Intelligent Database System Research Articles

Database-Driven Adaptive Learning: A Systematic Analysis of AI Integration in Educational Personalization

This article examines the transformative role of AI-driven databases in facilitating personalized learning experiences within educational institutions. Through a systematic analysis of implementations across K-12 and higher education settings, we investigate how intelligent database systems enable adaptive learning pathways and real-time intervention strategies. The article synthesizes data from 47 educational institutions implementing AI-enhanced learning management systems over a three-year period (2021-2024), employing a mixed-methods approach to evaluate both technical integration patterns and learning outcomes. Results demonstrate significant improvements in student engagement (mean increase of 27%, p < .001) and academic performance (average GPA increase of 0.4 points) when compared to traditional learning management systems. The article also reveals key implementation challenges, including data standardization issues and faculty adaptation requirements. The findings provide a comprehensive framework for educational institutions seeking to implement AI-driven database systems, while highlighting critical considerations for scalability, privacy, and pedagogical integration. This article contributes to the growing body of literature on educational technology by establishing empirical evidence for the effectiveness of AI-powered personalization in educational contexts and proposing a structured approach to its implementation.

Toward Improving the Quality of Mutation Operator and Test Case Effectiveness in Higher-Order Mutation Testing

Currently, there are many research studies that apply and improve mutation testing techniques including traditional mutation testing or first-order mutation testing, and higher-order mutation testing (HOMT) for evaluating the quality of the set of test data in particular, and the quality of test suites in general. The results of those studies have proven the effectiveness of mutation testing in the field of software testing. Mutation testing allows the quality of test cases to be automatically evaluated, thereby helping the testers to improve the quality in the design and execution of the software testing. Besides, these studies have also pointed out the main barriers in applying mutation testing techniques in practice. However, we are the first to introduce a method that can reduce the cost, but keep the quality of testing activity based on evaluating the quality of the mutation operator as well as the quality of the test cases. In this paper, we concentrate on two problems regarding higher-order mutation testing: Evaluating the quality of mutation operators as well as generated mutants and prioritizing test cases based upon its capability of killing mutants. This may help developers allocate suitably their resources during testing phase. The study of this paper is an extended version of our previous study titled “Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing”, which is published in the proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 (V. N. Do, Q. V. Nguyen and T. B. Nguyen, Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing, in Intelligent Information and Database Systems. ACIIDS 2021, eds. N. T. Nguyen, S. Chittayasothorn, D. Niyato and B. Trawiński. Lecture Notes in Computer Science, Vol. 12672 (Springer, Cham), https://doi.org/10.1007/978-3-030-73280-6_66 ) to confirm the usefulness of our proposed method.

Method to Haulage Path Estimation and Road-Quality Assessment Using Inertial Sensors on LHD Machines

For many years now, the mining industry has seen a boost in exploring and developing the systems for monitoring operational parameters of mining machines, in particular load-haul-dump machines. Therefore, further researches on algorithmics have also advanced dynamically regarding effective performance management as well as predictive maintenance. Nonetheless, the issue of road conditions is still being neglected. That issue has a substantial impact on both the overall operator’s convenience, their performance, and machinery reliability, especially its construction node and tire damages. Moreover, such negligence pertains also to the maintenance of mine infrastructure, including the network of passages. The paper explains the use of the portable inertial measurement unit (IMU) in evaluating road conditions in the deep underground mine. The detailed descriptions of the road quality classification procedure and bump detection have been included. The paper outlines the basic method of tracking the motion trajectory of vehicles and suggests the method of visualization of the results of the road conditions evaluation. This paper covers the sample results collected by the measurements unit in the deep underground mine during six experiments. This paper is an extended version of a paper presented at the ACIIDs 2020 conference [P. Stefaniak, D. Gawelski, S. Anufriiev and P. Śliwiński, Road-quality classification and motion tracking with inertial sensors in the deep underground mine, Asian Conference on Intelligent Information and Database Systems, March 2020, Springer, Singapore, pp. 168–178].

