With the rise of big data, numerous large-scale applications have shifted away from relational databases (RDB) to NoSQL stores owing to enhanced performance and flexibility. However, migrating from RDB to NoSQL stores involves tedious schema transformation. This is because existing NoSQL schema design approaches mostly rely on rules of thumb or guidelines for manually choosing a good schema. We have proposed a novel workload-driven and service level agreement (SLA)-aware model for relational to document store schema transformation. The proposed model is simultaneously automatic, workload-driven, and SLA-aware. The proposed model has three phases: model input, midway transformations, and model output. The proposed model begins with an extended entity-relationship schema along with workload information and SLA specifications as inputs. The paper proposes two algorithms for the midway transformations phase: (i) entity and relationship transformations, and (ii) workload-driven and SLA-aware refinements and transformations. The third phase includes generating the document store schema as model output. To validate the effectiveness of our research, we conducted an experimental evaluation using a case study in the e-commerce sector. The performance of the proposed model (P) is compared with the four existing workload-driven models, namely, GAF(G), UAF (U), QPG (Q), and Hypergraph (H), for relational to document store schema transformation. Our results show that the proposed model consistently improves query execution time, reduces read and write latency, and enhances aggregation pipeline performance. Finally, our results illustrate that our proposed model requires less storage space, is highly scalable, and demonstrates improved throughput and latency compared to existing models.

Diabetes Mellitus is a critical disease that affects a lot of people. Improving health quality for patients is an issue every country should care about. Diabetes Mellitus is triggered by obesity, old age, lack of exercise and various other factors. Several ML algorithms are used to make prior predictions of diabetes diagnosis in the human body for maximum accuracy by building models from patient data to predict their outcomes more accurately. Method: This study used ensemble approaches and some ML classification algorithms to predict diabetes in the dataset. The precision of the models varies compared to other models. Result: This paper suggests the model can predict diabetes from the test set with 96% precision with logistic regression, 84% precision with SVM, 84% precision with random forest classifier, 84% precision with Gaussian Naïve Bayes, 78% precision with K-Nearest Neighbors classifier, and 98.8% precision with AdaBoost Classifier. Machine learning is a fast-paced field of research and development for prediction and is an effective detection system.

The advancement of digital health solutions has led to the development and widespread adoption of EHR standards like FHIR and openEHR. However, as these standards and protocols grow, so too do the challenges they pose. Electronic health record system data is deeply nested and highly interconnected, posing a unique and complicated challenge. These challenges frequently require technical skills to address the deeply nested and complex data, that are short in supply. EHRInsight overcomes this challenge by offering the ability to transform complex and intricate electronic health record data into a more usable information. EHRInsight is interlinked and self-correcting across three core modules: the Query-Generation Agent, the Validation Agent, and the Visualization Agent. Using LLMs and Knowledge Graphs, we help generate more usable data in the form of visualizations that help make complex EHR data more intuitive. EHRInsight uses Neo4j Graph Database to semantically represent FHIR data, a Qdrant vector store to keep records of queries and responses as embeddings, and a HAPI FHIR server to store and validate resources. The heart of EHRInsight is that these were connected and integrated via LangGraph to access seamless functionality. The applications is assessed using a collection of bespoke queries designed to test the efficiency of the Query-Generation Agent across dimensions of the Gemini model. In particular, the evaluation of the queries focused on a number of metrics that were aggregated to obtain a macro-averaged score. The results from the experiment have shown that there is considerable improvement in performance in multiple Gemini models. In particular, the Gemini 2.5 flash model shows balance in performance metrics since precision, recall, F1 score and accuracy are 0.88 on average. In contrast, Gemini 2.5 Pro displays almost perfect performance where precision, recall, F1 score and accuracy are 0.99, 0.98 and 0.99 respectively. The results on the Visualization Agent that involve a collection of categorized challenges confirm that the agent provides a correct and meaningful visualization of the EHR data.

