Conventional Database Systems Research Articles

A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

Vehicle theft is a widespread and costly problem with thousands of cars stolen annually, impacting privacy, the economy, and security. Conventional database systems have proven unreliable, enabling a thriving market for stolen vehicles. In response, our study presents an innovative solution, leveraging the security of blockchain technology. By utilizing the Ethereum Virtual Machine (EVM) and Smart Contracts, our system is designed to thwart attacks, including Distributed Denial of Services (DDoS) attacks. Notably, Near Field Communication (NFC) enables the secure transmission of sensitive data for verification. Our research aims to evaluate the real-world impact of this technology, considering factors such as improvements in data security and the efficiency of the reporting process. One of the most notable outcomes of our proposal is the substantial reduction in reporting time for car theft. While conventional reporting typically takes several hours, our system streamlines the process to less than 1 minute. This innovation offers significant benefits to vehicle owners, law enforcement agencies, and the broader community.

Machine-Learning Approach for Predicting Harmful Diseases using Big Data and IoT: A Review

Abstract: The magnitude of the enormous quantity of data in the healthcare sector is currently on the order of petabytes, and it is expanding exponentially. The massive increase of data has resulted in a number of issues with computing, storage, and transfer. With the aid of a conventional relational database system, this type of data may be evaluated and processed. Moreover, structured data can only be processed by standard database systems. Big data, on the other hand, stores unstructured data. ICT would be able to assist society in a better storage with invention of new and effective mechanisms for the storage and accessing of the information. ICT implementation in the healthcare sector is referred to as eHealth. The processing of data, subsequent analysis, and enhancement of the decision-making processes would all benefit from application in the health care sector in order to gather better treatment options for diseases' symptoms.

Polynomial combined first-order rewritings for linear and guarded existential rules

We consider the problem of ontological query answering, that is, the problem of answering a database query (typically a conjunctive query) in the presence of an ontology. This means that during the query answering process we also need to take into account the knowledge that can be inferred from the given database and ontology. Building, however, ontology-aware database systems from scratch, with sophisticated optimization techniques, is a highly non-trivial task that requires a great engineering effort. Therefore, exploiting conventional database systems is an important route towards efficient ontological query answering. Nevertheless, standard database systems are unaware of ontologies. An approach to ontological query answering that enables the use of standard database systems is the so-called polynomial combined query rewriting, originally introduced in the context of description logics: the conjunctive query q and the ontology Σ are rewritten in polynomial time into a first-order query qΣ (in a database-independent way), while the database D and the ontology Σ are rewritten in polynomial time into a new database DΣ (in a query-independent way), such that the answer to q in the presence of Σ over D coincides with the answer to qΣ over DΣ. The latter can then be computed by exploiting a conventional database system.In this work, we focus on linear and guarded existential rules, which form robust rule-based languages for modeling ontologies, and investigate the limits of polynomial combined query rewriting. In particular, we show that this type of rewriting can be successfully applied to (i) linear existential rules when the rewritten query can use the full power of first-order queries, (ii) linear existential rules when the arity of the underlying schema is fixed and the rewritten query is positive existential, namely it uses only existential quantification, conjunction, and disjunction, and (iii) guarded existential rules when the underlying schema is fixed and the rewritten query is positive existential. We can show that the above results reach the limits (under standard complexity-theoretic assumptions such as ▪) of polynomial combined query rewriting in the case of linear and guarded existential rules.

THE USE OF BLOCKCHAIN IN THE MANAGEMENT OF COVID-19 VACCINE DATA

The ongoing COVID-19 pandemic has disrupted nearly every sector of the world economy. The recently discovered vaccine has promised a return to normalcy. Since traditional database storage systems can be tampered with quickly, the incorporation of blockchain would preclude the limitations of conventional database systems. This paper thus discusses the use of blockchain technology in managing the COVID-19 vaccine data to ensure credibility, safety, security, and transparency.

Big Data Security Challenges

Big data is usually defined as a huge volume of data that is constantly growing in real time and is difficult to store, retrieve and manage using common database techniques. Traditional technology areas are being transformed by big data technologies, and their effective use will require new security models and security design methodologies to address new security challenges. Big data is a collection of huge volumes of data that can be divided into several categories of structured and unstructured data. The article begins with a definition of big data and then looks at characteristics such as veracity, volume, variety, and dynamics that have the greatest impact on big data security. This huge volume of data cannot be captured, stored or analyzed by conventional database systems. Big data continues to grow along with the growth of the Internet. Big data analytics provide organizations such as government and businesses with new tools to analyze unstructured data. Privacy and security of big data are attracting more and more attention. In this phase of computing, where we are moving to zeta bytes from Giga/Tera/Peta/Exabytes, the risks have simultaneously increased. Next, we discuss several potential strategies and tactics for protecting the privacy and security of big data