Ontology Learning from Relational Database: Opportunities for Semantic Information Integration

Along with the rapidly growing scale of relational database (RDB), how to construct domain-related ontologies from various databases effectively and efficiently has been a bottleneck of the ontology-based integration. The traditional methods for constructing ontology from RDB are mainly based on the manual mapping and transformation, which not only requires a lot of human experience but also easily leads to the semantic loss during the transformation. Ontology learning from RDB is a new paradigm to (semi-)automatically construct ontologies from RDB by borrowing the techniques of machine learning, it provides potential opportunities for integrating heterogeneous data from various data sources efficiently. This paper surveys the recent methods and tools of the ontology learning from RDB, and highlights the potential opportunities and challenges of using ontology learning in semantic information integration. Initially, the previous surveys on the topic of the ontology-based integration and ontology learning were summarized, and then the limitations of previous surveys were identified and analyzed. Furthermore, the methods and techniques of ontology learning from RDB were investigated by classifying into three categories: reverse engineering, mapping, and machine learning. Accordingly, the opportunities and possibility of using ontology learning from RDB in semantic information integration were discussed based on the mapping results between the bottlenecks of ontology-based integration and the features of ontology learning. a a This paper is an extension of our prior work [C. Ma and B. Molnár, Use of ontology learning in information system integration: A literature survey, in Intelligent Information and Database Systems, eds. P. Sitek, M. Pietranik, M. Krótkiewicz and C. Srinilta, Communications in Computer and Information Science, Vol. 1178 (Springer, Singapore, 2020), pp. 342–353]

Validating Knowledge Contents with Blockchain-Assisted Gamified Crowdsourcing

This paper presents the use of gamified crowdsourcing for knowledge content validation. Constructing a high-quality knowledge base is crucial for building an intelligent system. We develop a refinement process for the knowledge base of our word retrieval assistant system, where each piece of knowledge is represented as a triple. To validate triples acquired from various sources, we introduce yes/no quizzes and present them to many casual users for their inputs. Only the triples voted “yes” by a sufficient number of users are incorporated into the main knowledge base. Users are incentivized by rewards based on their contribution to the validation process. To ensure transparency of the reward-giving process, blockchain is utilized to store logs of the users’ inputs from which the rewards are calculated. Different strategies are also proposed for selecting the next quiz. The simulation results indicate that the proposed approach has the potential to validate knowledge contents. This paper is a revised version of our conference paper presented at the 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020).

Optimizing predictability of rating scales by differential evolution for the use by collective intelligent information and database systems

Optimizing predictability of rating scales by differential evolution for the use by collective intelligent information and database systems

Operative control of ore quality at the input of ore preparation operations of enrichment fabric

ABSTRACTThis article describes the solution of the problem of determining the ore belonging to a particular quarry in the multi-flow technological process of ore crushing at the ore enrichment fabric of a mining and processing plant. The paper proposes a new temporal model for solving this problem and describes its implementation in the form of an online monitoring system of ore quality and it belonging to a concrete quarry.This publication is an expanded version of the publication on The 10th Asian Conference on Intelligent Information and Database Systems (ACIIDS) 19–21 March 2018, Dong Hoi City, Vietnam (Toporov, Axelrod, & Tukeyev, 2018). The added material concerns issues related to the following aspects:– increased visibility of the object of monitoring of ore-preparation operations of concentrating mills, where, as a rule, ore of various quarries is processed;– detailing the functional structure of the automated system for operational monitoring of characteristics of processed ores;– an assessment of the adequacy of the operational model of the input characteristics of the processed ores and the adaptation of its parameters.

An algorithmic approach to estimate cognitive aesthetics of images relative to ground truth of human psychology through a large user study

ABSTRACTThis research introduces a learning model that estimates the cognitive perception of aesthetics. Taking psychology into account, this bridges the gap between human and machine. The goal is to build a machine-learning model that can estimate beauty in images perceived by human eyes. We have summand our research [Firoze, A., Osman, T., Psyche, S. S., & Rahman, R. M. (2018). Scoring photographic rule of thirds in a large MIRFLICKR dataset: A showdown between machine perception and human perception of image aesthetics. Asian Conference on Intelligent Information and Database Systems (pp. 466–475), Springer; Osman, T., Psyche, S. S., Deb, T., Firoze, A., & Rahman, R. M. (2018). Differential color harmony: A robust approach for extracting Harmonic Color features and perceive aesthetics in a large dataset. International Conference on Big Data and Cloud Computing, Springer] together with the idea of humans’ personal preferences and achieved higher than state of the art performances. An extensive user study (374 participants) has been conducted to support claims. Several photographical compositional metrics have been used. Colour gradient, rule of thirds and human subject’s psychology has been picked as features. The consideration of user’s perspective or psychology is one of the key contributions of this research.

