OOREA: An Object-Oriented Resources, Events, Agents Model for Enterprise Systems Design

A number of modeling approaches have been proposed in the literature for designing business information systems. This paper critiques prior data modeling approaches and presents an integrated object-oriented modeling approach that captures both the structural and the behavioral aspects of the business domain. Although there is considerable interest in object-oriented (OO) technologies in practice and in the information systems literature, there is no widely accepted OO modeling approach that facilitates the identification of objects from a business information processing perspective. Based on McCarthy’s (1982) resources, events, agents (REA) framework, the business process focused object-oriented ontology presented in this paper identifies the key resources, events, and agents in an enterprise information systems context. Termed OOREA, the ontology extends McCarthy’s REA model by capturing both the structural aspects of modeling, in terms of the objects of interest in the domain, and also the behavioral aspects in terms of the processes that modify objects. Application of the model is illustrated in the context of sales and related events for a retailing enterprise

Antithesis of Object Orientation: Occurrence-Only Modeling Applied in Engineering and Medicine

This paper has a dual character, combining a philosophical ontological exploration with a conceptual modeling approach in systems and software engineering. Such duality is already practiced in software engineering, in which the current dominant modeling thesis is object orientation. This work embraces an anti-thesis that centers solely on the process rather than emphasizing the object. The approach is called occurrence-only modeling, in which an occurrence means an event or process where a process is defined as an orchestrated net of events that form a semantical whole. In contrast to object orientation, in this occurrence-only modeling objects are nothing more than long events. We apply this paradigm to (1) a UML/BPMN inventory system in simulation engineering and (2) an event-based system that represents medical occurrences that occur on a timeline. The aim of such a venture is to enhance the field of conceptual modeling by adding yet a new alternative methodology and clarifying differences among approaches. Conceptual modeling s importance has been recognized in many research areas. An active research community in simulation engineering demonstrates the growing interest in conceptual modeling. In the clinical domains, temporal information elucidates the occurrence of medical events (e.g., visits, laboratory tests). These applications give an opportunity to propose a new approach that includes (a) a Stoic ontology that has two types of being, existence and subsistence; (b) Thinging machines that limit activities to five generic actions; and (c) Lupascian logic, which handles negative events. With such a study, we aim to substantiate the assertion that the occurrence only approach is a genuine philosophical base for conceptual modeling. The results in this paper seem to support such a claim.Comment: 13 pages, 16 figure

Supervised Machine Learning Techniques to Detect TimeML Events in French and English

International audienceIdentifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In this paper, we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach

A framework for event based modeling and analysis, Journal of Telecommunications and Information Technology, 2006, nr 4

In this paper we will present a framework for modeling and management of complex systems. There are various approaches for modeling of these systems. One of the approaches is events driven modeling and management of complex system. Such approach is needed in information systems that provide information in real-time. Most of the existing modeling approaches use only information about type of event and the time when an event occurs. However, in the databases we can store and then we can use much richer information about events. This information might be structured as well as unstructured. There are new challenges in algorithms development in case of description of event by various attributes

Network modeling unravels mechanisms of crosstalk between ethylene and salicylate signaling in potato

To develop novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modeling represents a powerful tool that can unravel properties of complex biological systems. In this study, we aimed to use network modeling to better understand immune signaling in potato (Solanum tuberosum). For this, we first built on a reliable Arabidopsis (Arabidopsis thaliana) immune signaling model, extending it with the information from diverse publicly available resources. Next, we translated the resulting prior knowledge network (20,012 nodes and 70,091 connections) to potato and superimposed it with an ensemble network inferred from time-resolved transcriptomics data for potato. We used different network modeling approaches to generate specific hypotheses of potato immune signaling mechanisms. An interesting finding was the identification of a string of molecular events illuminating the ethylene pathway modulation of the salicylic acid pathway through Nonexpressor of PR Genesi gene expression. Functional validations confirmed this modulation, thus supporting the potential of our integrative network modeling approach for unraveling molecular mechanisms in complex systems. In addition, this approach can ultimately result in improved breeding strategies for potato and other sensitive crops

Technical product risk assessment: Standards, integration in the erm model and uncertainty modeling

