An Architecture for Handling Fuzzy Queries in Data Warehouses

This paper presents an augmented architecture of Data Warehouse for fuzzy query handling to improve the performance of Data Mining process. The performance of Data Mining may become worst while mining the fuzzy information from the large Data Warehouses. There are number of preprocessing steps suggested and implemented so far to support the mining process. But querying large Data warehouses for fuzzy information is still a challenging task for the researchers’ community. The model proposed here may provide a more realistic and powerful technique for handling the vague queries directly. The basic idea behind the creation of Data Warehouses is to integrate a large amount of pre-fetched data and information from the distributed sources for direct querying and analysis .But the end user’s queries contain the maximum fuzziness and to handle those queries directly may not yield the desired response. So the model proposed here will create a fuzzy extension of Data warehouse by applying Neuro-Fuzzy technique and the fuzzy queries then will get handled directly by the extension of data warehouse.