Fuzz-TIME: an intelligent system for managing fuzzy temporal information

PurposeTime modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is subjective and fuzzy. The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals.Design/methodology/approachOn the one hand, fuzzy extensions of Allen temporal relations are investigated and, on the other hand, extended temporal relations to define the positions of two fuzzy time intervals are introduced. Then, a database system, called Fuzzy Temporal Information Management and Exploitation (Fuzz-TIME), is developed for the purpose of processing fuzzy temporal queries.FindingsTo evaluate the proposal, the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose. Some demonstrative scenarios from history domain are proposed and discussed.Research limitations/implicationsThe authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system. However, thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed (Fuzz-TIME) can have many practical applications where time information has to be dealt with. In particular, in several real-world applications like history, medicine, criminal and financial domains, where time is often perceived or expressed in an imprecise/fuzzy manner.Social implicationsThe social implications of this work can be expected, more particularly, in two domains: in the museum to manage, exploit and analysis the piece of information related to archives and historic data; and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases.Originality/valueThis paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.

An intelligent multimedia information system for multimodal content extraction and querying

This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused to increase the accuracy of the object extraction process. The semantic contents that are extracted using the information fusion are stored in an intelligent and fuzzy object-oriented database system. In order to answer user queries efficiently, a multidimensional indexing mechanism that combines the extracted high-level semantic information with the low-level video features is developed. The proposed multimedia information system is implemented as a prototype and its performance is evaluated using news video datasets for answering content and concept-based queries considering all these modalities and their fused data. The performance results show that the developed multimedia information system is robust and scalable for large scale multimedia applications.

Collective intelligent information and database systems

Collective intelligent information and database systems

Development of an Intelligent Database System to Automate the Recognition of Machining Features from a Solid Model Using Graph Theory

Development of an Intelligent Database System to Automate the Recognition of Machining Features from a Solid Model Using Graph Theory

Research and development of intelligent cutting database cloud platform system

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system.

Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application

Spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems and environmental data management systems. For database researchers, modeling and designing a database of fuzzy spatiotemporal data and querying such a database efficiently have been challenging issues due to complex spatial features and uncertainty involved. This paper presents an integrated approach to modeling, indexing, and efficiently querying spatiotemporal data related to fuzzy spatial and complex objects and spatial relations. As our case study, we design and implement a meteorological database application that involves fuzzy spatial and complex objects, and a spatiotemporal index structure, and supports various types of spatial queries including fuzzy spatiotemporal queries. Our implementation is based on an intelligent database system architecture that combines a fuzzy object-oriented database with a fuzzy knowledge base.

Keynote II

Prof. Habibollah Haron is one of Professor in Faculty of Computing, University of Teknologi Malaysia. He received Bachelor of Computer Science from University of Teknologi Malaysia in 1989, Master of Science in Computer Technology in Manufacture, from University of Sussex & University of Brighton United Kingdom in 1995 and Doctor of Philosophy in Computer Science especially in Computer Aided Geometric Design (CAGD) area from University of Teknologi Malaysia in 2004. He has research experience in image processing, artificial intelligence, computer vision and Computer Aided Geometric Design. Currently, he is a senior member of IEEE, Information Visualization Society, United Kingdom and Persatuan Sains Matematik Malaysia (Malaysian Society of Sciences and Mathematics) (PERSAMA). He has received some awards such as Most Active Reviewers of International Arab Journal of Information Technology (IAJIT) During the Year 2007, 2008, 2009, 2010, 2011 and 2012, ODA Shared Scholarship Scheme for one year of M.Sc. course at the University of Sussex, UK by Association of Commonwealth Universities, in October 1994 and IDB Training Scheme Award under Development and Industrial Technology Program (Asian) Development Bank (PPTP/ADB) in May – July 1994 (3 months) at Dept. of Electronic Engineering, University of Hull, UK. He is also the organizing chair of the 5th Asian Conference on Intelligent Information and Database Systems Conference (ACIIDS 2013) and the 13th International Conference Intelligent Software Methodologies, Tools and Techniques (SOMET 2014). From 2004 until 2014 he served as Head of Computer Science Department and currently as Deputy Dean (Academic) in Faculty of Computing, University of Teknologi Malaysia.

Agent-based modelling of the evacuation of endangered areas in crisis situations

The evacuation process from endangered areas (EA) into safe spaces in crisis situations is modelled by means of simple agents (gate-ways equipped by sensors). Petri nets (PN) are utilized here to model the EA structure as well as the agents and their cooperation. More precisely, timed PN (TPN) and first-order hybrid PN (FOHPN) are used to do this. Rooms, other spaces to be evacuated (corridors) and the safe spaces out of EA (where people are evacuated) are modelled by TPN places and FOHPN continuous places. Gate-ways are modelled by TPN subnets and by FOHPN continuous transitions. While the supervisor for the TPN gate-ways can be synthesized by means of place/transition PN (P/T PN), the blocks of FOHPN discrete places and transitions are used to affect the gate-ways. Depending on the immediate throughput of the gate-ways the escape time behaviour is found in the process of simulation. This paper is the extended version of the paper (Capkovia? Intelligent Information and Database Systems. LNAI. Springer, Cham Heidelberg 2014) presented in the ACIIDS 2014 conference.