Health is a primary concern for humans, and good air quality enables to live healthier. The Air Quality Index (AQI) was developed to better understand the air quality situation, which comprises parameters such as particulate matter and harmful gases. Currently several AQI monitoring systems are available to measure or forecast AQI. After a survey of existing system designs, two significant challenges were identified while developing an AQI monitoring system: first, making it low-cost, and second, achieving better efficiency in terms of experiencing lesser missing values. Addressing first challenge, two low-cost programming boards were used to sense and communicate AQI data over a cloud database; however, the setup experienced some delay in serial communication among the programming boards, resulting in missing values. To minimise missing values (the second challenge), low-cost AQI monitoring system design with a delay synchronization (LcDs) algorithm has been proposed to measure AQI values. The LcDs comprises a data acquisition unit to collect sensor data of AQI, a data processing and communication unit to calibrate and apply the proposed delay synchronization algorithm, and a data visualization unit to see the results in real-time. The algorithm synchronizes the communication between two programming boards and sends the data over the cloud data repository. On average, the efficiency of receiving correct sensor data was increased from 85.7% to 96.28% with the proposed delay synchronization algorithm. The proposed low-cost AQI setup design will further help disseminate AQI data from various locations in smart cities to monitor air quality and attain sustainability.

The current digital ecosystem produces a vast volume of data, which is generated across various domains, including astronomy, healthcare, e-commerce, social media, over-the-top content platforms, etc. In many healthcare institutions and organizations, a tremendous amount of diverse and heterogeneous data is generated annually. These data comprise a wide and diverse variety of electronic and paper-based medical records in structured, semi-structured, and unstructured formats. This paper presents a comprehensive study and rigorous analysis of polyglot persistence-based systems that leverage multiple datastores to integrate, query, and store diverse and heterogeneous data across various domains, including an in-depth examination of datasets, approaches, procedures, results, and challenges. To unify these diverse data horizons and enable functional synergy, this paper proposes a polyglot persistence-based system to demonstrate healthcare information on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) dataset, motivated by the need to combine the strengths of both relational databases and NoSQL data stores. The system comprises a user interface, a security layer, a mediator, and multiple datastores. The snippets of the implementation have been presented, and the results demonstrate effective data unification of tabular, document, and key-value data, as well as the vast capability of querying within a single system framework.

Electronic Health Records (EHRs) are integral to modern healthcare, capturing vast amounts of patient data. The ORBDA OpenEHR dataset conforms to the openEHR specifications that is a standard framework for managing and navigating complex healthcare records. However, the deeply hierarchical and relational nature of EHR data presents significant challenges in querying and analyzing the data. Existing querying tools require in-depth technical knowledge, making them unsuitable for semi-skilled users. This study addresses these challenges by developing a high-level, user-friendly querying system for EHR data. The system leverages a graphical query interface to simplify query construction and enable non-technical users to interact with the data efficiently. The architecture consists of a React-based frontend for visual query building, a Node.js/Express backend for processing and transforming query inputs, and a MongoDB database that stores the flattened EHR data. The EHR Flattener module plays a central role by converting deeply nested openEHR JSON structures into a simplified flat format suitable for aggregation and filtering. The Aggregation Engine dynamically constructs MongoDB aggregation pipelines based on user inputs, and the Query Explanation module provides plain language summaries of the queries. This layered design improves query performance, ensures scalability, and enhances usability for users with limited technical expertise.

In the era of smart sensors and connected devices, distributed computing has become crucial for efficient data management and real-time decision-making also Distributed computing transforms IoT with real-time, scalable, and efficient solutions. The paper explores the distributed computing paradigms of Edge, Fog, and Cloud computing, emphasizing their role in enabling seamless data transfer, scalability, and energy-efficient operations in IoT environments. It addresses accurate and efficient data processing, the suitability of paradigms for real-world use, and the role of communication protocols in secure, reliable data exchange. The study presents a comparative analysis of distributed paradigms, highlighting their frameworks, challenges, and practical use cases. It also examines issues like heterogeneity, accuracy, scalability, and energy efficiency, offering insights to enhance IoT ecosystems with efficient, scalable, and reliable sensor-driven applications.

Ensuring privacy in data publishing is crucial for adhering to regulations. This paper presents a practical implementation of the l-diversity privacy model, which builds on k-anonymity to prevent attacks that reveal sensitive attributes. We developed a Python-based tool, QuasiSecure with a simple graphical interface that allows users to: (1) upload datasets, (2) select quasi-identifiers and sensitive attributes, and (3) evaluate compliance with l-diversity. Our tests on synthetic and MIMIC-III healthcare data show that l-diversity effectively combats homogeneity attacks when configured correctly, although issues persist with skewed data distributions. The tool achieves 89% compliance at l=2 while maintaining data utility, offering a practical solution for organizations working with sensitive data. Complaince reduced to 75% and 70% later as the value of l was changed to 3 and 4, respectively, maintaining the data utility. Shannon entropy was used as a measure to check for information loss post anonymization.

