Optimization Research On Relation On-Line Analytical Processing Based On Rough Sets

With the coming of digital era, the data accumulated by corporations are increasing explosively, so the analysis and decision based on numerous data have nowadays become one of the puzzles in the field of database. Meanwhile, the rapid development of distributed computing has offered chances and new challenges as well for the solution of this problem. It has become the optimal choice relied on the existing mature technique for this problem to construct a relational distributed data warehouse and aided with highly-powered optimization of on-line analytical processing. As the most essential character of on-line analytical processing, multi-dimension is exhibited by the materialized views. Under the assumption of real-time restriction to some extent, the basic task of on-line analytical optimization is to maintain the views effectively. Under all the above background, this paper integrates the on-line analytical functions into a self-developed accelerating engine for industrial application. Besides, it constructs a relational data warehouse DMWS with the character of distributed computing. And with a suite of optimization methods proposed for updating views, the maintenance efficiency of DMWS is sharply upgraded under the application of large-scaled workloads.This paper proposes a horizontal distributing technology hierarchically based on foreign keys searching and the measurement by selectivity. Firstly, in a breadth-first manner, a group of single-parent schemas linked by unilateral key referencing is detected either with high query frequency or at a heavy updating cost. Thus, the schema union is partitioned horizontally according to selectivity. Based on the trigger sub-system of the four-layer data warehouse, the strong consistency is achieved via round-robin placement. By sharing the schemas union among the autonomous sites with a horizontal manner, the proposed method obtains equilibration to guarantee the scalability of DMWS. And more importantly, due to the horizontal partitions obtained by selectivity, the system endows the updating relative to the complex views with conglomeration. Under the horizontal distribution of the union set, this paper also gives a vertical partitioning method on the fact table among the autonomous sites. Via the decomposition of all the query patterns, a clustering model is constructed by the blocking operators. Therefore, the vertical partition acquired by both the relatively indiscernibility cluster and validity index, can exhibits a controllable partition granularity. And the temporal localization can be realized in the on-line analytical queries accordingly.The complexity and diversity of the queries in the on-line analytical processing not only increases the hardness of the view maintenance, but also provides the requirement of digging the view dependencies. This paper proposes a hierarchically updating method for the multi-join view by the use of auxiliary view. The weak and strong combination are deeply analyzed in the view of the integrator. Due to the workload of the global query mainly concentrated on the autonomous sites under the strong combination, the optimization of SPJ view can be made on the multi-join view by selecting pushed down.Under this assumption, the candidate space of the auxiliary views is obtained by intersecting all the query views. A hierarchical evaluation method is constructed by the use of rough reduct based on both valid cost model and total cost model, and an iterative executing can bring about either an optimal hierarchical auxiliary view set or an optimal updating orders. Besides all the above, the inner-independency and the unilateral intra- dependency are both proved formally in the context of rough reduct. Finally, a full-scale experiment is given to show the superiority of the rough hierarchical updating method compared with the kindred under the circumstance of all the above optimizations.To support the on-line analytical optimization thoroughly, the rough sets theory is extended by three methods reletive to knowledge acquirement. Besides the ability of firmly fulfilling the vertical partition and the hierarchical updating, these methods can be primely adopted to the traditional on-line analytical problem with hierarchical character.