Dominant Relationship Analysis In Hotel

Implementing and Applying Dominant Graph for Dominant Relationship Analysis
S S Dhawale
Department of Computer Science & Engineering
S S P M 's College of Engineering
Kankavli, India. [e-mail] A B Chougule
Department of Computer Science & Engineering
Bharati Vidyapeeth 's College of Engineering
Kolhapur, India.

Abstract—Dominant relationship concept has recently used for answering preference queries. Here the concept of dominant relationship has been extended for the purpose of doing business analysis. More specifically here we have presented new type of analysis, called as dominant relationship analysis. This dominant relationship analysis can help product manufacturers to design new products, compare company 's

While the concept of dominance is very useful from the perspective of customers selecting the products they like, what is interesting to manufacturers is whether their products are popular with customers compared to their competitors’ products. Consider you are a manager of hotel company. You want to know the business position of a local hotel b in the current market with regard to your preference, i.e., price and distance to the beach, by checking how many other hotels are better/worse than b. For the sample hotels shown in Fig. 1, you can deduct the conclusion that hotel b is better than 2 other hotels but worse than another 2 hotels with regard to your preference.
Here, we present five types of queries for dominant relationship analysis. Queries are as follows (i) Linear Optimization Queries (LOQs), (ii) Subspace Analysis Queries (SAQs), and (iii) Comparative Dominant Queries (CDQs). (iv) Skyline Product Query (SPQ) (v) Skyline Subspace Query (SSQ). This dominant relationship analysis can help product manufacturers to design new products, compare company 's products with competitor company 's products and finding interesting subspaces of a product which can be used for promoting the product. We have designed efficient algorithms for answering queries

. . , A s } and PB = {B 1 , . . . , B t }, respectively. We also have the preference of a set of customers C = {C 1 , . . . , C n }. Each of the products or customer preferences can be represented as a point in an N -dimensional space, D, with dimensions D 1 , . . . , D N being the attributes of the products and customer preferences. For simplicity, we use the general term “object” to refer to a product or a customer’s preference. We also assume that attributes product or customer preferences are minimum