Knowledge Base Management System Research Articles

On Enforcing Dyadic-type Homogeneous Binary Function Product Constraints in MatBase

Homogeneous binary function products are often encountered in the sub-universes modeled by databases, from genealogical trees to sports, from education to healthcare, etc. Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility. The (Elementary) Mathematical Data Model provides 17 dyadic-type homogeneous binary function product constraint types. MatBase, an intelligent data and knowledge base management system prototype, allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them. This paper describes the algorithms that MatBase uses for enforcing all these 17 homogeneous binary function product constraint types, which may also be used by developers not having access to MatBase.

The effect of technological innovations and digitalisation on the competitiveness of hospitality industry enterprises

Subject. With an increasingly competitive hotel market, it is vital to provide high quality services to increase the number of guests and ensure competitiveness. An essential component of the quality of services at a modern hotel is the digital environment, which is used both for management purposes and to offer the guests digital services.Objectives. The purpose of our study was to analyse the digital systems and technologies used by leading international and Russian hotels and assess their effect on their competitiveness.Methods. In our study we performed a comprehensive case study and analysed statistical data, regulatory documents, articles, and websites concerning the digital transformation of the hospitality industry and the introduction of innovative tools by hotels. We also performed a survey of frequent Russian and international travellers.Results. In our study, we analysed the challenges and development trends of the hospitality industry. We determined the most promising innovations based on digital technologies that are used in the hospitality industry, both in Russia and all over the world. As a result of the survey, we found out that the quality and range of services are important factors when choosing a hotel. With the current turbulence on the market and stricter requirements to comply with, the introduction of digital solutions is of great importance for ensuring the competitiveness of hotel businesses.Conclusions. Digital transformation, the introduction of knowledge-based management systems, and marketing strategies taking into account the absolute digitalisation of society can help hotels to increase their competitiveness, to optimise their number of employees, and to ensure the stable development of the industry as a whole.

On enforcing dyadic relationship constraints in MatBase

Dyadic relationships are widely encountered in the sub-universes modeled by databases, from genealogical trees to sports, from education to healthcare, etc. Their properties must be discovered and enforced by the software applications managing such data, in order to guarantee their plausibility. The (Elementary) Mathematical Data Model provides 11 dyadic relationship constraint types. MatBase, an intelligent data and knowledge base management system prototype, allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them. This paper describes the algorithms that MatBase uses for enforcing all these 11 dyadic relationship constraint types.

On enforcing dyadic-type self-map constraints in MatBase

Self-maps are widely encountered in the sub-universes modeled by databases, from genealogical trees to sports, from education to healthcare, etc. Their properties must be discovered and enforced by the software applications managing such data to guarantee their plausibility. The (Elementary) Mathematical Data Model provides 13 dyadic-type self-map constraint types. MatBase, an intelligent data and knowledge base management system prototype, allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them. This paper describes the algorithms that MatBase uses for enforcing all these 13 self-map constraint types, which may also be used by developers not having access to MatBase.

Design of intelligent decision support system for supply chain sustainability assessment

The objective of this research is to develop an intelligent decision support system (IDSS) that can aid decision-makers in enhancing the performance and sustainability of the sugarcane agroindustry's supply chain. A case study focusing on a sugarcane agroindustry supply chain was proposed. The IDSS was developed using the waterfall approach, which follows the system development life cycle (SDLC) and employed an object-oriented programming technique. The outcome of this study is a prototype of the IDSS, consisting of a database, model base, and knowledge-based management system configuration. To evaluate the sustainability of the supply chain, a fuzzy inference system and an adaptive fuzzy inference system algorithm were utilized. Through verification and validation, it was determined that the IDSS prototype has the potential to be implemented in real-world scenarios, as long as certain assumptions related to the model are taken into consideration.

Refined Information Service Using Knowledge-Base and Deep Learning to Extract Advertisement Articles from Korean Online Articles

We live amidst a flood of information in the internet and digital revolution era. Due to such indiscriminate information access, there are many problems in accurately recognizing the information desired by the user. Moreover, there are many difficulties with finding accurate information and the articles that individuals want due to indiscriminate advertisements in various online papers such as SNS and internet newspapers. Negative experiences with these advertisements lead to advertisement avoidance; if media users avoid advertisements, the media’s existence is threatened. This system aims to provide high-quality online articles, excluding promotions, by designing a system using a knowledge-based management system (KBMS) and Deep Learning system to solve the problems of advertisement. In other words, this system compares advertisement phrases or general keywords related to a specific company and product promotion with the contents to be searched in the database system of the knowledge-based management service.

Automated Rule Checking for MEP Systems Based on BIM and KBMS

Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.

