Data modelling in complex application domains

This paper describes the general properties of complex objects in engineering designs. There are two types of complex objects: (i) the complex structural objects which describe the physical composition of the design, and (ii) the complex functional objects which describe the behavior of the design and its components. Data manipulation operations on complex structural objects are governed by a set of structural invariants. Similarly, the validation of functional abstraction is governed by a set of functional invariants. The structure-function interactions are represented by interaction objects that describe a set of mappings. These three object types constitute the Structure-Function paradigm. The S-F paradigm can be used to represent engineering designs and active environments, monitor manufacturing operations and industrial processes, and carry out simulations.

The use of STA Data Model in Learning and Teaching of Data Modeling

This paper proposes a design method and parameter tuning method for how only using the DCS configuration control software module to combine and achieve generalized predictive control algorithm in a complicated controlled plant case. In order to show the effectiveness of the control algorithm using the DCS layer control software module to achieve model predictive, two types of complex objects are considered as example to do simulation : non-minimum phase process and the complex process with recycling and pure delay. The simulation results show that the proposed control algorithm can provide similar control performance of GPC.

In biology and medical sciences, highly parallel biological assays spurred a revolution leading to the emergence of the '-omics' era. Dimensionality reduction techniques are necessary to be able to analyze, interpret, validate and take advantage of the tremendous wealth of highly dimensional data they provide. This paper is based on a DNA microarray study providing gene signatures for hypoxia. These gene signatures were tested on a large breast cancer data set for assessing their prognostic power by means of Kaplan-Meier survival, univariate, and multivariate analyses. We explore the use of several mathematical programming-based techniques that aim to reduce the gene signature sizes as much as possible while maintaining the key characteristics of the original signature, more precisely: the signature prognostic and diagnostic significance. The proposed signature reduction techniques have very interesting potential uses. Indeed, by downsizing the relevant data to a manageable size, one can then patent the core set of biomarkers and also create a dedicated assay (e.g.: on a customized array) for routine applications (e.g.: in the clinical set up) leading to individualized medicine capabilities. Our experiments show that the reduced hypoxia signatures reproduced qualitatively and quantitatively in a similar way that of the original ones.

In this paper, a scaled measurement system designed for wireless channel characterization is proposed and discussed, and experimental results are reported. The system consists of a vector network analyzer, miniaturized W-band transmitter and receiver probes, scaled buildings, and a precise computer-controlled positioner. The scaled propagation measurement system is designed to operate coherently at the 90.8-92.8-GHz band, and it provides a dynamic range of 85 dB. As the measurement wavelength in this system (/spl cong/3 mm) is approximately two orders of magnitude smaller than the actual wavelength in most commercial and military wireless systems, a typical dimension of the propagation environment can be smaller than the actual environment proportionally. Hence, propagation measurements can be done very efficiently under laboratory conditions using a scaled model of a city block. This system is intended for verifying the results of wave-propagation prediction software. It can also be used to investigate scattering models for different types of buildings and complex objects, data collection for wireless communication, and many other studies such as diversity methods (frequency and polarization).

This paper is a short presentation of the SOM (Semantic Object Model) approach. SOM was created to fulfill two main objectives. The first objective is the revision of the traditional data modeling techniques in order to integrate them within an object oriented framework, without sacrificing the main object-oriented principles, namely encapsulation, extendibility and reusability. The paper advocates that the way data modeling concepts have been combined with object-oriented concepts does not reach that goal. The second objective is the improvement of data modeling techniques in order to make them able to model roles. Roles are an important real world aspect we think has not been suitably dealt with. This paper describes attributes and phases, the two main concepts of SOM. Attribute is the single concept used to model all static relationships and phases are thought to model roles. The paper also outlines the textual language (T-SOM) and the graphical language (G-SOM) used in SOM to describe conceptual schemes.

The goal of this paper is to establish that there exists a strong relationship between current issues of data models in Database Management and representations of knowledge in Artificial Intelligence. The different data modelling techniques that have been used in the two research areas are surveyed and classified into Predicate Calculus-based, Network, and Procedural ones. The similarities and differences between them are presented.

