Schema Management Research Articles

Implementing Secure and Scalable APIs in Java Spring Boot: RESTful vs. GraphQL Services

As modern web applications continue to evolve, secure and scalable APIs have become essential building blocks for dynamic, data-driven platforms. Java Spring Boot, renowned for its developer-friendly approach and extensive ecosystem serves as a common foundation for implementing microservice APIs. Two prominent architectural styles, RESTful and GraphQL, each offer distinct approaches to request handling, data fetching, and schema management in a microservices or distributed environment. This paper presents an extensive study of REST vs. GraphQL for developing secure and scalable APIs in Spring Boot, focusing on performance, data modeling, ease of adoption, and real-world pitfalls. We examine security best practices covering authentication, authorization, and data encryption while highlighting scaling techniques such as load balancing, container orchestration, and caching. Through diagrams, code snippets, and references to real-world scenarios, we provide a practical guide for architects and developers aiming to choose the right approach. In doing so, we also discuss known anti-patterns that undermine security or hamper throughput, and propose best practices to ensure robust, future-proof APIs. Keywords Java Spring Boot, REST, GraphQL, Microservices, Security, Scalability, API Design, Authentication, Authorization, Cloud-Native

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

IoT sensor data retrieval and analysis through cloud environment for effective power management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Other newly created techniques, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. In this study, the researchers look at the four systems mentioned above to identify their shared functions and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cloud service would have been used as the foundation technology for the proposed schema management platform, collecting the information generated by each sensor in the smart factory. It was easy to manage and manipulate remotely using IoT technology and a mobile device with a network connection. Here, to enhance safety of the entire building by installing multiple IoT devices in a smart building. The proposed systems were designed in a simulated Cooja simulator environment. The proposed approaches to the collection and manipulation of sensor information in smart cities can help us create a green, resource-efficient project.

SkiQL: A unified schema query language

Most NoSQL systems are schema-on-read: data can be stored without first having to declare a schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data change frequently. However, freeing from declaring schemas does not mean their absence, but rather that they are implicit in data and code. Therefore, diagramming tools similar to those available for relational systems are also needed to help developers and administrators to design and to understand NoSQL schemas.Visualizing diagrams is not practical if schemas contain hundreds of database entities, so exploration or query facilities are then needed. In schemaless NoSQL stores, data of the same entity can be stored with different structure (e.g., non-uniform types and optional fields), which can increase the difficulty of having readable diagrams.NoSQL schema management tools should therefore have three main components: schema extraction, schema visualization, and schema query. As there are four main NoSQL data models, it is convenient for such tools to be built on a generic data model so that they provide platform-independence (of data models and data stores) to query and visualize schemas. With the aim of favoring the creation of generic database tools, the authors of this paper defined the U-Schema unified data model that integrates the four main NoSQL data models as well as the relational model.This paper is focused on querying NoSQL and relational schemas which are represented as U-Schema models. We present the SkiQL language designed on U-Schema to achieve a platform-independent schema query service. SkiQL provides two constructs: schema-query and relationship-query. The former allows to obtain information of entity or relationship types, and the latter that of the aggregations or references (relations among types). We will show how SkiQL was evaluated by calculating well-known metrics for languages as well as using a survey with developers with experience in NoSQL.

The relationship between medical comorbidities and health-related quality of life among adults with type 2 diabetes: The experience of different hospitals in southern Bangladesh.

Health-related quality of life (HRQoL) is a critical determinant to assess the severity of chronic diseases like diabetes mellitus. It has a close association with complications, comorbidities, and medical aid. This study aimed to estimate the prevalence of medical comorbidities and determine the relationship between comorbidities and HRQoL among type 2 diabetic patients of southern Bangladesh. This study was a cross-sectional study conducted through face to face interviews using a pre-tested structured questionnaire and by reviewing patient's health records with prior written consent. The study was conducted on 2,136 patients with type 2 diabetes attending five hospitals of Chattogram, Bangladesh, during the tenure of November 2018 to July 2019. Quality of life was measured using the widely-used index of EQ-5D that considers 243 different health states and uses a scale in which 0 indicates a health state equivalent to death and 1 indicates perfect health status. The five dimensions of the quality index included mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. Patients with three comorbidities and with four or more comorbidities had a higher probability of reporting "extreme problem" or "some problem" in all five dimensions of the EQ-5D index compared with those without comorbidity (Odds ratio: mobility, 3.99 [2.72-5.87], 6.22 [3.80-10.19]; usual activity, 2.67 [1.76-4.06], 5.43 [3.28-8.98]; self-care, 2.60 [1.65-4.10], 3.95 [2.33-6.69]; pain or discomfort, 2.22 [1.48-3.33], 3.44 [1.83-6.45]; anxiety or depression, 1.75 [1.07-2.88], 2.45 [1.19-5.04]). The number of comorbidities had a negative impact on quality of life. Prevalent comorbidities were found to be the significant underlying cause of declined HRQoL. To raise diabetes awareness and for better disease management, the exposition of comorbidities in regards to HRQoL of people with diabetes should be considered for type 2 diabetes management schemas.