Research on Enterprise Economic Management System Based on Computer Big Data Technology

The support system for enterprise economic management is an intelligent decision support system. It is composed of user interface management system, problem handling system, database and database management system, model library and model library management system, method library and method library management system, knowledge base and knowledge base management system. It integrates economic mathematical modelling, artificial intelligence technology, operations research methods and decision-making methods. Through the combination of knowledge reasoning and numerical calculation, it provides more powerful decision support capabilities than traditional decision support systems. Based on this research background, the paper takes financial management in economic management as an entry point and introduces a computer financial economic management system based on mobile Agent. At the same time, the article introduces a financial and economic early warning algorithm for enterprises based on decision trees.

KNOWLEDGE BASE MANAGEMENT SYSTEM UNTUK TROUBLESHOOTING COMPUTER PADA CV VINGKA COMPUTER

The Objective of this research is to easier in providing the waiter to the customer if you want to consult about a computer malfunction. The methodology in this research is the Waterfall method and for the method on the application form of the method Forward Chaining which is a method of inference that reasoning from a problem to a solution and Backward Chaining is Adopting the Goal-Driven, starting from the expectation of what will happen (hypothesis) and then look for evidence to support (or opposite) with our expectations. The results obtained from this research is the Knowledge Base Management System For Computer Troubleshooting integrated with the database to support the process of computer malfunction information easily, quickly, and accurately. The conclusion that can be drawn from this program, namely system reduces the Knowledge Base Trouble Shooting, will be help in gave the service to customers, storage has become a single entity, can reduce redundancy or redundancy of the data, can display information a computer malfunction that required so can easier the officer analysis computer damage

К ВОПРОСУ ПОСТРОЕНИЯ ПРЕЦЕДЕНТНЫХ СИСТЕМ УПРАВЛЕНИЯ

article describes a formal approach to making managerial decisions based on precedents and building case management systems. Speaking of a precedent without reference to a specific subject area, it is important to emphasize that we are talking about a retrospective restoration of certain events, gaining experience, and possibly making managerial decisions similar to those that have been observed in the past. Decision making in human-technical systems is fraught with significant difficulties. These difficulties arise both as a result of uncertainty and the lack of experience in the functioning of systems (decision making) in new (changing) conditions. As practice shows, many real complex socio-technical systems operate in one day mode (short-term planning and manual control), without relying on previous experience, which ultimately leads to the need to use manual control in case of any emergency situations. It has been emphasized that in any socio-technical system that depends on the human factor, a knowledge-based management system is necessary, which, on the one hand, allows formalizing the decision-making process as a whole, and, on the other hand, reduces the dependence on the knowledge of individual narrow specialists in the subject area. To a certain extent, such systems accumulate knowledge; nevertheless, it is obvious that there are certain prerequisites (including external ones) under which this knowledge can be effectively applied. Often knowledge alone is not enough, experience is also needed. There have been considered the precedent decision-making systems based on the factor-domain principle of the subject area classification.

Toward a Big Data Knowledge-Base Management System for Precision Livestock Farming

Nowadays, we are in the era of advanced technologies where tremendous amount of data is produced by multiple sources such as sensors, devices, social media, user experiences, etc. Furthermore, this raw data has a low value, and major part is not really useful or important for business. One way to give an added value to this stored data is to extract useful knowledge from it, for the ending-system or the end-users by a process commonly called knowledge Discovery in Database (KDD). Smart Farming uses a large amount of connected technologies producing also a huge amount of data in order to maximize productions by reducing: human efforts, environment impact and wasting natural resources. In this paper, We develop a new data analytic architecture dedicated to Precision Livestock Farming (PLF) to improve in particular the livestock animals production, animals’ welfare, and farming processes. We present a new data processing architecture for a knowledge-base management system (KBMS) allowing to ease decision support and monitoring operations that can help farmers and stakeholders to better exploit data and have a long-term view of the evolution of the knowledge it contains. Our main contribution in the present paper is a new architecture specifically developed for the precision livestock farming integrating a periodical data reevaluation which address the problematic of data conservation and the decrease in data value over time.

Matbase auto Function Non-relational Constraints Enforcement Algorithms

MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relational Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the Department of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucharest Polytechnic University, as well as successfully used by two Romanian software companies.

The Design Approach of Knowledge-based Management System for Control of Manufacturing Processes of Woven Fabrics

The Design Approach of Knowledge-based Management System for Control of Manufacturing Processes of Woven Fabrics

Knowledge Base and Rule Base Safety Management Systems Development Strategies and Applications for Petrochemical Industries: A Brief Outline

This review paper discussed about the development tactics and applications of knowledge based and rule-based decision support systems for health and safety in oil and gas sector. It recommends the users on the extent of health and safety concerns for risk and hazard investigation and prevention in which the system developers and researchers mostly face issues during selection and adaptation of development approaches. A complete and systematic review and classification of articles from last seven years has been conducted and briefly analysed, to investigate various methodologies employed in previously developed rule based (RB) and knowledge based (KB) decision support systems. All major bibliographic databases covering a broad series of health and safety fields were searched in various oil and gas production sector related studies. Eighteen articles met the KB and RB study. Twelve of these met the methodological prospective of required investigation. In this review paper, researchers briefly explain about the methodological aspects and industrial applications of rule and knowledge-based decision support systems for reducing work place risks and hazards in safety and health domain. Conclusions that can be extracted from this analytical review paper of the methodological variation and applications of rule based and knowledge-based decision support system is that both inferences are interrelated to each other. RB and KB inferences techniques and methods are used in both interpretations for developing decision support systems in the context of oil and gas extraction and production sector. But safety and health consider KB decision systems as more effective for taking suitable decisions due to real time information and experts’ opinions which facilities safety professionals to take appropriate decision then RB reasoning decision support systems.

