Definition Of Data Model Research Articles

Enhancing Network Security: Leveraging Machine Learning for Intrusion Detection

In order to precisely identify and categorise different kinds of network attacks, this study focuses on applying machine learning approaches for network intrusion detection. Data collection, preprocessing, feature scaling, model definition, feature selection, and assessment metrics are all part of the methodology. Different machine learning models, including Decision Tree Classifier and Random Forest Classifier, are considered, along with the use of all features or some part of features for each attack category. Evaluation is performed using K-fold cross-validation, with metrics such as accuracy, precision, recall, and F1-score analysed. Results indicate the efficiency of Random Forest Classifier in handling high-dimensional datasets and improving detection accuracy, making it a superior choice for network intrusion detection tasks.

From survey to integrated digital documentation of the cultural heritage of museums: A protocol for the anastylosis of archaeological finds

The conservation, development, and enjoyment of the artefacts in museums can benefit greatly from the application of integrated digital technologies. This topic is of great interest not only to researchers in various disciplines, but also to the managers of this heritage, the producers of technologies, innovative companies and start-ups, and the wider public. However, it does not currently seem to be possible to identify methods and protocols shared between the actors involved for the purpose of managing the phases of acquisition, definition of data models, management of digital content, and its later implementation. “Tailor-made” approaches therefore have direct consequences on the efficacy of the digitalisation processes currently available when applied to conservation and restoration, and to the promotion of cultural heritage. In this paper, we propose a protocol for the digitalisation of archaeological artefacts for the purpose of their display in museums. The method is illustrated with reference to a case study involving very rare artefacts from a wooden throne from the Villanovan Age. Finally, the work is part of the results of a wider project for the definition of protocols for the surveying and modelling of cultural heritage, from the architectural scale down to the scale of artefacts, carried out through public-private partnerships.

FDup: a framework for general-purpose and efficient entity deduplication of record collections.

Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.

The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium

We describe the design, implementation, and impact of a data harmonization, data quality checking, and dynamic report generation application in an international observational HIV research network. The IeDEA Harmonist Data Toolkit is a web-based application written in the open source programming language R, employs the R/Shiny and RMarkdown packages, and leverages the REDCap data collection platform for data model definition and user authentication. The Toolkit performs data quality checks on uploaded datasets, checks for conformance with the network’s common data model, displays the results both interactively and in downloadable reports, and stores approved datasets in secure cloud storage for retrieval by the requesting investigator. Including stakeholders and users in the design process was key to the successful adoption of the application. A survey of regional data managers as well as initial usage metrics indicate that the Toolkit saves time and results in improved data quality, with a 61% mean reduction in the number of error records in a dataset. The generalized application design allows the Toolkit to be easily adapted to other research networks.

Back Propogation Neural Network based Cybersecurity Information Retrival from Repository

The wireless network is interconnected with pervasiveness of vast range of personal devices. The advancement of technological devices involved in generation, processing, exchange, sharing and utilization of specific applications. Thus, cyberspace has been adopted for device security for business, organization and government. To retain cybersecurity machine learning (ML) is employed for detection and authentication of malware. However, ML attacks are vulnerable for both training and testing phases for reduction of security breaches. This paper developed an appropriate cybersecurity mechanism for repositories for data exchange. The proposed scheme involved in definition of data model with Incident Object Description Exchange structured information format. The structured information is utilized in XML format of cybersecurity for information exchange. The developed structured information utilizes ML for analysis of feasibility and usability. The simulation analysis demonstrated that proposed ML exhibits significant performance for cybersecurity estimation.

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

Overview of the Available Knowledge for the Data Model Definition of a Building Renovation Passport for Non-Residential Buildings: The ALDREN Project Experience

According to its strategic long-term vision, Europe wants to be a climate-neutral economy by 2050. Buildings play a crucial role in this vision, and they represent a sector with low-cost opportunities for high-level CO2 reduction. The challenge the renovation of the existing building stock, which must be increased to 3%/year, more than double compared to the current 1.2%/year. In this context, the ALliance for Deep RENovation (ALDREN) project has the goal of encouraging property owners to undertake renovation of existing buildings using a clear, robust, and comparable method. This paper aims to present the ALDREN approach and the ALDREN Building Renovation Passport (BRP), giving an overview of the connections and data links to other existing databases and certification schemes. To understand the data value potential of buildings, one requires reliable and trustworthy information. The Building Renovation Passport, introduced by the recent Energy Performance Building Directive (EPBD) recast 844/2018/EU, aims to provide this information. This paper presents the experience of the ALDREN BRP for non-residential buildings as well as the development procedure for its data model and the potential that this tool could have for the construction market. The ALDREN BRP has been structured into two main parts—BuildLog and RenoMap—with a common language that facilitates communication on the one hand and, on the other, the setting of renovation targets based on lifetime, operation, and user needs.