Research on the Multidimensional Data Cube Method of the Quantum Science Experiment Satellite

Scientific experiment satellite should be target-oriented, which requires several mission teams to develop a phase experiment plan and take operations according to the actual situation. These experiment plans depend on the data generated by mission operation and are integrated from the strategy set derived from some data analysis of each subsystem of the scientific satellite mission. Along with the operation of QUESS on orbit, a large amount of operational data will be generated. To solve this problem, current mainstream approach mainly relies on log statistics and data statistics of conventional database systems. However, these methods consume more energy and time, and require more professional skills of analysts, which cannot meet the requirements of multi-angle and multi-granularity research and judgment tasks. Moreover, the scalability of the methods is very poor. When the observation angle of the problem changes, it is often necessary to reorganize the statistical analysis of the data. In view of the above situations, a multi-dimensional data modeling and analysis method based on data cube is proposed, which can face different topics and support multi-level, multi-angle and multi-granularity statistical analysis of data, providing good support for decision makers.

Correlational Analysis of the Availability and Usage of Geographic Information Systems by Students and Lecturers an Higher Educational Institutions

There has been a rapid development of Information and communication Technologies (ICTs) globally, and this includes the Geographic Information Systems (GISs) which has evolved over the years and aimed at solving many geographic problems as fast and efficiently as possible. The importance of GIS to students and lecturers in higher educational institutions is it’s ability to easily analyse locations together with the conventional database systems. This paper assessed the correlation between the availability and use of GIS in Higher Educational Institutions (HEIs). The study is exploratory in nature and employed the use of questionnaires as instrument to collect data. Simple random sampling technique was used to select one hundred and ten (110) lecturers one thousand five hundred and seventy-nine (1579) students from a university. Findings revealed that GIS for analysis is still at its lowest ebb and hence calls for an action to motivate or encourage lecturers and students to use it. Though there are indications that GIS will be of great value in schools, students and lecturers they will be happy to use it for their teaching and learning. The study also revealed availability of GIS was not tied to its usage.

Conventional database management systems

Before the advent of Database Management Systems, computer programmers or developers spent a lot of time writing codes to create and manage databases also known as data files before venturing into the actual programming work, thereby making it possible for only trained computer professionals to operate and manipulate databases or data files. It left the programmers who are targeting high performance application to deal with first data integrity and security challenges, inefficient process to access and manipulate stored data, data recovery challenges in the case of crashes and many other challenges. In this paper, we present a review of the traditional database management system (DBMS).First; we gave a detailed explanation of the meaning, types and uses of a database management system. We highlighted the features of Conventional Database Management Systems and the challenges it faces today especially in the handling of high-profile data for many enterprise systems, hardware requirements, human resources, network challenges and costs are also discussed in this research. Finally, we propose a solution to the challenges facing the conventional database system. From our findings, we discovered that the Big Data technology is a potential solution to the limitations of the conventional database management systems. This research will be beneficial to scholars and researchers who are interested in Database management systems.

Detección de anomalías en grandes volúmenes de datos

El desarrollo de la era digital ha traído como consecuencia un incremento considerable de los volúmenes de datos. A estos grandes volúmenes de datos se les ha denominado big data ya que exceden la capacidad de procesamiento de sistemas de bases de datos convencionales. Diversos sectores consideran varias oportunidades y aplicaciones en la detección de anomalías en problemas de big data. Para realizar este tipo de análisis puede resultar muy útil el empleo de técnicas de minería de datos porque permiten extraer patrones y relaciones desde grandes cantidades de datos. El procesamiento y análisis de estos volúmenes de datos, necesitan de herramientas capaces de procesarlos como Apache Spark y Hadoop. Estas herramientas no cuentan con algoritmos específicos para la detección de anomalías. El objetivo del trabajo es presentar un nuevo algoritmo para la detección de anomalías basado en vecindad para de problemas big data. A partir de un estudio comparativo se seleccionó el algoritmo KNNW por sus resultados, con el fin de diseñar una variante big data. La implementación del algoritmo big data se realizó en la herramienta Apache Spark, utilizando el paradigma de programación paralela MapReduce. Posteriormente se realizaron diferentes experimentos para analizar el comportamiento del algoritmo con distintas configuraciones. Dentro de los experimentos se compararon los tiempos de ejecución y calidad de los resultados entre la variante secuencial y la variante big data. La variante big data obtuvo mejores resultados con diferencia significativa. Logrando que la variante big data, KNNW-BigData, pueda procesar grandes volúmenes de datos.

