Ontologies-based business integration

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Integrating Processes Applications and Information. Semantic Web Programming Frameworks. EBusiness Integration with Semantic Technologies. EBusiness Standards.

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Knowledge Management Technologies. Ontology Engineering. Advanced Knowledge Creation Techniques. Matching and Mapping.

Deducing New Knowledge. To keep the ontology readable for humans, we conduct an activity for annotating the ontology. In addition to general information e. In the same way as the implementation activity, this activity is semi- automatically supported by the use of existing databases in this domain. Ontology Evaluation. Before the ontology is published, ontology evaluation is performed.

Here, the final ontology is first evaluated against the CQs listed during the specification activity. Then, different tools i. In addition to the subsequent activities as described above, the IT benchmarking ontology engineering process is supported by a number of side activities as also suggested by NeOn. Those activities are described as follows:. Ontology Quality Assurance and Control. The control activity refers to process monitoring and ensures that the subsequent activities described above are performed and completed correctly. The ontology quality assurance activity ensures the quality of the ontology implementation process and its artifacts.

During the development of the IT benchmarking ontology, the process was monitored and controlled constantly using checklists. Ontology Documentation. While developing the IT benchmarking ontology, the utilized and created documents and artifacts e. Thus, the OWL ontology consists of the following: i classes as sets of individuals, ii individuals as instances of classes i.

In addition to the implementation of the domain knowledge, it is possible to define cardinality ranges and other constructs e. Moreover, a reasoning engine was used during the development process to avoid inconsistencies in the specifications of the ontology classes and properties. The IT benchmarking ontology was initially built based on already-existing IT service descriptions and catalogs of numerous small- to medium-sized enterprises and several questionnaires from different IT benchmarking approaches.

As previously stated, these data were collected over the last eight years in the context of research activities and were supervised and evaluated within different benchmarking approaches cf. These data encompass results from strategic and consortial IT benchmarks. Subsequently, as a result of the different data acquisition channels of online web platforms, Excel questionnaires and other sources cf.

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The database consists of unique descriptions of key performance indicators, which are composed of 25 service catalogs from individual companies. In addition, the underlying data for the ontology development consist of data sets from consortial IT benchmarks. These data sets encompass questions on 15 IT services answered for 10 companies as an yearly average over the last six years.

Furthermore, IT benchmarking results from different companies were used to extend the database for the ontology development. These data were acquired over the last eight years within a strategic benchmark based on [ 46 ], and each data set consists of 1, quantitative and qualitative data points of a single organization. As previously stated, the existing service catalogs and databases were analyzed using NLP techniques to extract the most important concepts and terms relevant to building the ontology for more details on NLP, see Pfaff and Krcmar [ 42 ].

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Moreover, the alignment to DUL [ 15 ] was added to make the ontological commitments explicit and to specify the intended meaning of the introduced concepts [ 23 ]. Starting with the top-level description of the proposed benchmarking ontology, the ontology can be divided into the following three sections: Individual Benchmarks equivalent to one specific benchmark , Participants and Values and the General Indicator Declaration.

Individual Benchmarks section introduces concepts to describe, processes relying on different IT service descriptions or questionnaires, including a customizable structure of selectable indicators measured within a benchmark. Participants viz. The indicators themselves and their hierarchical and intermediate relationships are organized in a three-layer taxonomy referred to as the General Indicator Declaration section. The General Indicator Declaration is described in more detail in Sect. Gray nodes indicate inheritances from DUL concepts and properties. The nodes of the graph illustrated in Fig.

IT benchmarking ontology consisting of three different sections: i Individual Benchmarks, ii Participants and Values and iii General Indicator Declaration. Solid arrows indicate data or object properties, with their direction being defined by rdfs:domain and rdfs:range [ 1 ]. An IT benchmark is identified by a specific name. As described in Sect. In the following, an individual benchmark refers to a single conduction of a benchmark that a company is participating in i.

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In other words, two individual benchmarks can be conducted based on two different indicator structures and indicators, or these individual benchmarks can differ in the year of being conducted. In both cases, these benchmarks are represented as a delimited instance within the ontology to uniquely identify individual benchmarks. As already mentioned in Sect. For example, whereas an individual benchmark may be based on specific questionnaires i.