Аналітичне сховище для великих потокових даних

A concept for organizing an analytical data warehouse has been developed, which includes a method of interaction between data producers and a repository, a method of data circuit control, a method of data streaming, a method of storing initial data, a method of data processing and a method of providing secure data access. Other concepts on the market are discussed, namely: SDLF as the leading standard recommended by AWS, IronSource DL using Upsolver, SimilarWeb DL using Upsolver. A comparative analysis was conducted (mostly with SDLF, as its implementation is open, and the implementation by private companies is hidden). The advantages of the proposed concept over the existing ones are examined in detail. Recommendations on how to integrate the concept with data schema control applications are given. A service for streaming data using Apache Beam in Java has been developed. A repository architecture for analytics was designed and developed. A data schema management model was developed as well as a data schema management model and a model for secure access to data. The research that has been conducted can be improved by the experience of implementing the concept in business, as well as by collecting and systematizing knowledge about other standards that will be created.

MIDS Level 1: Specification, conformance checklist, mapping template and instructions for use

Approved formally as a TDWG Task Group (TG) in September 2020, TG MIDS is working to harmonise a framework for "Minimum Information about a Digital Specimen (MIDS)". MIDS clarifies what is meant by different levels of digitization (MIDS levels) and specifies the minimum information to be captured at each level. Capturing and presenting data in future digitization in standard formats is essential so that data can be more easily understood, compared, analysed and communicated via the Internet. Adopting MIDS and working to achieve specific MIDS levels in digitization ensures that enough data are captured, curated and published such that they are useful for the widest possible range of future research, teaching and learning purposes. Adopting MIDS makes it easier to consistently measure the extent of digitization achieved over time and to set priorities for the remaining work. In the year since MIDS was first introduced at TDWG 2020, the TG has focussed on the details of MIDS level 1, representing the basic minimum level of information to be expected and captured in basic digitization activities such as creating a catalogue record and (optionally) making photographic or other digital images of specimens. To help the community adopt and embed MIDS conformance as a core part of digitization and data publishing/management pipelines, the MIDS specification consists of definitions of the expected information elements, a template for mapping terms/fields in institutional collection management systems and other data management schemas to those information elements, a conformance proforma allowing declaration of how a digitization or data publishing event conforms to MIDs, and instructions for use.

Cross-Cultural Learning in Gameplay: BAFÁ BAFÁ, Persuasive Technology, and the Exploration of Intercultural Sensitivity

Background. Serious games have the potential to provide benefits that beyonds entertainment value. Recent growth in the empirical research of serious games shows their abilities to deliver prosocial messages. Aim. This study aims to examine the use of games in the management of cultural schemas as well as moderating intercultural sensitivity and intercultural communication competence. Moreover, this study explores how and why simulation games, such as BAFÁ BAFÁ, function as persuasive technologies (PTs) effectively motivating individuals into behavior change. Method. An experimental study with three conditions (game, reading, and control) was conducted to examine the effects of BAFÁ BAFÁ on enhancing participants’ intercultural sensitivity as well as communication competence compared to reading intercultural materials or regular activity. Results. Both intercultural sensitivity and communication competence were measured with multi-dimensional scales, and the results of the multi-variate analyses indicated that the intercultural simulation game BAFÁ BAFÁ can facilitate the increase of participants’ intercultural sensitivity and communication competence.

Digital Literacy of Employees in Production Process - Analyse of Production Stability and Productivity in INDUSTRY 4.0 Concept

Important part of the higher productivity and effectiveness in today's industrial enterprises confronted with growing automation and digitization of production processes is strong connected with the ability of employees to be prepare communicate with newest digitized production technologies. Skills and abilities of employees influence radical the productivity, performance, value added of production flows. Presented paper analyse important parameters of digital literacy of employees in production processes and confronts the results with the core requirements on continuously process performance and improvement. Results achieved by presented analyse are useful for development of digitized process management schemas, prediction of effective value stream in production process.

