol significantly outperforms other possible protocols, confirming the effectiveness of an in-network skyline as well as the join filtering capabilities of our protocol.

A Data Integrity Verification Scheme for Centralized Database Using Smart Contract and Game Theory

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Heuristic Pairwise Alignment in Database Environments.

Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach.

EP01.04-005 Quantitative Characteristics in Global CT Lung Cancer Screening Populations Using the ELIC Distributed Database and Computation Environment

High resolution CT lung scans contain large amounts of potentially relevant information to evaluate the prospective risk of developing lung cancer. Significant relationships between presence of emphysema and lung cancer risk have been observed, particularly when analyzing high-quality, high-resolution CT scans. A systematic quantitative analysis of lung structure, considering both the shape and density of lungs and lobes of screening detected lung cancer patients and lung cancer free screening participants has the potential to provide insight into promising quantitative lung cancer risk biomarkers.

MA11.07 The ELIC Distributed Database and Computation Environment for Analyses of Lung Cancer Screening LDCTs Across the World

The rapid adoption of LDCT lung cancer screening throughout the world has resulted in a significant number of published reports providing important local findings from lung cancer screening studies. While these reports provide regional study/program performance information, a global resource to facilitate radiomics and AI research is currently not available. We report here on the recently deployed Early Lung Imaging Confederation (ELIC) database and computation infrastructure currently running on the AWS cloud and illustrates how this resource can be used for AI research.

Wildlife Wrangler: A high-level data processing framework that supports the utilization of species occurrence data for biogeographical analyses

Wildlife Wrangler: A high-level data processing framework that supports the utilization of species occurrence data for biogeographical analyses