FUZZY RECURSIVE RELATIONSHIPS IN RELATIONAL DATABASES

Recursive relationships are used for modelling problems coming from the real life, such as, for example, a relationship describing formal dependencies between employees of an enterprise, where creation of work groups and teams requires analysis of many elements. In conventional database systems, the precision of data is assumed. If our knowledge of the fragment of reality to be modelled is imperfect one should apply tools for describing uncertain or imprecise information. One of them is the fuzzy set theory. The paper deals with recursive relationships in fuzzy databases. The analysis is performed with the use of the theory of interval-valued fuzzy sets. A definition of a fuzzy interval recursive relationship has been presented. The paper defines different connections of entities which participate in such relationships. Op-erations of the extended relational algebra are also discussed.

PHP120 - Use of Clinical Intelligence in Monitoring Rational Drug Use Indicators in Thailand

To apply clinical intelligence (CI) technique in monitoring the rational drug use (RDU) indicators and discuss its benefits and obstacles in Thailand. A new technique, CI, was applied in the hospital database system by health analytical pharmacists to generate routine RDU indicators on website by QlikView® version 11.20.12664.0. The previous system which needed clinical pharmacists manually collected data from patient’s medical records combined with usable information from the conventional database system, and analyzed by Microsoft Excel®, was then compared in term of pharmacyworkload and time consume. Pharmacy workload was calculated by Thai standard full time equivalence (FTE). Since CI has launched in 2015, it automatically generate 24 from 24 indicators (accounted for 100 percent) in routine. The tool can reduce clinical pharmacists workload by 0.5 FTEs per month or 580 USD (1 pharmacist FTE = 1,160 USD). In term of time consume, although the first run was 2 days, the following runs were automated from retrieving data from database system to generate reports on hospital website would take not more than 5 minutes/run. Clinical intelligence (CI) was the suitable technique to generate reports which produced from huge and complicate data system. It would reduce the workload of clinical pharmacists; however, to implement CI in hospital system, it needed investment and staff training.

Apache Flink in current research

Abstract Recent trends in data collection and the decreasing prices of storage result in constantly growing amounts of analyzable data. These masses of data cannot easily be processed by traditional database systems as these do not allow for a sufficient degree of scalability. Programs especially designed for parallel data analysis on large-scale distributed systems are required. Developing such programs on clusters of commodity hardware is a complex challenge for even the most experienced system developers. Frameworks such as Apache Hadoop are scalable, but – when compared to SQL – extremely hard to program. The open-source platform Apache Flink is a link between conventional database systems and big data analysis frameworks. Flink is based on a fault tolerant runtime for data stream processing, which manages the distribution of data as well as communications within the cluster. A high diversity of use cases can be supported through various interfaces that allow for the implementation of data analysis processes. In this paper, we present an overview of Apache Flink as well as some current research activities on top of the Apache Flink ecosystem.

Applying cognitive load theory to the redesign of a conventional database systems course

Cognitive load theory (CLT) was used to redesign a Database Systems course for Information Technology students. The redesign was intended to address poor student performance and low satisfaction, and to provide a more relevant foundation in database design and use for subsequent studies and industry. The original course followed the conventional structure for a database course, covering database design first, then database development. Analysis showed the conventional course content was appropriate but the instructional materials used were too complex, especially for novice students. The redesign of instructional materials applied CLT to remove split attention and redundancy effects, to provide suitable worked examples and sub-goals, and included an extensive re-sequencing of content. The approach was primarily directed towards mid- to lower performing students and results showed a significant improvement for this cohort with the exam failure rate reducing by 34% after the redesign on identical final exams. Student satisfaction also increased and feedback from subsequent study was very positive. The application of CLT to the design of instructional materials is discussed for delivery of technical courses.

Challenges and Techniques for Testing of Big Data

Big Data, the new buzz word in the industry, is data that exceeds the processing and analytic capacity of conventional database systems within the time necessary to make them useful. With multiple data stores in abundant formats, billions of rows of data with hundreds of millions of data combinations and the urgent need of making best possible decisions, the challenge is big and the solution bigger, Big Data. Comes with it, new advances in computing technology together with its high performance analytics for simpler and faster processing of only relevant data to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on complex data to continuously drive innovation and make the best possible decisions. While Big Data provides solutions to complex business problems like analyzing larger volumes of data than was previously possible to drive more precise answers, analyzing data in motion to capture opportunities that were previously lost, it poses bigger challenges in testing these scenarios. Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization. One of the most challenging things for a tester is to keep pace with changing dynamics of the industry. While on most aspects of testing, the tester need not know the technical details behind the scene however this is where testing Big Data Technology is so different. A tester not only needs to be strong on testing fundamentals but also has to be equally aware of minute details in the architecture of the database designs to analyze several performance bottlenecks and other issues. Like in the example quoted above on In-Memory databases, a tester would need to know how the operating systems allocate and de-allocate memory and understand how much memory is being used at any given time. So, concluding, as the data- analytics Industry evolves further we would see the IT Testing Services getting closely aligned with the Database Engineering and the industry would need more skilled testing professional in this domain to grab the new opportunities.