Research on Management System of Automatic Driver Decision-Making Knowledge Base for Unmanned Vehicle

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.

An Efficient Collaborative Recommender System for Textbooks using Silhouette index and K-Means Clustering Technique

A recommender system provides a great platform for filtering of information and various knowledge-based management systems. They provide very good recommendation to the users so that they are able ...

Construction productivity fuzzy knowledge base management system

Construction companies need a knowledge management system to collate, share and ultimately apply this knowledge in various projects. One of the most important elements that determine the time estimates of any construction project is productivity. Such projects have a predilection towards uncertainty and therefore require new generation of prediction models that utilizes available historical data. The research presented in this paper develops, using fuzzy approach, a knowledge base to analyze, extract and infer any underlying patterns of the data sets to predict the duration and productivity of a construction process. A six-step protocol has been followed to create this model: (i) determine which factors affect productivity; (ii) select those factors that are critical; (iii) build the fuzzy sets; (iv) generate the fuzzy rules and models; (v) develop the fuzzy knowledge base; and (vi) validate the efficacy and function of these models in predicting the productivity construction process. The fuzzy knowledge base was validated and verified using a case study and the results were satisfactory with 92.00% mean validity. In conclusion, the developed models and system demonstrated the ability of a knowledge base management to predict the patterns and productivity of different construction operations.

Building a knowledge portal for communities based on personalized functionalities

Over the last decade portals have become an important facet of the Web and of structured communities like companies. This paper presents a knowledge portal that has been developed to provide an improved support to the users during their navigation and access to information. The idea of this work is to create a smooth and richer connection between the users and the relevant data. These tasks have been done by considering personalization aspects (i.e., user preferences and user activities), based on ontological user profiles aimed at managing information effectively. The portal has been tested within the Knowledge-Based Management System 2.0 Project.

Desktop or remote knowledge base management systems for taxonomic data and identification keys: Xper2 and Xper3

Storing, sharing, managing taxonomic data is crucial for research and systematic outreach. In this context, Xper2 (Ung et al. 2010) and Xper3 (Vignes Lebbe et al. 2015) are two platforms dedicated to taxonomic descriptions and computer-aided identification. These freeware provide a sleek and easy to use interface, and do not require computer skills. Xper2 is available on Windows, MacOS or Linux. Download the software on the website. Xper3 gives users the ability to work simultaneously and remotely on the same knowledge base. Create your own account on the online platform. The numerous knowledge bases (over one thousand active KB) already created and published with the two platforms prove their relevance and efficiency (See for example Mathieu et al. 2012, Kerner 2012, Corvez and Grand 2014, Martin et al. 2015, Engel et al. 2016, Saïd et al. 2017). Scientists can share their data on phenotypes (structured descriptions, documented with images, videos, and text including bibliography and external links), compare phenotypes, and import or export partial or total content in various standard formats such as SDD (Structured Descriptive Data, a TDWG data standard), CSV (Comma Separated Values, used in Spreadsheet), and NEXUS (used in phylogenetic analyses, Maddison et al. 1997) for external analyses. KB can be exchanged between Xper2 and Xper3 via SDD files. We propose demos of the desktop version (Xper2) and online version (Xper3), illustrated with various contents on fossils, botany, marine species and arthropods. The TDWG 2017 participants are invited to come with their own data (list of taxa, identification keys, character-taxon matrices). They will learn and test how to import existing content into Xper KB, and how to build and publish keys (free access keys and single access keys) automatically.

Knowledge-Based Network Management System for Movable and Deployable ICT Resource Unit

When a disaster occurs, the demand for information and communication technology (ICT) services drastically increases. To meet such demands, a national project was undertaken in Japan to develop the Movable and Deployable ICT Resource Unit (MDRU). One challenge regarding the MDRU is securing operators to work the units in emergency situations. As ICT service users have diverse and frequently changing demands, strong technical skills and practical knowledge are required for the administration of MDRUs. In this paper, we propose a knowledge-based network management system to alleviate the burden on administrators. To deal with the structural changes to network systems that frequently occur with changes in ICT service demand, we introduce modularization techniques into our previous research. The proposed system can be easily reconfigured by join/disjoin modules corresponding to changes in the system configuration of the MDRU. The results of our experiments using the implemented experimental system confirm that the proposed system can be applied to MDRU operation and effectively supports administrators.