Ontologies-Based Business Integration

To represent and distinguish the contexts a specific indicator is captured within individual benchmarks; different concepts have been introduced to represent an indicator structure i. In the following, the concepts that an Individual Benchmark consists of are described in more detail:. A benchmark can be seen as a time-specific event for the conduction of a benchmark. Each benchmark has to be assigned to one or two of these benchmarking types.

The labels of a benchmark are represented by arbitrary strings, referring to benchmark names, for example expressed in one or multiple languages. During a benchmark event, indicator values are reported by utilizing exactly one previously specified questionnaire that defines a structure for capturing these data of the KPIs. These questionnaires are connected to a benchmark instance using the hasReporting property. Within the ITBM ontology, a questionnaire refers to a physical object e. Indicators are more abstract information objects and are linked to a questionnaire using the categorizesIndicator property, which is a sub-property of DUL:realizes.

A questionnaire or a group of questionnaires consists of different indicators focusing on different aspects or activities within an IT department, such as general service offerings or more generic questions. A questionnaire can be further nested into sub-questionnaires coupling questions to a specific topic of interest to compare through the benchmark. This results in a mono-hierarchical structure that can be realized using the transitive hasChild property, which is a sub-property of DUL:hasPart and defines a questionnaire to be a part of another questionnaire.

An IT service consists of a set of different activities to be performed by an IT department to meet specific business or IT demands. Thus, as the structure of an individual benchmark is based on IT service catalogs, describing the parts of this service in natural language and based on indicators for the measurement of the service KPIs, this structural information is represented by the ITService concept.

In other words, an ITService is a specialization of the more general questionnaire consisting of KPIs that are directly linked to IT service activities and their organizational resources such as costs or human resource. Once an IT service is defined, it can also be further divided into sub-services. It is also possible to structure an IT service in more fine-grained ways. Thus, an IT service can be divided into a set of smaller service segments. For example, an indicator set of a service could be divided into indicators referring to mobile and stationary IT systems in accordance with the description of the underlying IT service catalog structure.

Moreover, a service segment may be further divided into smaller segments if necessary to maintain the structural information of this service. In the domain of IT benchmarking, a participant represents an organization contributing values of benchmarking indicators answering questions specific to an individual benchmark. In the ontology, this organization is represented as a class i. The contributed values are indicated by the use of the Participation and IndicatorDeclaration classes. The description of these classes is as follows:. A participant represents an organization participating in specific benchmarks minimum of one and is identified by its name.

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According to the IT benchmarking process an organization contributes its KPIs alues while participating in a specific benchmark. However, this approach is insufficient, as a single property cannot represent the ternary relation of a benchmark and the participant in combination with the contributed values cf.

Towards Systems for Ontology-based Data Access and Integration using Relational Technology (Part 5)

Therefore, the participation has been implemented based on the Nary Participation ontology design pattern [ 14 ], which specifies a reified participation concept and a participationIncludes property to link participation with i at least one event e. Regarding the ITBM ontology, however, the time index of the participation iii was removed as we are only interested in the time span for which collected values are valid i.

An Ontology Based Architecture for Integrating Enterprise Applications

Moreover, to further specify the role of a certain entity during one participation, additional properties i. For each indicator value, provided by a specific organization, an IndicatorDeclaration grounded in the DUL:Region concept is instantiated. This is included in one participation and represents the measures of exactly one specific indicator.

An IndicatorDeclaration has one or multiple values attached to it. Currently, these values can be in the format of strings, Booleans or decimals, represented by the corresponding subclasses. For each pair consisting of a participation and an indicator, only one IndicatorDeclaration is instantiated.

Thus, using a subclass referring to a specific unit type, instead of the more abstract IndicatorDeclaration, an indicator can only be described by a single type of unit at one time, even if more values are attached to it e. A StringIndicatorDeclaration refers to indicator values that are described in string format. Suitable indicators include qualitative indicators such as descriptions of service level agreements. A BooleanIndicatorDeclaration refers to indicator values that are described in Boolean format, that is, indicators having binary values assigned e.

For example, such indicators refer to the question of whether a certain technology is used within an organization. A DecimalIndicatorDeclaration refers to indicator values that are described in decimal format.

kessai-payment.com/hukusyuu/javascript-application/diqu-comment-pirater.php It represents, for example, quantitative performance indicators, such as the number of workplaces, as well as cost indicators. One of the most important relations within the concepts described above is the relation between the Benchmark and its associated participation and the involved organizations.

The Participation concept is only required to model the ternary relation between a benchmark, its participants and their provided values.