Accelerated join evaluation in Semantic Web databases by using FPGAs

SummaryWhile the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and cannot meet these performance requirements anymore. In recent years, systems with many processing cores are considered to satisfy these demands. Furthermore, these systems include more and more heterogeneous cores tailor‐made to solve one specific task in an efficient manner. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. Thus, the challenge is orchestrating the diversity of the functionality of all the cores to be optimized for performance/energy efficiency. In this paper, a concept is introduced on how to develop a flexible Field‐Programmable Gate Arrays (FPGA)‐based hardware accelerator to improve the performance of query evaluation in a Semantic Web database. As a first step to the hardware/software system, several joint algorithms are implemented on an FPGA and evaluated against a well‐developed software solution (implemented in C). The comparison shows a significant speedup of up to 10 times. Because of the complexity of the join operator, it is promising that the overall performance of query evaluation can be further enhanced by processing whole queries on an FPGA. Copyright © 2015 John Wiley & Sons, Ltd.

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Cloud computing enables a conventional relational database system's hardware to be adjusted dynamically according to query workload, performance and deadline constraints. One can rent a large amount of resources for a short duration in order to run complex queries efficiently on large-scale data with virtual machine clusters. Complex queries usually contain common subexpressions, either in a single query or among multiple queries that are submitted as a batch. The common subexpressions scan the same relations, compute the same tasks (join, sort, etc.), and/or ship the same data among virtual computers. The total time spent for the queries can be reduced by executing these common tasks only once. In this study, we build and use efficient sets of query execution plans to reduce the total execution time. This is an NP-Hard problem therefore, a set of robust heuristic algorithms, Branch-and-Bound, Genetic, Hill Climbing, and Hybrid Genetic-Hill Climbing, are proposed to find (near-) optimal query execution plans and maximize the benefits. The optimization time of each algorithm for identifying the query execution plans and the quality of these plans are analyzed by extensive experiments.

Column Level Uni -Temporal Data

The basic paradigm of conventional database systems is based on the current valid data processing. However, today's database systems should provide also management for historical and future valid data. Standard temporal model, using object level, stores the whole object data after the insert or update operation, although only some attributes changed their values. This paper deals with the principle of temporal data modelling based on the column level, not the whole object, describes the structure and principles of required methods, procedures, functions and triggers to provide functionality of the system. All designed and implemented solutions are compared with the existing solutions.

Big Data and Ethics Review for Health Systems Research in LMICs: Understanding Risk, Uncertainty and Ignorance—And Catching the Black Swans?

“Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn't fit the strictures of your database architectures. To gain v...

A Materialized View for the Same Generation Query in Deductive Databases

Traditionally, deductive databases are designed as extensions to relational databases by either integrating a logic programming language, such as PROLOG, with a conventional relational database system that provides storage persistence needed for any database system, or by integrating an expert system with a relational database system. Deductive databases take advantage of a special kind of rule recursion called linear recursion to provide inference capabilities to improve the intelligence of the database system. The simplicity of implementation of linear recursive rules, like same generation rules, is far from the difficulty and the cost of computing the results of queries based on these recursive rules. Thus, to reduce costs and improve performance of the same generation queries, many techniques were suggested. In this paper, we propose the use of materialized views to speed up the evaluation of these queries and explain how to maintain the materialized view if the underlying base relation is updated. Finally, simulations are used to compare the materialized view approach with other approaches that are used to compute the results of the same generation queries.

Design and development of algorithms for identifying termination of triggers in active databases

An active database system is a conventional database system extended with a facility for managing triggers (or active rules). Active database systems can react to the occurrence of some predefined events automatically. In many applications, active rules may interact in complex and sometimes unpredictable ways, thus possibly yielding infinite rule executions by triggering each other indefinitely causing non-termination. Almost all of the work to date on termination analysis for active databases relies on checking that a directed graph called the |triggering graph| for a set of rules is acyclic. This paper presents new algorithms for detecting termination/non-termination of rule execution using triggering graph. These algorithms detect termination/non-termination among the rules in a triggering graph without any limitation on the number of rules.