Intelligent Database Management System Research Articles

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

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.

Introduction to the Special Issue on Distributed Computing with Applications in Bioengineering

Biomedical Engineering, usually known as Bioengineering, is among the fastest developing and one of most important interdisciplinary fields today. It connects natural and technical sciences, for all of which biological and medical phenomena, computation, and data management play important roles in science and industry. Distributed computing and parallel algorithms have proved to be effective in solving the problem with high computational complexity in a wide range of domains, including areas of computational bioengineering. This special edition collects high-quality research papers in the field of application of distributed computing systems in bioengineering applications. This issue is the achievement of the developments of modern computer and biomedical technology. Most bioengineering or bioinformatics methods need to access and analyses large amounts of data. For that it is necessary to achieve an effective way that from Big data creates knowledge and useful applications. The nature of such scientific data/information/knowledge demands the use of a powerful computing and intelligent database management systems. Areas of current and future research include advanced topics in this special issue. The paper from Juhasz et al. present a novel, GPU-based streaming architecture that has the potential for drastically reducing execution times and at the same time providing simultaneous 2D and 3D visualization facilities. The system uses a highly-optimised and re-configurable pipeline of CPU and GPU cores that attempts to exploit the tremendous computing power whenever possible. The system can process live data arriving from an EEG device or data stored in EEG data files. The computer drives a large display wall system of four 46 monitors that provides a 4K-resolution drawing surface for visualizing raw EEG data, potential maps and various 3D views of the patient's head. Authors from the group of M. Gusev presents the scalable balancer in Cloud computing extended to Dew Computing level. Given a successful implementation of scalable low level load balancer, implemented on the network layer. The scalability is proved with series of experiments. The experiments showed that it adds small latency of several milliseconds and thus it slightly reduces the performance when the distributed system is underutilized. Rybicki et al. demonstrate how to realize a Software-as-a-Service solution for a variety of science software using container technologies. The presented solution utilized DARIAH-DE research infrastructure based on OpenStack and UNICORE grid to deliver an extensible solution for the digital humanities domain. The paper from authors Forer et al. describe the extension of Cloudflow to support ApacheSpark without any adaptions to already implemented pipelines. The described performance evaluation demonstrates that Spark can bring an additional boost for analyzing next generation sequencing(NGS) data to the eld of genetics. The Cloudflow framework is open source and freely available at https://github.com/genepi/cloudflow . The paper by Memon et al. describes an approach for executing computational jobs across HPC and HTC resources while operating on geographically dispersed data via a global federated file system. The solution is realized as a new framework that integrates UNICORE and GFFS to provide a standards-based environment to support large scale data intensive computations frequent in today's biomedical analyses. The paper Parallel algorithm for the state space exploration, authored by L. Allal et al., proposes a new automatic verification technique based on model checking that determines whether a given system satisfies its specification. Such a technique suffers from the state explosion problem when traversing all possible states of systems. A new synchronized parallel algorithm (SPA) of exploration is proposed based on a fixed number of threads. Exhaustive comparative studies between the standard parallel exploration algorithm in SPIN and the new SPA show that the SPA performs slightly better regarding the execution time and memory complexity. Smart systems in telemedicine frequently use intelligent sensor devices at large scale. Practitioners can monitor non-stop the vital parameters of hundreds of patients in real-time. The most important pillars of remote patient monitoring services are communication and data processing. Large scale data processing is done mainly using workflows. In the paper Eszter et al. give a brief overview of the different check pointing techniques and propose two new provenance based check pointing algorithms which uses the information stored in the workflow structure to dynamically change the frequency of check pointing and can be efficiently used for dynamic health care smart systems.