What distinguishes data from models?

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.

Exploration Analysis Based on Improved BP Neural Network in Human Body Movement

This paper fist gives common definition of human body movement data model and establishes human body movement model according to kinemaics of human body, and then adopts improved BP neural network to analyze and predict human body movement. Introducing genetic algorithm into BP neural network can better overcome problems caused by random preliminary value of BP neural network during the process of determining network structure, such as network vibration and it is easy for network to fall into local solution. In addition, generalization ability of neural network can be effectively improved. It can be obtained from analyzing and comparing simulation experiments that the accuracy of BP neural network algorithm in analyze and predict human body movement is slightly improved.

Formal Specification of the NoSQL Document-Oriented Data Model

We built formal definition of the NoSQL document-oriented data model. Two formal data models were built. The first data model is based on sets and second one is based on multisets (bags). The special relations called subdocument and subrecord were introduced. It is proven that those relations are preorder. Also general results about the cofinal relation on the sets are given.

データモデル定義に基づくマルチモーダル対話記述言語の設計と実装

It is necessary to establish a methodology of developing multimodal interactive systems as the spread of various input and output devices. Previous MMI (MultiModal Interaction) description language used a state transition model for defining interaction and that has low maintainability and extensibility. In this paper, we design and implement a new MMI description language named MrailsScript from the software engineering point of view. MrailsScript consists of the datamodel definition that can be inherited from semantic web class definition, and the annotations of interaction task and dialogue initiative. By expanding the existing web application development framework, our Mrails framework can automatically generate prototype codes of MMI application which is based on MVC (Model-View-Controller) model. We also developed a helper application of MMI system, MrailsBuilder, which assists coding of MrailsScript and realizes multilingual capability of the target MMI system.

A standard-based data model for configuration management and maintenance support

The goal of this paper is to define a data model for configuration management and maintenance, taking into account the whole life cycle of assets. First step was to review the systems related to configuration management checking the utilization of standards and norms which are connected in a practical way, and looking for the relationship of the different possible data models used for configuration management systems. A definition of data model is presented organized in 9 layers of information and it is applied in an use case where is checked the problems of the data model with the purpose of improve it.

Problem of Standardization of Configuration Data in Wireless Sensor Networks: The Case of Routing Protocols

The exploitation of Wireless Sensor Networks (WSN) is constrained by limited power, low computing power and storage and short-range radio transmission. Many routing protocols respecting these constraints were developed but, it still lacks formal and standardized solutions being able to help in their configuration. The configuration management that responds to this concern is very important in this type of network. It consists of the definition of data models to configure and is very necessary for the good network performance. Tangible results were obtained in traditional networks with the emergence of NETCONF and YANG standards, but on the best of our humble knowledge there are none yet in WSNs. We propose in this paper wsn-routing-protocol, a YANG data model for routing protocols configuration in WSNs. Following our model, we propose two YANG configuration data models based on the latter: they are respectively aodv for AODV and rpl for RPL.

Integrated model-centric framework for support of manufacturing operations. Part I: The framework

Model-centric technologies must overcome a number of challenges limiting their ability to meet the needs of the Process Industries. First, a software component enabling the formulation of hybrid data-driven/model-based problems must be developed so that a number of realistic computer-aided process-engineering (CAPE) activities can be performed in an industrial environment. Second, these software components must be integrated into a single software platform so that the synergy between complementary model-based applications is unlocked and fully exploited by end-users. In this work we present a model-centric framework for integrated simulation, parameter estimation, data reconciliation and optimisation of industrial process systems. This framework is based on a new software architecture for CAPE that promotes the formulation of process-operations problems based on first-principles process models. This is achieved via the innovative Problem Definition Component (PDC) and the underlying mechanism of domain-specific data models, given in the form of templates (Data-Model Templates, or DMTs) and definitions (Data-Model Definitions, or DMDs). An industrial case-study is presented to illustrate the framework.

Standardization Activities for IPTV Set-Top Box Remote Management

IPTV services are gaining widespread use, requiring service providers to have effective methods for remotely configuring and managing IPTV set-top boxes (STBs). Solutions for such remote management are becoming standards-based. This article examines published specifications and ongoing activities on IPTV STB remote management in four standards organizations: the Broadband Forum, the Digital Video Broadcasting Project, the Open IPTV Forum, and the Alliance for Telecommunication Industry Solutions IPTV Interoperability Forum. The authors compare the protocol and data model definitions, investigate interoperability test events, and comment on possible future directions for standardization.

