Process management and assertion enforcement for a semantic data model Download PDF EPUB FB2
Process management and assertion enforcement for a semantic data model Proceedings of ACM SIGMOD '86 International Conference on Management of Data, Washington, DC, May 28–30,SIGMOD Record Nixon B.A., Mylopoulos J. () Process management and assertion enforcement for a semantic data model. In: Schmidt J.W., Ceri S Cited by: Semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases.
This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An extension of process modeling languages is designed which allows representing the semantics of model element labels which are formulated in natural language by using concepts of a formal ontology.
This combination of semiformal models with formal ontologies will be characterized as semantic process modeling. The approach is exemplarily applied to the languages EPC (Event-driven Cited by: Semantic Business Process Management: A Vision Towards Using Semantic Web Services for Business Process Management Martin Hepp1,2, Frank Leymann3, John Domingue4, Alexander Wahler1, and Dieter Fensel1,5 1DERI Innsbruck Innsbruck, Austria 2Florida Gulf Coast University, Ft.
Myers, FL, USA 3University of Stuttgart, Stuttgart, Germany 4Knowledge Media Institute, The Open University, Walton.
model, logical data model, and physical data model. •A semantic data model (SDM) captures the business view of information for a specific knowledge worker community or analytic application. •A logical data model (LDM) captures the business relationships in the enterprise information independent of a specific analytic application or.
IMPLEMENTING A SEMANTIC DATA MODEL I. IMPLEMENTING A SEMANTIC MODEL Overview This thesis is a description of an implementation of the SIDUR data model.
SIDUR is the semantic level in the OSIRIS Integrated Information System, which is an information system architecture developed by Michael J. Frei ling and his students.
Semantic Modeling 26 CIS Pros and Cons of E-R Emp#, Name, Address Salary, Skill Advantages uSimple and easy to understand. uVery popular. uSemantic richer than classical data models. Disadvantages: uNot a formally defined data model. uDeals with some integrity constraints.
uDifficult to distinguish entities from relationships. The semantic data model is a method of structuring data in order to represent it in a specific logical way. It is a conceptual data model that includes semantic information that adds a basic meaning to the data and the relationships that lie between them.
Advances in Database Technology - EDBT '88 International Conference on Extending Database Technology Venice, Italy, MarchA Prolog interface to a Functional Data Model database. Pages Process management and assertion enforcement for a semantic data model.
Pages Chung, K. Lawrence (et al.). management, semantic business process management, and policy management. 4 Related Work Compliance management is critical for enterprise governance, as w e have shown in. In 80% of studies, the semantic process mining was useful and effective to improve hospital processes and improve its sion: This review can show an overview the application of.
and business process models by applying reasoning techniques to query the process space or using ontological mappings for bridging the business-IT divide. Such an approach is known as semantic business process management. We conducted an empirical case study to explore semantic business process man-agement.
Semantic Data Model. Imagine that you are developing the next-generation music app, and need to create a robust database and application to store and work with data about topics such as artists. maintain process models. However, with a semantic annotation of process models and the usage of ontologies, tasks can be automized and the workload of the user can be reduced to a minimum.
In this paper we discuss different possibilities and the added value of annotating a business process model with semantic information. IntroductionCited by: Advances in Database Technology--EDBT ' International Conference on Extending Database Technology Venice, Italy, MarchProceedings.
Efficient data sharing for information retrieval systems --Process management and assertion enforcement for a semantic data Efficient data sharing for information retrieval systems.
the validation of a semantic process model, i.e., the question whether the semantic annotations and the standard parts of the process model together “make sense”: an isolated view on the structure of the process or the semantic annotations alone cannot answer this question.
Semantics for task management. 2 State of the Art of Semantic Technology Process Model Matching and Similarity Process model matching is concerned with automatically identifying the correspondences between the activities of two process models.
It represents the prerequisite for vari-ous advanced techniques such as automated modeling recommendation [KHO11], dupli-Cited by: 8. comprehensive semantic business process model. A semantic framework for BPM that is designed to optimize and querying the process space is introduced in .