Fuzzy Expert System for the Competitiveness Evaluation of Shipbuilding Companies

The world's shipbuilding industry as well as shipping industry is going through some unprecedented recession period in recent years. Shipbuilding companies in China have suffered a lot and many are now focusing on improving their competitiveness to survive the recession. The evaluation of a company's competitiveness is difficult considering the high level of uncertainty involved. This paper is a preliminary attempt to evaluate the competitiveness of Chinese shipbuilding companies using Fuzzy Expert System. An example system is presented in this paper which has shown its adoptability. With a larger and better intelligent database system established in the future, more accurate and reliable results can be achieved, which could help the practitioners in the shipbuilding industry to better understand and predict their companies' competitiveness.

Mining FDA drug labels for medical conditions

BackgroundCincinnati Children’s Hospital Medical Center (CCHMC) has built the initial Natural Language Processing (NLP) component to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration’s (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task.MethodsThis paper discusses a hybrid NLP system, called AutoMCExtractor, to collect medical conditions (including disease/disorder and sign/symptom) from drug labels published by the FDA. Altogether, 6,611 medical conditions in a manually-annotated gold standard were used for the system evaluation. The pre-processing step extracted the plain text from XML file and detected eight related LOINC sections (e.g. Adverse Reactions, Warnings and Precautions) for medical condition extraction. Conditional Random Fields (CRF) classifiers, trained on token, linguistic, and semantic features, were then used for medical condition extraction. Lastly, dictionary-based post-processing corrected boundary-detection errors of the CRF step. We evaluated the AutoMCExtractor on manually-annotated FDA drug labels and report the results on both token and span levels.ResultsPrecision, recall, and F-measure were 0.90, 0.81, and 0.85, respectively, for the span level exact match; for the token-level evaluation, precision, recall, and F-measure were 0.92, 0.73, and 0.82, respectively.ConclusionsThe results demonstrate that (1) medical conditions can be extracted from FDA drug labels with high performance; and (2) it is feasible to develop a framework for an intelligent database system.

The use of virtual reality and intelligent database systems for procedure planning, visualisation, and real-time component tracking in remote handling operations

The organisation of remote handling (RH) operations in fusion environments is increasingly critical as the number of tasks, components and tooling that RH operations teams must deal with inexorably rises. During the recent JET EP1 RH shutdown the existing virtual reality (VR) and procedural database systems proved essential for visualisation and tracking of operations, particularly due to the increasing complexity of remote tasks. A new task planning system for RH operations is in development, and is expected to be ready for use during the next major shutdown, planned for 2009. The system will make use of information available from the remote operations procedures, the RH equipment human–machine interfaces, the on-line RH equipment control systems and also the virtual reality (VR) system to establish a complete database for the location of plant items and RH equipment as RH operations progress. It is intended that the system be used during both preparation and implementation of shutdowns. In the preparations phase the system can be used to validate procedures and overall logistics by allowing an operator to increment through each operation step and to use the VR system to visualise the location and status of all components, manipulators and RH tools. During task development the RH operations engineers can plan and visualise movement of components and tooling to examine handling concepts and establish storage requirements. In the implementation of operations the daily work schedules information will be integrated with the RH operations procedures tracking records to enable the VR system to provide a visual representation of the status of remote operations in real time. Monitoring of the usage history of items will allow estimates of radiation dosage and contaminant exposure to be made. This paper describes the overall aims, structure and use of the system, discusses its application to JET and also considers potential future developments.

Intelligent high-speed cutting database system development

In this paper, the components of a high-speed cutting system are analyzed firstly. The component variables of the high-speed cutting system are classified into four types: uncontrolled variables, process variables, control variables, and output variables. The relationships and interactions of these variables are discussed. Then, by analyzing and comparing intelligent reasoning methods frequently used, the hybrid reasoning is employed to build the high-speed cutting database system. Then, the data structures of high-speed cutting case base and databases are determined. Finally, the component parts and working process of the high-speed cutting database system on the basis of hybrid reasoning are presented.