Knowledge repositories

It is therefore important to know how to access and trawl thorough this internal knowledge so that you can add value to the company, e.g. Chemical structural databases, project data, information from theoretical modelling. 

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Inventories are basically structured knowledge repositories. They can be independent systems or alternatively can integrate data from other information systems. Information Technology (IT) tools are software applications that assist the user in specific operations. Some inventories and IT tools that can be considered as essential components of the DSS are described in generic terms below, with emphasis on their scientific features rather than to specific software applications (e.g., commercially available software). 

They capture explicit knowledge generated within the organization in a centralized or multiple decentralized knowledge repositories. 

The database component of a DSS provides the knowledge repository that is needed for decision support. Some sort of management system must be associated with a database in order to provide the intelligent access to data that is needed. In this section we have examined a number of existing constructs for database management systems. We now turn to the model-based portion of a DSS. 

The science of nutrition is the knowledge repository for the understanding of essential nutrients, their quantitative requirements at different life stages, the mechanisms behind their essentiality and the pathological phenotypes... 

No risk management process and tool in place. However, after the introduction of each new product, the project team formally meets and reviews the process, learning, failures and successes, and document and stores them in a knowledge repository to be considered when launching other new products. 

Allowing time at your monthly meeting for employees (on a rotating basis) to share customer service best practice stories. Record their suggestions to build a knowledge repository for new employees. 

Note that each generated text will accord exactly with the underlying specification knowledge repository because there is no manual involvement in its production. If exact accordance is not obtained, this can only be because of a translator error once corrected, tliat error vanishes for all time and so the translation quality improves steadily rather tlian remaining constant. 

There are very many statements tliat are provably true and hence have the status of theorems within the knowledge repository. All of these are built by applying the inference rule to unproven data statements and proving their correctness by resolution. Tliese theorems are later used to support correctness reasoning about tlie specification of tlie application when it is presented. 

Tlie diagram (Figure 2) shows how tlie system is constructed. Initially there are a small nmnber of axioms (currently four) tliat are simply placed in tlie knowledge repository. These (and only these) support tlie next level of reasoning. The inference rule is then applied to tlie meta-compiler data and each offered tuple is proved by resolution before inclusion in tlie repository. At this point, tlie repository includes a knowledge representation of the meta-compiler, tlie axioms could be removed because every proof that might require to reference them will instead reference a lower-level theorem, preserved as a lemma at tlie meta-compiler level, in tlie knowledge repository. 

In this work we propose a framework of the process model knowledge repository (PMKR) to store and exchange three kinds of process model information neutral process model, neutral process data and related process rules. When the operation process change, the different process models are modified to adapt to the new environment aided by PMKR. The process model repository is illustrated in figurel. 

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