Ontologies for the description of business processes would allow coupling business process definitions with semantic regulatory compliance definitions. In , such an approach is proposed. The basic “semantic bottleneck” in Business Process Management can be described as follows (see also [HeLD'05]): Although a significant part of an enterprise and its process space is already stored in computer systems (e.g.
in the form of process models, code fragments as. The resulting Domain Model with its components is intended to support semantic interoperability across the participating systems in order to provide common services. The result of the work, the final Domain Model, will contain a set of semantic agreements covering the overall model, the data entities and the controlled Size: KB.
the existing data model to enable a customized business process creation. This approach is demonstrated with an artifact that stores information from anITS, loads that infor-mation into the chosen data model and gives the user the chance of choosing the relevant ﬁelds for the creation of personalized process models that are tailored for his File Size: 2MB.
In this paper, the authors present a framework for semantic annotation that tackles the problem of the heterogeneity of distributed process models to facilitate management of process knowledge.
The feasibility of the approach is demonstrated by means of exemplar studies, and a comprehensive empirical evaluation is used to validate the authors Cited by: Key words: process management systems, semantic constraints, se-mantic process veriﬁcation, compliance validation and enforcement 1 Introduction Due to continuously changing market conditions, companies are forced to fre-quently adapt their business strategies in.
Specification, verification, and enforcement of semantic integrity using behavioral abstraction (Technical report) [Leveson, Nancy G] on *FREE* shipping on qualifying offers.
Specification, verification, and enforcement of semantic integrity using behavioral abstraction (Technical report)Author: Nancy G Leveson. Chapter 2 defines these languages in terms of the Semantic Binary Model. Later chapters show the use of these languages in other database models.
Chapter 3 defines the Relational Data Model and presents a top-down methodology for the design of relational databases. Chapter 4. Semantic Methods for Execution-level Business Process Modeling: Modeling Support Through Process Verification and Service Composition (Lecture Notes in Business Information Processing) [Weber, Ingo M.] on *FREE* shipping on qualifying offers.
Semantic Methods for Execution-level Business Process Modeling: Modeling Support Through Process Verification and Service Composition Cited by: A semantic data model is one built upon concepts and the model describes the meanig of its instances. The data model describes how each the stored data or symbols relate to the real world.
Otherwise the data is random and has no logical meaning. 5/5(). Effects of Semantic Quality in Business Process Modeling Johannes Buder In contrast to the increasing meaning of business process management (BPM), there is a lack of knowledge about processes It will be shown that semantic quality in data semantics improves model.
semantic quality. Adding semantic annotation of existing process models, i.e. adding references to ontology elements. Storing the semantic process models into a Semantic Business Process Library. Querying of the library for discovery of existing semantic process models or fragments for reuse through auto-completion, which will decrease the effort and.
Exporting Object-Process Methodology System Models to the Semantic Web paradigm , management of an actual process taking place in real time can be made easier using an OPM model representing framework for building Semantic Web and Linked Data applications." .
Personalizing Actions in Context for Risk Management using Semantic Web Technologies Jiewen Wu 1, Freddy L ecu e;4, Christophe Gueret, Jer Hayes, Sara van de Moosdijk1, and Gemma Gallagher3, Peter McCanney2, Eugene Eichelberger2 1 Accenture Labs, Ireland 2 Accenture The Dock, Ireland 3 FJORD, Ireland 4 INRIA, France Abstract.
The process of managing risks of client contracts is manualFile Size: 1MB.Lawrence Chung, Daniel Rios-Zertuche, Brian A. Nixon, John Mylopoulos: Process Management and Assertion Enforcement for a Semantic Data Model.
EDBT ; Brian A. Nixon, John Mylopoulos: Integration Issues in Implementing Semantic Data Models. DBPL At the moment Semantic Business Process Model (SBPM) build on the semantic annotation, the specification of process activity, the control flow between activities and business functions of process. However, the correctness of SBPM is based on technical knowledge and experiences of the model designer, which is apparently not feasible for complex and dynamic business : Peng Tan, Feng He, Chen Min Yan.