Semantic data modeling of multimedia data is a fundamental problem in multimedia databases. The variety, context-dependency, and ambiguity of the semantics of multimedia data make it difficult to apply conventional data modeling techniques, which usually deal with explicit, self-contained alphanumeric data. Many semantic data modeling techniques are either designed for multimedia or can be used for modeling multimedia data. Specifically, we describe MediaView as a novel object-oriented view mechanism that can successfully capture the elusive nature of multimedia data.

Multi-agent systems (MAS) offer to solve problems in complex system and application domains, but are also complex environments. Standardization in the area of agent communication has led to development of MAS frameworks, simplifying MAS development. Agent interactions are essential for agents to exhibit their social abilities, and interaction scenarios for commonly known, standard interaction protocols, can be seen as building blocks of successful multi-agent systems. This article presents an introduction to interaction scenario modelling and code generation techniques, allowing quick prototyping of agent interactions and creation of agents implementing scenario roles

This paper reviews the progress of the research on spatial data interoperability and several methods currently used to achieving spatial data interoperability, puts the emphasis on analyzing the advantages, as well as the handicaps and existing conditions of interoperability based on shared database and the discrepancy of data models specific to different GIS, which is a main obstacle to spatial data interoperability, and brings forward two methods for achieving spatial data interoperability based on shared spatial database: one is realizing spatial data interoperability among different clients by using the same set of data model in database, the other is interoperability among clients through objects translation in a shared database. The paper also makes a comparison between these two methods in feasibility and difficulty of carrying out. Based on the analysis, this paper makes an experiment with Oracle Spatial in a local network using the second method, the emphasis of the experiment is put on the interoperability of oriented point objects and text, or annotation objects, as the two types of objects have no standard data model in Oracle Spatial and data models brought forward by different GIS vendor used to describe these two types of objects are specific to their GIS software. Two GIS software, MapInfo and Geomedia, are used as client in the experiment. The experiment shows that this method to achieve spatial data interoperability is practical and could be used inside an organization by employees in different departments, who are familiar with different software in their work, to obtain data sharing and interoperability. This method can also lower data redundancy and improve work efficiency.

Although hybrid learning has been adopted in higher education for a decade, it is still a challenge to build a satisfactory Computer Aided Instruction (CAI) on Internet. Not only it is difficult to guide users through the operations of CAI, but also it is hard to measure the learning effectiveness. This paper presents an effective method of using a set of CAI steps to demonstrate hybrid learning on data modeling technique. A constructivist theory is applied for users to go through the CAI steps. The result is a CAI for teaching and learning data modeling techniques. A case study of designing a hospital patient system is used for illustration. Its merit is the effective application of interactivity in teaching and learning data modeling techniques in designing an extended entity relationship model.

A conceptual data modeling framework with four levels of abstraction for environmental information

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

Since the early recognition of Persuasive Technologies, the notion of digital design with the intent to influence and change attitudes and behaviors has evolved tremendously. Digital influence and behavior design is now recognize in a variety of theoretical and methodological approaches, and behavior design is continuously influencing more complex application domains. With the development of the field emerges a need for clearer understanding of these different design approaches, their possibilities and limitations, in order for designers to identify and apply the best approach depending on the intent and context. In this paper, we discuss some of the primary overlaps between Nudging and Persuasive Design. Moreover, we point towards research in neuroscience in order to highlight a potential need to reconsider central elements of persuasive design, such as efficient praise and rewards. The aim of the paper is to contribute to the ongoing discussion about the role of behavior design in digital media, and the ethical considerations that emerge from this domain.

We take the term X/O impedance mismatch to describe the difficulty of the OO paradigm to accommodate XML processing by means of recasting it to typed OO programming. In particular, given XML types (say, XML schemas), it is notoriously difficult to map them automatically to object types (say, object models) that (i) reasonably compare to native object types typically devised by OO developers; (ii) fully preserve the intent of the original XML types; (iii) fully support round-tripping of arbitrary, valid XML data; and (iv) provide a general and convenient programming model for XML data hosted by objects. We reveal the X/O impedance mismatch in particular detail. That is, we survey the relevant differences between XML and objects in terms of their data models and their type systems. In this process, we systematically record and assess X-to-O mapping options. Our illustrations employ XSD (1.0) as the XML-schema language of choice and C# (1.0-3.0) as the bound of OO language expressiveness.