Precise segmentation of brain tumors in MRI scans is essential for accurate diagnosis, treatment planning, and monitoring of disease progression. To overcome challenges related to both data security and segmentation accuracy, this study introduces a novel framework that integrates blockchain technology with machine learning. We propose an enhanced architecture that combines a modified Edge U-Net model augmented with Edge Guidance Blocks (EGBs) and LeakyReLU activation functions for improved feature extraction and boundary localization with a blockchain-based data management system. The framework operates on multimodal MRI data securely stored and verified through a blockchain platform, ensuring data integrity, provenance, and tamper resistance. To further optimize performance, a Lightweight Proof-of-Work (LPoW) consensus algorithm is introduced, significantly reducing block processing time and communication overhead compared to traditional consensus mechanisms. Experimental evaluations confirm that the proposed approach outperforms existing state-of-the-art models, achieving 99.71

Almost all human endeavors in the era of the digital revolution, from commercial and industrial processes to scientific and medical research, depend on the use of ever-increasing amounts of data. However, this humungous data and its complexity make data exploration and querying challenging even for experts. This led to the demand for easy access to data, even for naive users, all the more evident. Considering this, the database community has tilted toward NoSQL Data stores. While there has been much study on query formulation assistance for NoSQL data stores, many users still want help when specifying complex queries (such as aggregation pipeline queries), which require an in-depth understanding of the data storage architecture of a specific NoSQL data store. To help users perform interactive browsing and navigation in NoSQL data stores (MongoDB), this paper proposes a novel, simple, and user-friendly interface, MTable, that provides users with a presentation-level interactive view. This view compactly presents the query results from multiple embedded documents within a single tabular format compared to MongoDB's find operation, which always returns the main document. A certain cell of the MTable contains clickable hyperlinks for users to interact directly with the data persisted in the document stores. This helps the users to incrementally construct complex queries and navigate the document stores without worrying about the tedious task of writing complex queries. In a user study, participants performed various querying tasks faster with MTable than with the traditional querying mechanism. MTable has received positive subjective feedback as well.

The Internet of Things (IoT) is an application domain that integrates different technological and social fields. Air pollution is one of the significant issues in a country like India, and it can be controlled by periodically monitoring the level of pollutant gases. Sensing air quality is a primary concern for environmental monitoring. The Air Quality Index (AQI) is used to measure air pollution. Nowadays, AQI data is generated from various sources, but utilizing that fruitfully for the betterment of society has become complex. Previous research related to AQI monitoring consists of different sensor-based protocol-based parameters, and various prediction models were designed for AQI prediction. It lacks an In-depth analysis of AQI data as per the dimensions like time and weekday. The current paper lists the primary pollutants, such as PM2.5, PM10, SO2, NO2, CO, NH3, O3, Pb, and their primary sources. It further surveys available air quality indexing systems. A four-dimensional prescriptive analysis framework RSTMAQI (AQI affected by Rainfall, Seasons, Time, and Months) has been proposed, which consists of data acquisition, data processing, and data visualization followed by identification of data as per the variation of these dimensions on the AQI. Data acquisition has been made from government repositories1,2,3 via web scrapping; this investigation focuses on India's 15 most polluted cities from 01 January 2020 to 30 June 2021, showcasing that rainy days are best and winter seasons are the worst in the air quality. The further 4:00 AM- 7:00 AM records lesser AQI, and Sundays experience the best air quality. Further, the research is enhanced by a proposed algorithm for improving the forecasting AQI mechanism, which includes conditions like rainfall and day to provide accurate AQI on a desired day. The accuracy of the predicted AQI value has increased by 37