A critique of imbalanced data learning approaches for big data analytics

Biomedical research becomes reliant on multi-disciplinary, multi-institutional collaboration, and data sharing is becoming increasingly important for researchers to reuse experiments, pool expertise and validate approaches. However, there are many hurdles for data sharing, including the unwillingness to share, lack of flexible data model for providing context information for shared data, difficulty to share syntactically and semantically consistent data across distributed institutions, and expensive cost to provide tools to share the data. In our work, we develop a web-based collaborative biomedical data sharing platform SciPort to support biomedical data sharing across distributed organisations. SciPort provides a generic metadata model for researchers to flexibly customise and organise the data. To enable convenient data sharing, SciPort provides a central server-based data sharing architecture, where data can be shared by one click through publishing metadata to the central server. To enable consistent data sharing, SciPort provides collaborative distributed schema management across distributed sites. To enable semantic consistency for data sharing, SciPort provides semantic tagging through controlled vocabularies. SciPort is lightweight and can be easily deployed for building data sharing communities for biomedical research.

SQL is No Excuse to Avoid DevOps

Using SQL databases is not an impediment to doing DevOps. Automating schema management and a little developer discipline enables more vigorous and repeatable testing, shorter release cycles, and reduced business risk. When you can confidently deploy new releases, you do it more frequently. New features that previously sat unreleased for weeks or months now reach users sooner. Bugs are fixed faster. Security holes are closed sooner. It enables the company to provide better value to customers.

Efficient Resource Consumption by Dynamic Clustering and Optimized Routes in Wireless Sensor Networks

The energy issue is an important parameter in the wireless sensor networks and should be managed in the different applications. We propose a new routing algorithm that it is energy efficient and uses different approaches as dynamic clustering, spanning tree, self-configurable routing and controls energy consuming by data-driven and power management schemas. It has two main phases. The first is consisting of the steady cluster, cluster head election and creation-spanning tree in each cluster and the second phase is data transmission. The proposed protocol is compared with four other protocols in network lifetime, network balance, and average packet delay and packet delivery. Simulation results show the proposed protocol performance in the network lifetime is about 6 per cent higher than Improved-LEACH, 21.5 per cent higher than EESR and 5.8 per cent higher than DHCO. Its improvement in packet delivery parameter is about 3.5 per cent higher than Improved-LEACH, 6.5 per cent higher than EESR and 3 per cent higher than DHCO. In addition, the performance or in packet delay is about 17 per cent higher than EESR and 6 per cent higher than DHCO but Improved-LEACH protocol has a good performance than our protocol about 4 per cent.

Source Routing Over Protocol-Oblivious Forwarding for Named Data Networking

Named data networking (NDN) has promising advantages, however, it is hard to deploy because of the lack of special hardware to support it. Besides, the lack of specific routing schema also restrains the advantages of NDN. To solve the problems of deployment and routing, we come up with the source routing schema over protocol-oblivious forwarding (POF) for NDN. POF is able to directly carry NDN packets without over IP because it provides an adaptable and programmable forwarding plane by defining a generic flow instruction set. Source routing is a flexible and scalable routing schema and provides the flexible and efficient implementation for the future network. The centralized management of the source routing schema over POF can provide the following properties: (i) quickly responding to the change of network, and (ii) forwarding the Interest packet to the best cache node. Experiments show that the source routing schema over POF for NDN can reduce more than 50% of the networking traffic flow and increase the cache hit rate by up to 25% more than the baseline schemas. Thus, the source routing schema over POF could be an implementation plan for NDN.

Resumable online index rebuild in SQL server

Azure SQL Database and the upcoming release of SQL Server enhance Online Index Rebuild to provide fault-tolerance and allow index rebuild operations to resume after a system failure or a user-initiated pause. SQL Server is the first commercial DBMS to support pause and resume functionality for index rebuilds. This is achieved by splitting the operation into incremental units of work and persisting the required state so that it can be resumed later with minimal loss of progress. At the same time, the proposed technology minimizes the log space required for the operation to succeed, making it possible to rebuild large indexes using only a small, constant amount of log space. These capabilities are critical to guarantee the reliability of these operations in an environment where a) the database sizes are increasing at a much faster pace compared to the available hardware, b) system failures are frequent in Cloud architectures using commodity hardware, c) software upgrades and other maintenance tasks are automatically handled by the Cloud platforms, introducing further unexpected failures for the users and d) most modern applications need to be available 24/7 and have very tight maintenance windows. This paper describes the design of "Resumable Online Index Rebuild" and discusses how this technology can be extended to cover more schema management operations in the future.