Contextual crowd intelligence

Most data analytics applications are industry/domain specific, e.g., predicting patients at high risk of being admitted to intensive care unit in the healthcare sector or predicting malicious SMSs in the telecommunication sector. Existing solutions are based on "best practices", i.e., the systems' decisions are knowledge-driven and/or data-driven . However, there are rules and exceptional cases that can only be precisely formulated and identified by subject-matter experts (SMEs) who have accumulated many years of experience. This paper envisions a more intelligent database management system (DBMS) that captures such knowledge to effectively address the industry/domain specific applications. At the core, the system is a hybrid human-machine database engine where the machine interacts with the SMEs as part of a feedback loop to gather, infer, ascertain and enhance the database knowledge and processing. We discuss the challenges towards building such a system through examples in healthcare predictive analysis -- a popular area for big data analytics.

Three-Dimensional Surface Modeling using Intelligent Database Techniques

Range image acquisition can be defined as the process of determining the physical distance from a given observation point to all points of consideration in a three-dimensional (3D) surface object. The technology of range finders has been used for many years in military and airborne remote sensing survey applications. This paper suggests the use of computer databases (instead of mathematical formulas) for modeling 3D surfaces. The basic concept is the proper selection of representative samples from the measured range values to be records in the model database. The main contribution in this paper is developing an Intelligent Database Management System (IDMS) used for selecting samples and giving depth prediction at a given space point. The IDMS performs standard database operations such as adding, updating and deleting records in order to minimize the data storage memory size and prediction error. Search for specific data which are not previously stored in the database is impossible in classical database management systems (DMS).

The Intelligent Data Management System for Toxicogenomics

Toxicogenomics is now emerging as one of the most important genomic application because the toxicity test based on gene expression profiles is expected to be more precise and efficient than current histopathological approaches in a pre-clinical phase. One of the challenging issues in toxicogenomics is the construction of intelligent database management system which can deal with heterogeneous and complex data from many different experimental and information sources. TEST(Toxicogenomics for Efficient Safety Test) database is especially focused on the connectivity of heterogeneous data and the intelligent query system which enable users to obtain relevant useful information from the complex data sets. The database deals with four kinds of information; compound, histopathology, gene expression, and annotation information. Currently, TEST database maintains toxicogenomics information for 16 compounds, 45 microarrays, 190 animal experiments, and customized 4.8 K rat clone set. Our presented system is expected to be a good information source for studying of toxicology mechanism in the genome-wide level and can also be applied to the designing toxicity test chip.

An approach to realizing interoperability using APPC

With the advent of reliable and high-speed network systems and intelligent database management systems, distributed systems are no longer a myth. There is a rapid development of software as well as hardware technology to support the distributed environment. Many researchers have done work in this field and several technical papers can be found cited in various textbooks 1–6. More and more, the need to realize open systems is becoming inevitable. Interoperability is no more just an idea but a reality. At the same time, more systems are looking for protocols to support dialog paradigm. This paper describes a conceptual framework for an architecture using the Advanced Program-to-Program Communications LU 6.2 protocol for illustrative purposes to achieve interoperability.

The automatic synthesis of descriptive data using the taxonomic hierarchy

SummaryMaxted, N., White, R. J. & Allkin, R.: The automatic synthesis of descriptive data using the taxonomic hierarchy. ‐ Taxon 42: 51‐62. 1993. ‐ ISSN 0040‐0262.Descriptive data about taxa at one level of the taxonomic hierarchy may be inferred from representative data referable to another level. The process is bi‐directional. Data agglomeration enables taxon characteristics to be derived from the characteristics of a subordinate level and data propagation enables taxon characteristics to be derived from the characteristics of a superior taxonomic level. This applies to any form of descriptive taxonomic data (e.g. morphological, cytological, distributional, uses, etc.) and at all levels of the taxonomic hierarchy. Examples taken from a revision for the production of DELTA format descriptions and distribution data are discussed. The major advantages of implementing these processes in a database system are: products and queries are based directly on representative data; they can be derived automatically from the data set without the necessary intervention of a taxonomist; less work is required to synthesize products or answer queries; there is greater guarantee of database integrity; data redundancy is reduced and thus storage space is conserved. The use of these processes should play a central part in any development of taxonomically intelligent database management systems.