Protocol-Independent Data Modeling: Lessons Learned from the SMIng Project

The definition of management data models remains a significant effort and because the number of deployed management protocols keeps growing, duplicated efforts and inconsistencies remain a serious problem. A protocol- independent data modeling language for network management protocols called SMIng was developed within the Network Management Research Group of the Internet Research Task Force to tackle some of these problems. This article summarizes the main lessons learned during this project, with a view to helping designers of future management data modeling languages.

STEP-compliant process planning system for compound sheet metal machining

Compound punch-cutting machines are referred as machines that perform both punching and laser-cutting operations. Such machines can greatly increase the efficiency and flexibility of sheet metal manufacturing. Though there are computer-aided design (CAD) systems developed to take care of product design, and computer-aided process planning (CAPP) systems to generate optimal tool paths and plans and computer-aided manufacturing (CAM) systems for various manufacturing requirements, according to our literature search, there is no platform or system dedicated to compound sheet metal machining, which lead to problems such as low efficiency and high cost due to lack of optimal scheduling methods for the compound machining methods. This research attempts to develop a prototype STEP-compliant process planning system for supporting the compound punch-cutting machines. The system has interfaces to input CAD product data and output machine control codes. It integrates software modules for nesting optimization, cutting/punch path optimization and planning, simulation, and machining parameters set up. The focus of the paper is placed on investigating the problems and solutions of compound machining, the structure of the STEP-compliant system, and the definition of STEP-compliant product data models and interfaces for creating a STEP-compliant programming environment, where the above mentioned software modules are integrated.

A STEP-compliant process planning system for sheet metal parts

Sheet metal product design, processes planning and manufacturing are normally carried out in a sequence of stages using different computer-aided software tools. There are many computer-aided design (CAD) systems to take care of product design, computer-aided process planning (CAPP) systems to generate optimal tool paths and plans and computer-aided manufacturing (CAM) systems for various manufacturing requirements such as punching or cutting. According to our literature search, there is no platform or system available that can efficiently integrate the CAPP and CAM and computer numerical control (CNC systems) in sheet metal product development processes. Although ISO 10303 AP207 was formally published by the ISO, its application is limited. This research attempts to develop a prototype STEP-compliant process planning system for sheet metal product development. The system has an interface to input CAD product data. It integrates software modules for nesting optimization, path optimization and planning, simulation, and machining parameters set-up and CNC machining. The focus of the paper is placed on the structure of the prototype STEP-compliant system, as well as the definition of STEP-compliant product data models and interfaces for creating a STEP-compliant programming environment, where the abovementioned software modules are integrated.

Plant−Model Mismatch Analysis in Deethyleniser Simulation. 2. Application to Data-Model Selection and Tuning

The focus of this investigation into difficulties associated with the simulation of a commercial deethyleniser is on the use of K-value data models based on equations of state as provided in PRO/II (Simulation Sciences, Inc.). For various K-value data-model definitions, the value of the temperature-independent form of the binary interaction coefficient, kij = αij, of the dominant ethylene/ethane binary has been varied. All other thermodynamic data-model definitions were kept the same. The values of the optimized average equilibrium tray efficiency parameter, , can vary markedly with the choice of the K-value data model and the kij values used and ranged from about 0.84 to about 1.01. The associated minimum total error objective function values are, however, remarkably similar, leading to the concept of “Equivalent” simulation models. For all three equation of state K-value data models examined, a quadratic relation was found to exist between a defined kij and the corresponding optimized average equilibrium tray efficiency.

Recent developments in information management for primary and community health services.

While health services provided outside the hospital environment are utilised by the majority of the community, until recently there has been little interest in developing a standard approach to information management in community health settings. With greater accountability for health services expected in the future, State and Federal governments have begun to set the necessary standards by the design of a common data model and data definitions for Primary and Community Health Services. This model will affect the manner in which the National Health Data Dictionary develops--from a primarily institution-based document to a broader approach which encompasses non-institutional care. Two new high level concepts have been introduced: "issue" and "activity". Four States have also formed a consortium to design and implement an information management system for Community Health Services. This necessitates adoption of standard classification systems which could be applied in this environment, especially to the two new high level concepts. This paper outlines recent developments in information management for Community Health and provides a brief summary of available classification systems.