A Critique of Imbalanced Data Learning Approaches for Big Data Analytics

Biomedical research becomes reliant on multi-disciplinary, multi-institutional collaboration, and data sharing is becoming increasingly important for researchers to reuse experiments, pool expertise and validate approaches. However, there are many hurdles for data sharing, including the unwillingness to share, lack of flexible data model for providing context information for shared data, difficulty to share syntactically and semantically consistent data across distributed institutions, and expensive cost to provide tools to share the data. In our work, we develop a web-based collaborative biomedical data sharing platform SciPort to support biomedical data sharing across distributed organisations. SciPort provides a generic metadata model for researchers to flexibly customise and organise the data. To enable convenient data sharing, SciPort provides a central server-based data sharing architecture, where data can be shared by one click through publishing metadata to the central server. To enable consistent data sharing, SciPort provides collaborative distributed schema management across distributed sites. To enable semantic consistency for data sharing, SciPort provides semantic tagging through controlled vocabularies. SciPort is lightweight and can be easily deployed for building data sharing communities for biomedical research.

Neuronal Modulation in the Prefrontal Cortex in a Transitive Inference Task: Evidence of Neuronal Correlates of Mental Schema Management.

In cognitive neuroscience, it is unknown how information that leads to inferential deductions are encoded and manipulated at the neuronal level. We addressed this question by recording single-unit activity from the dorsolateral prefrontal cortex of monkeys that were performing a transitive inference (TI) task. The TI required one to choose the higher ranked of two items, based on previous, indirect experience. Our results demonstrated that single-neuron activity supports the construction of an abstract, mental schema of ordered items in solving the task and that this representation is independent of the reward value that is experienced for the single items. These findings identify the neural substrates of abstract mental representations that support inferential thinking.

Schema management for document stores

Document stores that provide the efficiency of a schema-less interface are widely used by developers in mobile and cloud applications. However, the simplicity developers achieved controversially leads to complexity for data management due to lack of a schema. In this paper, we present a schema management framework for document stores. This framework discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. The major technical challenge comes from varied structures of records caused by the schema-less data model and schema evolution. In the discovery phase, we apply a canonical form based method and propose an algorithm based on equivalent sub-trees to group equivalent schemas efficiently. Together with the algorithm, we propose a new data structure, eSiBu-Tree, to store schemas and support queries. In order to present a single summarized representation for heterogenous schemas in records, we introduce the concept of "skeleton", and propose to use it as a relaxed form of the schema, which captures a small set of core attributes. Finally, extensive experiments based on real data sets demonstrate the efficiency of our proposed schema discovery algorithms, and practical use cases in real-world data exploration and integration scenarios are presented to illustrate the effectiveness of using skeletons in these applications.

Networking Control Education by the use of Software Defined Networking Tools and Techniques

Software Defined Networking (SDN) is an emerging field of study within computer networks research area that will, in a near future, play an important role in the design and development of network infrastructures. The main idea behind SDN is to separate the control and the data planes that are nowadays coupled in the traditional network devices (routers, switches, etc). This separation of the control plane from the electronic that performs the actual packet switching tasks allows the former to be implemented in a physical different device and also permits to build the control strategies in a vendor and platform independent fashion, what helps to unify the different operating systems that today make it difficult to configure a complex network. The data plane (packet switching) is performed in physical devices but the control plane is implemented entirely by software, in devices that are called controllers. Controllers communicate control schemes to the packet switching devices by means of different protocols and APIs and can also communicate and interact with higher level applications that can be developed for different tasks: from easing the management of control schemas to monitoring the network status or, as is the case in this paper, for educational purposes. The aim of the work described in this paper is to show the possibilities of using software applications embedded in SDN controllers as an Internet-based educational tools in the field of computer network control.

Experimental Evaluation of NoSQL Databases

Relational databases are a technology used universally that enables storage, management and retrieval of varied data schemas. However, execution of requests can become a lengthy and inefficient process for some large databases. Moreover, storing large amounts of data requires servers with larger capacities and scalability capabilities. Relational databases have limitations to deal with scalability for large volumes of data. On the other hand, non-relational database technologies, also known as NoSQL, were developed to better meet the needs of key-value storage of large amounts of records. But there is a large amount of NoSQL candidates, and most have not been compared thoroughly yet. The purpose of this paper is to compare different NoSQL databases, to evaluate their performance according to the typical use for storing and retrieving data. We tested 10 NoSQL databases with Yahoo! Cloud Serving Benchmark using a mix of operations to better understand the capability of non-relational databases for handling different requests, and to understand how performance is affected by each database type and their internal mechanisms.