One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast PendseB. On one hand, speed can be achieved by specially devised data structures and algorithms. On the other hand, the analytical process is cyclic. In other words, the user of the OLAP application runs his or her queries one after the other. The output of the last query may be there (at least partly) in one of the previous results. Therefore caching also plays an important role in the operation of these systems. However, caching itself may not be enough to ensure acceptable performance. Size does matter: The more memory is available, the more we gain by loading and keeping information in there. Oftentimes, the cache size is fixed. This limits the performance of the multidimensional database, as well, unless we compress the data in order to move a greater proportion of them into the memory. Caching combined with proper compression methods promise further performance improvements. In this paper, we investigate how caching influences the speed of OLAP systems. Different physical representations (multidimensional and table) are evaluated. For the thorough comparison, models are proposed. We draw conclusions based on these models, and the conclusions are verified with empirical data.
When we analyse algorithms that operate on data on the secondary storage, we usually investigate how many disk input output (I O) operations are performed. This is because we follow the rule @cite_19 . We followed a similar approach in Section below.
@cite_19
2149509970 2063730794 2070593325 1474119323
In today's computer systems, the disk I O subsystem is often identified as the major bottleneck to system performance. One proposed solution is the so called redundant array of inexpensive disks (RAID). We examine the performance of two of the most promising RAID architectures, the mirrored array and the rotated parity array. First, we propose several scheduling policies for the mirrored array and a new data layout, group-rotate declustering, and compare their performance with each other and in combination with other data layout schemes. We observe that a policy that routes reads to the disk with the smallest number of requests provides the best performance, especially when the load on the I O system is high. Second, through a combination of simulation and analysis, we compare the performance of this mirrored array architecture to the rotated parity array architecture. This latter study shows that: 1) given the same storage capacity (approximately double the number of disks), the mirrored array considerably outperforms the rotated parity array; and 2) given the same number of disks, the mirrored array still outperforms the rotated parity array in most cases, even for applications where I O requests are for large amounts of data. The only exception occurs when the I O size is very large; most of the requests are writes, and most of these writes perform full stripe write operations. We study an optimization problem that arises in the context of data placement in a multimedia storage system. We are given a collection of M multimedia objects (data items) that need to be assigned to a storage system consisting of N disks d1, d2,...,dN. We are also given sets U1, U2,...,UM such that Ui is the set of clients seeking the ith data item. Data item i has size si. Each disk dj is characterized by two parameters, namely, its storage capacity Cj which indicates the maximum total size of data items that may be assigned to it, and a load capacity Lj which indicates the maximum number of clients that it can serve. The goal is to find a placement of data items to disks and an assignment of clients to disks so as to maximize the total number of clients served, subject to the capacity constraints of the storage system.We study this data placement problem for homogeneous storage systems where all the disks are identical. We assume that all disks have a storage capacity of k and a load capacity of L. Previous work on this problem has assumed that all data items have unit size, in other words si = 1 for all i. Even for this case, the problem is NP-hard. For the case where si ∈ 1,..., Δ for some constant Δ, we develop a polynomial time approximation scheme (PTAS). This result is obtained by developing two algorithms, one that works for constant k and one that works for arbiuary k. The algorithm for arbitrary k guarantees that a solution where at least ((k-Δ) (k + Δ))(1 - 1 (1 + √k (2Δ))2)- fraction of all clients are assigned to a disk (under certain assumptions). In addition we develop an algorithm for which we can prove tight bounds when si ∈ 1,2 . In fact, we can show that a (1 - 1 (1 + √⌊k 2⌋)2)-fraction of all clients can be assigned (under certain natural assumptions), regardless of the input distribution. Existing main memory data processing systems employ a variety of storage organizations and make a number of storage-related design choices. The focus of this paper is on systematically evaluating a number of these key storage design choices for main memory analytical (i.e. read-optimized) database settings. Our evaluation produces a number of key insights: First, it is always beneficial to organize data into self-contained memory blocks rather than large files. Second, both column-stores and row-stores display performance advantages for different types of queries, and for high performance both should be implemented as options for the tuple-storage layout. Third, cache-sensitive B+-tree indices can play a major role in accelerating query performance, especially when used in a block-oriented organization. Finally, compression can also play a role in accelerating query performance depending on data distribution and query selectivity. We present a large scale study of primary data deduplication and use the findings to drive the design of a new primary data deduplication system implemented in the Windows Server 2012 operating system. File data was analyzed from 15 globally distributed file servers hosting data for over 2000 users in a large multinational corporation. The findings are used to arrive at a chunking and compression approach which maximizes deduplication savings while minimizing the generated metadata and producing a uniform chunk size distribution. Scaling of deduplication processing with data size is achieved using a RAM frugal chunk hash index and data partitioning - so that memory, CPU, and disk seek resources remain available to fulfill the primary workload of serving IO. We present the architecture of a new primary data deduplication system and evaluate the deduplication performance and chunking aspects of the system.