Mining E-commerce Data from E-shop Websites

E-commerce is a constantly growing and competitive market. Comparing product prices is an important task for online retailers as well as for e-shoppers. Online merchants compare their prices to those of their competitors for being able to adjust their prices on the market in order to remain competitive whereas the consumers want to find the best price for a specific product. Since internet prices are updated once a day or even more often and there is a huge number of product offers on the Web the product and price data need to be identified, collected and compared by an automated approach. This paper contributes a novel approach for the automated identification and extraction of product price data from arbitrary e-shop websites which is independent from the e-shops´ language and the product domain. The adequacy of the proposed approach is demonstrated and evaluated through an experiment.