The current study explores methodologies (like TLD, CLDA, and Kuzera) and the challenges of migrating schemas from RDBMS to NoSQL document stores. These challenges include data misinterpretations, increased response time due to costly joins, poor data locality, and unalignment of schema design with query patterns. To encounter these challenges, the proposed model presents a schema transformation approach from a Relational Database Management System (RDBMS) to NoSQL using Hypergraph. The existing methodologies use a graph data model that cannot represent complex relationships, whereas the Hypergraph provides accurate group-wise complex relationships. The paper contains four phases: input, data modeling decision, transformation, and output phase. The input phase contains relational tables and workload queries. The data modeling decision phase incorporates the calculation of normalized weight followed by the decision to select the type of data model (embedding or referencing). The role of the transformation phase is to generate a collection of tables that reflect the table hierarchy, utilized to create the NoSQL schema. The transformation phase comprises hypergraph generation, matrix transformation, and matrix enumeration. The hypergraph generation phase includes query mapping, inverse query mapping, and hypergraph generation. The matrix transformation phase covers the creation of the incidence matrix and square matrix, which are used for matrix enumeration. The incidence matrix shows which queries are related to which tables. The square matrix represents the frequency and strength of these relationships. Matrix enumeration is used to finalize the grouping of related tables based on query patterns, optimizing the schema for better performance by reducing the need for complex joins. In the output phase, the final NoSQL schema is created by using the results of the preceding phases (data modeling decision phase and transformation phase). An experiment has been conducted on the TPC-H benchmark and NoSQL document-oriented datasets. The proposed model is compared with existing models (TLD, CLDA, and Kuszera), where it outperforms TLD with 6.67

The rapid expansion of healthcare data necessitates scalable, interoperable, and privacy-preserving mobile data management solutions. MedEForm addresses this by integrating the openEHR standard with Google Firebase to deliver a real-time, cloud-based electronic health record (EHR) platform optimized for Android devices. Utilizing Archetype Definition Language (ADL) models from the openEHR Clinical Knowledge Manager (CKM), MedEForm features a GUI generator that dynamically renders context-aware data-entry forms client-side, eliminating the need for app updates. Authentication is securely managed via Firebase Authentication, while encrypted user credentials and EHR data are stored in Firestore, Firebase’s NoSQL document store. A dedicated query module enables role-based execution of single-patient, multi-patient, and cohort-level queries across a 90,000-instance dataset. Firestore’s real-time synchronization, offline support, and fine-grained security rules enforce low-latency access with robust access control. For epidemiological analysis, MedEForm implements anonymization of demographic, clinical, and geospatial data at weekly intervals, storing these de-identified records in a separate Firestore collection. This architecture supports high-quality analytics while upholding stringent privacy guarantees. By unifying open standards, dynamic interface generation, and secure cloud infrastructure, MedEForm offers a modular and interoperable framework for mobile health data collection and analysis.

The traditional relational database faces many difficulties, such as consistency and integrity issues, high read and write costs, performance degradation with large datasets, and complex queries in managing the data of today's world. The root cause of this scenario is the explosion of diverse data due to the advancements in the Internet of Things (IoT), Mobile devices, Social Networking, and Web Technology Applications. These data not only introduce costly joins, which lead to an increase in response time, but also add the risk of data redundancy. Unalignment of schema design with the query patterns is also a significant issue due to incorrect schema mapping. To overcome these challenges, many industries migrate their data from relational database systems to NoSQL document stores, which is a complex task. Such migration can be achieved through schema transformation using the three approaches: Complete Referencing, Total Embedding, and Hybrid. This paper showcases an overview of these three approaches by providing a comprehensive comparison and analytical experiments considering the workload queries to lay out a detailed description of each approach to focus on their unique properties and use cases for schema migration from relational to NoSQL stores. The results show that the hybrid approach performs better in average query execution time. In the case of overall space consumption, the total embedding approach consumes 21% less space than the hybrid approach and 31% less space than the complete referencing approach in the 10 Megabyte dataset, and for the 100 Megabyte dataset, the total embedding takes 11% and 29% less space than the hybrid and complete referencing approaches. In contrast, complete referencing shows the least data duplication in terms of redundancy. However, the hybrid approach balances the issues of data updation, high space consumption, and scalability by embedding frequently accessed data and referencing large data to limit space consumption. Thus, summarizes the potential of each approach to provide valuable insights by evaluating analytical workloads for migrating from relational to NoSQL stores.

