504,764 research outputs found

    Effects of Semantic Quality in Business Process Modeling

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    In contrast to the increasing meaning of business process management (BPM), there is a lack of knowledge about processes that impedes their analysis, implementation, and execution in business process management systems (BPMS). In this context, process modeling is an opportunity to capture process knowledge. Nevertheless, models are incomplete concerning semantic completeness. Therefore, ontologies as explicit and formal specification are used to enrich models semantically to achieve a high degree of semantic quality, model efficiency and effectiveness. But also ontology engineering needs models to describe the underlying discourse of universe. For this reason, it is the paper’s goal to examine and compare the Resource Event Agent Model (REA) with respect to semantic quality, model effectiveness, and -efficiency in data modeling for ontologies. Moreover, the paper addresses the research question, if REA enables an effective modeling to reduce the precision deficit and complexity in BPM. Keywords (Required

    Structuring and Modeling Knowledge in the Context of Enterprise Resource Planning

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    In recent years, the Information Technology (IT) industry has been overwhelmed by a new class of packaged application software collectively known as Enterprise Systems (ES). Enterprise Systems are comprehensive business operating systems that weave together all the data within an organisation's business processes and associated functional areas. In particular, ES provide organisations with the ability to manage data and information in a real-time environment and to integrate operations between various departments; capacities that had been previously unrealized in traditional information systems. ES have since been established as an integral development in the Information Systems (IS) field and extensively studied by academics. The implementation and operation of ES are known to be complex and costly installations that require knowledge and expertise from various areas and sources. The knowledge necessary for managing ES is diverse and varied; it extends from the application of knowledge in different phases of the ES life cycle to the exchange of knowledge between ES vendors, clients and consultants. The communication of knowledge between the various agents adds another dimension to the complex nature of ES. Thus, ES clients have been motivated to reduce costs and retain ES knowledge within the organisation. Research has been conducted on the critical success factors and issues involved in implementing ES. These studies often address the lack of appropriate in-house ES knowledge and the need to actively manage ES-related knowledge. With motivation from another area of research known as Knowledge Management, academia and industry have strived to provide solutions and strategies for managing ES-related knowledge. However, it is often not clear what this 'knowledge' is, what type(s) of knowledge is relevant, who possesses the type(s) of knowledge and how knowledge can be instituted to facilitate the execution of processes. This research aims to identify the relevant knowledge in the context of Enterprise Systems. The types of knowledge required for ES are derived by studying the knowledge (techne)1 for different ES roles, managers and implementation consultants. This provides a perspective for understanding how ES knowledge can be structured. By applying a process modeling approach, the understanding of the relation of ES knowledge to roles and business processes thus gained will demonstrate how knowledge can be modeled. The understanding of ES knowledge and how it can be managed is first formalized by the development of a conceptual framework based on the existing literature. An exploratory study found that the identification of ES knowledge was necessary before the other activities in the knowledge management dimension could be effected. As an appropriate concept of knowledge could not be derived from the IS literature, the concept of techne emerged from a more comprehensive literature review. Techne ('art' or 'applied science' or 'skill') is defined as the trained ability of rationally producing, i.e. the ability to produce something reliably, under a variety of conditions, on the basis of reasoning. This involves having knowledge, or having what seems to be knowledge (awareness) of whatever principles and patterns one relies on. With this foundation, the main focus of the research is on the content analysis of the most popular implementation tool for Enterprise Systems management, ValueSAP. This tool is studied with respect to the types of knowledge (techne), roles and activities in ES implementation. The analysis of ValueSAP thus contributes to the understanding of the structure and distribution of knowledge in ES projects. Consequently, case studies were conducted to understand how the derived ES knowledge can be instituted in business processes using process modeling techniques. This part of the study demonstrates the modeling perspective of the research. 1. The terms 'knowledge' and 'skills' will be used interchangeably for the context of this thesis; where the term 'knowledge' is mentioned, the author refers to the skills required in the ES context. This section is further elaborated in Chapter 2 on techne and skills

    Analyzing requirements of knowledge management systems with the support of agent organizations

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    Knowledge Management (KM) is considered by many organizations a key aspect in sustaining competitive advantage. Designing appropriate KM processes and enabling technology face considerable risks, as they must be shaped to respond to specific needs of the organizational environment. Thus, many systems are abandoned or fall into disuse because of inadequate understanding of the organizational context. This motivates current research, which tends to propose agent organizations as a useful paradigm for KM systems engineering. Following these approaches, organizations are analyzed as collective systems, composed of several agents, each of them autonomously producing and managing their own local data according to their own logic, needs, and interpretative schema, i.e. their goals and beliefs. These agents interact and coordinate for goal achievement defining a coherent local knowledge system. This paper presents a novel methodology for analyzing the requirements of a KM system based on an iterative workflow where a pivotal role is played by agent-oriented modeling. Within this approach, the needs for KM systems are traced back to the organization stakeholders’ goals. A case study is used to illustrate the methodology. The relationship of this work with current studies in agent organizations and organizational knowledge management is also discussed. Differently from other works, this methodology aims at offering a practical guideline to the analyst, pointing out the appropriate abstractions to be used in the different phases of the analysis

    Implementation of context-aware workflows with Multi-agent Systems

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    Systems in Ambient Intelligence (AmI) need to manage workflows that represent users’ activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents.This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach

    Ontology-based context-sensitive software security knowledge management modeling

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    The disconcerting increase in the number of security attacks on software calls for an imminent need for including secure development practices within the software development life cycle. The software security management system has received considerable attention lately and various efforts have been made in this direction. However, security is usually only considered in the early stages of the development of software. Thus, this leads to stating other vulnerabilities from a security perspective. Moreover, despite the abundance of security knowledge available online and in books, the systems that are being developed are seldom sufficiently secure. In this paper, we have highlighted the need for including application context sensitive modeling within a case-based software security management system. Furthermore, we have taken the context-driven and ontology-based frameworks and prioritized their attributes according to their weights which were achieved by using the Fuzzy AHP methodology

    Mobile terrestrial LiDAR data-sets in a Spatial Database Framework

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    Mobile Mapping Systems (MMS) have become important and regularly used platforms for the collection of physical-environment data in commercial and governmental spheres. For example, a typical MMS may collect location, imagery, video, LiDAR and air quality data from which models of the built-environment can be generated. Numerous approaches to using these data to generate models can be envisaged which can help develop detailed knowledge in the monitoring, maintanence and development of our built-environment. In this context, the efficient storing of this raw spatial data is a significant problem such that bespoke and dynamic access is possible for the generation of modeling requirements. This fundamental requirement of managing these data, where upwards of 40 gigabytes per hour of spatial-information can be collected from an MMS survey, poses significant challanges in data management alone. Existing methodologies mantain bespoke, survey oriented approaches to data management and model generation where the original MMS spatial data is not generally used or available outside these requirements. Thus, there is a need for an MMS data management framework where effective storage and access solutions can hold this information for use and analysis in any modeling context. Towards this end we detail our storage solution and the experiments where the procedures for high volume navigation and LiDAR MMS-data loading are analysed and optimised for minimum upload times and maximum access efficiency. This solution is built upon a PostgreSQL Relational Database Management System (RDBMS) with the PostGIS spatial extension and pg bulkload data loading utility
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