Database optimization for novelty mining of business blogs

The widespread growth of business blogs has created opportunities for companies as channels of marketing, communication, customer feedback, and mass opinion measurement. However, many blogs often contain similar information and the sheer volume of available information really challenges the ability of organizations to act quickly in today’s business environment. Thus, novelty mining can help to single out novel information out of a massive set of text documents. This paper explores the feasibility and performance of novelty mining and database optimization of business blogs, which have not been studied before. The results show that our novelty mining system can detect novelty in our dataset of business blogs with very high accuracy, and that database optimization can significantly improve the performance.

► This paper contributes to the investigation of the feasibility and performance of detecting novel business blogs.
► The experimental results of using B-Tree indexing on a larger data set show an increase in performance of database retrieval in novelty mining.
► The experiments also noted that indexing and batching techniques have reduced the time needed by over 90%.
► In conclusion, a well planned and design schema and strategies can speed up a database application by orders of magnitudes compared to a database application that is poorly designed.
► The successful optimization of the MySQL database has been implemented in a real novelty mining system.