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STRENGTH AND LAPS OF FRAMEWORK

The proposed model IDA based on the AODB model has an advantage over the other database models. As the iDSS query execution time reduces by 93% than the existing data models.

3.6.1 USABILITY

The IDA based on the AODB model is designed to allow the user to determine attributes. Criteria and methods are required to define based on the object for decision-making. Decision makers can submit a query or use stored PL/SQL procedure. The results are all annotated with the graphical user interface, which provides enough decision-making ready information to the decision maker to make the decision. Therefore, it satisfies the usability characteristics.

3.6.2 REUSABILITY

The object-oriented relation database is used to implement the AODB model. Reusability in object-oriented concepts includes code reuse within a single-software project and code reuse between multiple projects. The base object defined by the I-Data Engineer, if the structure of another object in different decision-making system is similar to that is applicable. Therefore, it satisfies the reusability characteristics.

Execution time and memory used for IDA data model IDSS query performance in data retrieval from the AODB model using ID As

FIGURE 3.12 Execution time and memory used for IDA data model IDSS query performance in data retrieval from the AODB model using ID As.

3.6.3 FLEXIBILITY

In this work, implemented UDT such as base-data objects and resource objects can be used to derive new “object” according to the future need using inheritance, aggregation, and alteration in the existing UDT. The newly obtained data type can add to the current agent table. Therefore, it is a flexible solution.

iDSS query performance using IDAs (Experiments 5-8)

FIGURE 3.13 iDSS query performance using IDAs (Experiments 5-8).

3.6.4 MANAGEABILITY

In this work, the term manageability is considered in terms of ability to manage the decision support data model by the database programmer and I-Data Engineer, which means management capabilities of Database Engineer and i-Data Engineer that allow quickly determine, set up, and provide support for IDA. It is based on the AODB model used in cognitive decisionmaking. Therefore, it is an easy solution.

3.6.5 SCALABILITY AND EXPANDABLE

The consideration of scalability in the proposed model to handle the gl owing amount of data and the work-capable manner or its ability to be enlarged to accommodate that growth. Therefore, the proposed solution can fulfill the future needs of decision-making applications. This model extends by implementing FACULTY_AGENT to store faculty-related base-data object and resource-data object.

3.6.6 INTEROPERABILITY

In this work, interoperability is considered as the ability of implementing the proposed model to communicate and work together in a different programming environment. Performed IDAs using PL/SQL supports user- defined object types as parameters. These stored subprograms can quickly call by VB.Net, ORACLE Forms, and JADE programming interface.

3.6.7 DEGREE OF AUTOMATION

The term degree of automation is considered as the automatic generation of data retrieval and storage in Figure 3.5. Automation is implemented through triggers and calling resource object methods to generate cognitive decisionmaking information.

The proposed model requires more memory storage to store cognitive decision-making information. The memory utilization of blocks is fotmd 0.39% in MDDBs, but it saves only aggregate data hr the storage media, 62.61% in RDBs as compared to AODBs. Tire 1252-kB memory block is founded hr AODB, which is the highest memory block utilization among three models. The proposed model is based on a structured data model. Currently, this solution can implement cognitive decision-making based on structured data.

The limitation of this work is that it does not support the multimedia database. For flexibility, if the AODB model is utilizing images and video information as base data, image processing and video streaming algorithms are used as resource objects. Management of a large volume of data hr the distributed ЮА based on the AODB model is a challenging research work for evaluating scalability. Expansion of the AODB model with more number of agents requires work to provide security for agent communication hr the model.

CONCLUSION

This work highlights the result statistics, due to which the introduced model is improving information retrieval as well as storing cognitive decision-making information for an iDSS. It suggests database designers to adopt the IDA using the AODB model for developing an iDSS for improved performance in cognitive decision-making.

 
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