The data warehouse holds information management and turns it into meaningful management information, from which, very interesting patterns can be discovered by applying knowledge discovery process. As the update of the Data Warehouse is not too frequent, it is possible to improve query performance while storing the data retrieved by them in a cache. However, the most powerful systems have a small capacity to store the entire database in memory cache. The caching chunks technique is designed to keep in cache the query results in the form of chunks of values, instead of storing them in large tables. In this paper, we propose a new technique for caching multidimensional queries based on association rules. Using this technique will allow all users to enjoy the benefits of Data Warehousing in the best manner, and also to improve performance and also increase the use of the system while reducing the response time. The technique is build using an architecture comprising a data warehouse, a memory cache on the server and a one on each user's machine, in which the association rules and query results are stored. These results are kept in the form of chunks to enjoy all the advantages of the technique of fragmentation into chunks. This approach has been implemented and tested over a real huge data followed by displaying the results and analyzes. Data Warehouse, Data Mining, OLAP, Association Rules, Cache based on chunks In the modern business environment, Business Intelligent System should provide data access for managers, analyzers and decision makers. Business Intelligent System has become a key contributor to mark a significant competitive advantage for companies. It includes a Data Warehouse (DW) environment [9], a system for On Line Analytical Processing (OLAP), tools for Data Mining (DM) [2, 12, 14, 15, 17] , etc. Many companies have adopted DWs from operational databases. Thus, the DW associated to analysis tools OLAP are an effective solution for business intelligence. These systems are based on the multidimensional paradigm, which, based on the concepts of dimension, fact, measurement and OLAP operators, enable a multidimensional analysis of large amounts of data. There are two types of DW: ROLAP in which data is represented in a relational database and MOLAP in which data is represented in multidimensional data cubes [1] . In this work, we focus on the second one. The large size of most DWs leads to a significant cost of queries processing and leads us to find a cache efficient technique for reducing delay in multidimensional processing. The DW stores information that is collected from multiple heterogeneous information sources to answer complex queries and analyze information [5, 6, 8, 10, 16] . It collects copies of data from remote data sources and integrates this information into a repository for reporting and monitoring of strategic decisions. An OLAP system provides tools to explore and navigate through data cubes to extract valuable information [6] . Systems or applications of decision support running both queries in multidimensional data using complex techniques, and also use DM techniques to extract valuable information and useful in the considered application domain. DM techniques provide powerful extraction and knowledge discovery such as Association Rules (ARs), classification, and segmentation, etc. A DM algorithm is a well-defined procedure that takes input data and produces output as templates [1] . The DM algorithms allow the user to find partial information of great importance from the DW. Nevertheless, OLAP is not capable of explaining relationships that could exist in a DW. ARs are a kind of DM techniques to find associations between data. In this work, we present a new technique for caching multidimensional queries. This technique is based on extracting the necessary information based on ARs, caching the queries in order to reduce the processing time of them. Also, we propose a new replacement policy named MLAR, Multi-Layer caching based on ARs. It is based on a combination of cache replacement policies, already proposed in the literature with ARs leading to improve replacement requests when the cache becomes full. The remainder of the paper is organized as follows. In Section 2, we describe the context and the related works on caching. The section 3 contains a detailed explanation of our caching technique by illustrating our policy MLAR. Section 4 discusses the performance evaluation. Finally, in section 5 we conclude and we describe future works. This section is divided in two parts: in the first part, we present the context of our work. The second part discusses the related works.
Moudani et al. REF suggested to use knowledge discovery process (data mining) for managing data warehouse performance.
Enhancement of a Data Warehouse Performance using Association Rules Technique
International Journal of Computer Applications
Computer Science