Graph-based data management system for efficient information storage, retrieval and processing

Aldwairi, Monther; Jarrah, Moath; Mahasneh, Naseem; Al-khateeb, Baghdad

doi:10.1016/j.ipm.2022.103165

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…The obtained result is further compared with the RDF4j system. The result of the model indicates that the proposed Neo4j-based data storing and retrieving techniques outperform others regarding security and flexibility [7].…”

Section: Literature Reviewmentioning

confidence: 94%

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

subramanian,

Ammunuel

et al. 2023

Preprint

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In the rapidly evolving field of education, the need for a semantic search engine to efficiently retrieve graph-based data is crucial. Universities and colleges generate vast amounts of educational content research articles, and having a semantic search engine can enhance the accuracy of search results, ensuring that students and staff can access the right information effectively. It's imperative to develop an AI-based smart recommendation system equipped with semantic search capabilities to optimize information retrieval and provide accurate mapping for students or scholars. Such a system can revolutionize how educational content is accessed, offering personalized recommendations that align with the individual learning objectives of students and scholars, thus improving their educational experience. KMSBOT is an innovative academic recommendation system designed to enhance the efficiency of existing models. It effectively summarizes academic data and provides tailored information for students, research scholars, and educational faculty. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses the limitations of traditional recommendation systems and contributes to improving user satisfaction and engagement in academic environments.

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Section: Literature Reviewmentioning

confidence: 94%

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

subramanian,

Ammunuel

et al. 2023

Preprint

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“…m represents the total count of neurons in the result level. The relationship between learning speed and the total number of IoT devices is shown in Equation (1).…”

Section: Intelligent Management Systemmentioning

confidence: 99%

“…DBMS have been fundamental in organizing and retrieving information for years (Aldwairi et al, 2023). The birth of this is traced back to the 1960s when hierarchical and network models were introduced, establishing the basis for organized data storage and retrieval (Feng et al, 2021).…”

Section: Introduction To Database Management System and Solutionsmentioning

confidence: 99%

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Clavijo-López,

Navarrete,

Velásquez

et al. 2024

JISIS

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Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

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“…Skema representasi data memainkan peran penting dalam bagaimana data disimpan, yang memengaruhi efisiensi pemrosesan dan pengambilannya. Desain komponen sistem mewujudkan konsep rekayasa perangkat lunak untuk mengaktifkan metrik kinerja seperti skalabilitas, efisiensi, fleksibilitas, pemeliharaan, dan perluasan [8].…”

Section: Pendahuluanunclassified

Rancangan Sistem Informasi Pencatatan dan Pendataan Kesehatan Pegawai di Lingkungan Pemerintah Provinsi Bali

Karmana

Makful²,

Prabawa³

2023

JSAI

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Kesibukan dan beban kerja yang tinggi serta kesadaran yang rendah pada usia produktif merupakan faktor penyebab pekerja atau pegawai sering mengabaikan kesehatannya. Perubahan cepat dalam sistem pelayanan kesehatan di seluruh dunia dimungkinkan oleh kemajuan Informasi. Tujuan penelitian ini yaitu membangun sistem informasi pencatatan dan pendataan kesehatan pegawai ASN dan non ASN di lingkungan pemerintah daerah provinsi bali. Manfaat penelitian ini yaitu terbentuknya perancangan sistem sebagai dasar membuat aplikasi informasi mengenai pencatatan/pendataan Kesehatan pada pegawai ASN dan Non ASN di lingkungan pemerintah provinsi Bali. Sistem informasi pencatatan/pendataan Kesehatan tersebut nantinya dapat digunakan sebagai aplikasi yang dapat digunakan oleh Dinas Kesehatan Provinsi Bali untuk memantau status Kesehatan pegawai dan secara tidak lansung dapat digunakan sebagai pendorong pegawai agar dapat melakukan pemeriksaan kesehatan secara rutin sebagai upaya pencegahan dan deteksi dini suatu penyakit sehingga dapat meningkatkan derajad Kesehatan pegawai ASN dan Non ASN di lingkungan pemerintah provinsi Bali.

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Graph-based data management system for efficient information storage, retrieval and processing

Cited by 9 publications

References 27 publications

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Rancangan Sistem Informasi Pencatatan dan Pendataan Kesehatan Pegawai di Lingkungan Pemerintah Provinsi Bali

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