Tertiary Storage Organization for Large Multidimensional Datasets.
Sachin More, Alok Choudhary · 2000
Large multidimensional datasets are found in diverse application areas, such as data warehousing [6], satellite data processing, and high-energy physics [9]. According to current estimates, these datasets are expected to hold terabytes of data. Since these datasets hold mainly historical and aggregate data, their sizes are increasing. Daily accumulation of raw data and jobs generating aggregate data from the raw data are responsible for this increase. Hence, estimates for the dataset sizes run into several petabytes. Though cost per byte as well as area per byte for secondary storage has been dropping, it is still not cost effective to store petabyte-sized datasets in the secondary storage [4]. Efficient storage organization for multidimensional data has been investigated extensively [8, 1, 5]. Chen et al [1] discuss organization of multidimensional data on a hierarchical storage system. The authors prove that the problem of efficient organization of multidimensional data on a one-dimensional storage system, such as tertiary storage, is NP-complete when arbitrary range queries are allowed. They present a five step strategy based on heuristics for the problem. Jagadish et al ([5]) investigated the problem of efficient organization of a data warehouse on secondary storage. The workload consists of a restricted set of range queries using hierarchies defined on the dimensions. They cast the problem as finding an optimal path through a lattice. They propose a dynamic programming based algorithm that determines how various dimensions are laid out. We are not aware of any work that takes into consideration practical constraints like the order in which the data already exists or will be generated. Given an order in which data currently exists (or will be generated), and a limited amount of temporary storage space, we investigate issues in efficiently organizing multidimensional datasets on tertiary storage. We cast the problem as permutation of the input data stream using limited storage space. The rest of this document is organized as follows: The problem is formulated in Section 2. Section 3 describes our approach. In Section 4, we present performance results. Section 5 presents conclusions.