In this technological era, the technology of databases is very essential to many aspects of modern life. To give the prospective medical practitioner, the finest in class and most recent medical knowledge, it seems mandatory that education in the health domain be well-integrated with the most recent databases. This is because there is a growing demand for it and there are benefits from the collaboration of healthrelated issues of the public and database technology. Database technology can help improve health in several ways, including connecting geographically separated health providers and patients, collecting data for research studies like drug and vaccine trials, keeping track of chronic diseases, and guaranteeing that patients follow their prescribed treatments. In this pandemic situation of COVID-19, which the whole world is currently suffering, the current paper attempts to emphasize the databases’ role. It illustrates how the COVID-19 Dataset can be stored, queried, and analyzed, and helps in providing decision support to various end-users. We have performed descriptive analysis by executing specific queries on the COVID-19 Dataset. Then, we performed predictive analysis using two data analysis techniques on the COVID-19 Dataset to approximate the situation in some major cities of India. Further, we have visualized our results to get valuable information from our analysis.

In today’s life, humans compromise with nature while evolving into a more advanced species. One of the main effects of that advancement is air pollution. Air pollution seriously threatens human health, the environment, and the general quality of life worldwide. A quantitative analysis of air quality was the purpose of developing the Air Quality Index (AQI), an indexing approach. The air quality index is computed using measurements for particulate matter (PM), PM2.5, PM10, NO2, CO, CO2, NH3, and other contaminants. Agnihotra (Yagya) is a method of environmental purification mentioned in the Hindu sculpture (The Book Yagya Vimarsh, written by Dr. Ramprakash, talks about the air pollution reduction method). This paper discusses the Vayu Vishleshan framework for monitoring air quality in the presence of Agnihotra (Yagya). This study is divided into two processes. The first one is AQI monitoring, and the second is AQI reduction. AQI Monitoring covers sensing particulate matter (PM), PM2.5, PM10, NO2, and CO and performing analysis over the AQI data before, during, and following Agnihotra (Yagya). According to analysis, there is an approximate 6–7

The blockchain-enabled healthcare system assists in tackling the inherent issues of centralized healthcare systems, such as data stewardship, single point of failure, and data integrity. However, existing blockchain-enabled healthcare systems suffer from high-energy consumption, low transaction throughput, and poor scalability. This work proposes the healthcare framework ParallelChain to tackle the low scalability and high-energy consumption issue. ParallelChain divides the network into clusters, enabling linear scalability of nodes. The parallel execution of transactions in each cluster improves the throughput. The leader selection process of ParallelChain does not require solving computer-intensive puzzles, making it more energy efficient than proof-of-work. The performance of ParallelChain is validated using metrics such as data transfer, processing time, consensus delay, block congestion, and message exchange. It shows that the amount of data transmitted is 4.6 times more in Bitcoin than ParallelChain with varying nodes. The number of messages exchanged is reduced by 48%, and the processing time is reduced by 2.17 and 1.7 times for varying nodes and block sizes, respectively.

This article presents a Blockchain-based data-sharing approach that offers reliability, integrity, and decentralization properties of Blockchain, making the system user-controllable. It contains the evolution of the healthcare industry with respect to other industries. We also discuss the impact of industry 5.0 technologies and Blockchain-enabled healthcare. The proposed system stores medical data off-chain with IPFS, and the reference of off-chain data is stored on Blockchain. Information flow is set up with the help of smart contracts deployed over Blockchain to ensure immutability and traceability. A prototype has been simulated on the Rinkeby test network using a Proof-of-Work consensus algorithm for various parameters such as upload time, retrieval time, and gas consumed for transaction execution.

In the ever-evolving healthcare landscape, integrating knowledge-based systems into data querying processes is becoming imperative. The existing challenges in querying healthcare data lie in the complexity of extracting meaningful insights from vast and heterogeneous datasets. EHRs store different forms of data, and query systems’ scalability and performance, especially considering the increasing volume of EHR data, are the main challenges faced. To overcome these challenges, the paper proposes a system with a user-friendly graphical interface for creating Archetype Query Language (AQL) queries in openEHR systems. It consists of three components: User Interface, which allows the user to specify query parameters, modify EHRs paths, filter data, and customize query results; Query builder, which creates the AQL query based on input from the User Interface and Repository of Documents where the compositions are stored and the query result obtained from this component is sent back to User Interface. It stands out with its innovative approach, systematically extracting openEHR schemas and simplifying the creation of complex AQL queries. The system’s effectiveness and user satisfaction make learning, using, and developing queries for graph-driven healthcare data knowledge easy. The system enhances the overall functionality and usability of the query builder within the system. It offers a pathway to improved clinical decision-making and patient care outcomes.