European Union has accomplished, through introducing New Approach to technical harmonization and standardization, a breakthrough in the field of technical products safety and in assessing their conformity, in such a manner that it integrated products safety requirements into the process of products development. This is achieved by quantifying risk levels with the aim of determining the scope of the required safety measures and systems. The theory of probability is used as a tool for modeling uncertainties in the assessment of that risk. In the last forty years are developed new mathematical theories have proven to be better at modeling uncertainty when we have not enough data about uncertainty events which is usually the case in product development. Bayesian networks based on modeling of subjective probability and Evidence networks based on Dempster-Shafer theory of belief functions proved to be an excellent tool for modeling uncertainty when we do not have enough information about all events aspect

Modeling and control of a dynamic information flow tracking system

This thesis introduces and details the effort of modeling and control design of an information tracking system for computer security purposes. It is called Dynamic Information Flow Tracking (DIFT) system. The DIFT system is developed at the Computer Science Department at the University of New Mexico, works by tagging data and tracking it to measure the information flow throughout the system. DIFT can be used for several security applications such as securing sensor networks and honeypot - which is a trap set to detect, deflect, or counteract attempts at unauthorized use of information systems. Existing DIFT systems cannot track address and control dependencies, therefore, their applicability is currently very limited because important information flow dependencies are not tracked for stability reasons. A new approach is taken, aimed at stabilizing DIFT systems and enabling it to detect control dependencies at the assembly-level, through control theory. Modern control has been used to model several cyber-physical, computing, networking, economical... systems. In an effort to model a computing system using control theory, this thesis introduces a general hybrid systems framework to model the flow of information in DIFT when control dependencies are encountered. Information flow in DIFT is represented by a numeric vector called taint vector . The model suggested benefits from the characteristics of hybrid systems and its ability to represent continuous variables and discrete events occurring. The system is stabilized by making sure that the taint vectors represent the true information flow in control dependencies. This problem is solved by designing a PID and model predictive controller which guarantee that system does not over taint, while allowing information to flow properly. The modeling framework is validated by comparing simulations of the hybrid models against. This research provides a new approach to solve the DIFT over-tainting problems through modeling it as a hybrid system and forcing the constraints to be obeyed by the taint values.\u2

Quantitative Risk-Based Analysis for Military Counterterrorism Systems

The article of record as published may be found at http://dx.doi.org/10.1002/sysThis paper presents a realistic and practical approach to quantitatively assess the risk-reduction capabilities of military counterterrorism systems in terms of damage cost and casualty figures. The comparison of alternatives is thereby based on absolute quantities rather than an aggregated utility or value provided by multicriteria decision analysis methods. The key elements of the approach are (1) the use of decision-attack event trees for modeling and analyzing scenarios, (2) a portfolio model approach for analyzing multiple threats, and (3) the quantitative probabilistic risk assessment matrix for communicating the results. Decision-attack event trees are especially appropriate for modeling and analyzing terrorist attacks where the sequence of events and outcomes are time-sensitive. The actions of the attackers and the defenders are modeled as decisions and the outcomes are modeled as probabilistic events. The quantitative probabilistic risk assessment matrix provides information about the range of the possible outcomes while retaining the simplicity of the classic safety risk assessment matrix based on Mil-Std-882D. It therefore provides a simple and reliable tool for comparing alternatives on the basis of risk including confidence levels rather than single point estimates. This additional valuable information requires minimal additional effort. The proposed approach is illustrated using a simplified but realistic model of a destroyer operating in inland restricted waters. The complex problem of choosing a robust counterterrorism protection system against multiple terrorist threats is analyzed by introducing a surrogate multi-threat portfolio. The associated risk profile provides a practical approach for assessing the robustness of different counterterrorism systems against plausible terrorist threats. The paper documents the analysis for a hypothetical case of three potential threats.This work was performed as part of the Naval Postgraduate School institutionally funded research

Automated Integration of Infrastructure Component Status for Real-Time Restoration Progress Control: Case Study of Highway System in Hurricane Harvey

Following extreme events, efficient restoration of infrastructure systems is critical to sustaining community lifelines. During the process, effective monitoring and control of the infrastructure restoration progress is critical. This research proposes a systematic approach that automatically integrates component-level restoration status to achieve real-time forecasting of overall infrastructure restoration progress. In this research, the approach is mainly designed for transportation infrastructure restoration following Hurricane Harvey. In detail, the component-level restoration status is linked to the restoration progress forecasting through network modeling and earned value method. Once the new component restoration status is collected, the information is automatically integrated to update the overall restoration progress forecasting. Academically, an approach is proposed to automatically transform the component-level restoration information to overall restoration progress. In practice, the approach expects to ease the communication and coordination efforts between emergency managers, thereby facilitating timely identification and resolution of issues for rapid infrastructure restoration