Scholarly record
EQUATIONLESS QUALITATIVE MODELS OF BUSINESS INTELLIGENCE TASKS BASED ON COMMON SENSE HEURISTICS
Abstract
There is a broad spectrum of BI (Business Intelligence) models. CBIM (Complex Business Intelligence Model) integrate several BI aspects, e.g. artificial intelligence, data bank, psychology, sociology etc. They are unique, partially subjective, inconsistent, vague and multidimensional. CBRMs development suffers from IS (Information Shortage). IS often eliminates straightforward application of traditional statistical methods. It is therefore often prohibitively difficult to analyse them using numerical quantifiers. Oversimplified or highly specific CBRMs are sometimes obtained. Their practical applicability is therefore (very) limited. AI (Artificial Intelligence) has developed a number of tools to solve such problems. Qualitative reasoning is one of them. It is based on the least information intensive quantifiers i.e. trends. There are just three trend / qualitative values used to quantify variables and their derivatives: plus/increasing; zero/constant; negative/decreasing. There are qualitative BI knowledge items in equationless forms such as heuristics. For example if the investments into BI are increasing then the costs of BI tasks are decreasing. Such verbal knowledge item cannot be incorporated into a traditional numerical model. A qualitative model can be developed under conditions when the relevant quantitative BI model must be heavily simplified. The key information input into CBRMs is expert knowledge. The case study presents a model based on integration of 6 equationless relations using 5 variables e.g. Degrees of data consistence and data completeness. The result is represented by 11 scenarios. The paper is self-contained, no a prior knowledge of qualitative models is required.
Publication Impact Profile
- Captures
- Mendeley - Readers: 2
Publication details
ReferencesPending
Structured references will appear here after the reference import pass. The count is preserved now so the scholarly record is not incomplete.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
