Implementing data structures on a hypercube multiprocessor, and applications in parallel computational geometry

In this paper, we study the problem of implementing standard data structures on a hypercube multiprocessor. We present a technique for efficiently executing multiple independent search processes on a class of graphs called ordered h-level graphs. We show how this technique can be utilized to implement a segment tree on a hypercube, thereby obtaining O(long2n) time algorithms for solving the next element search problem, the trapezoidal composition problem, and the triangulation problem

Implementing data structures on a hypercube multiprocessor, and applications in parallel computational geometry

In this paper, we study the problem of implementing standard data structures on a hypercube multiprocessor. We present a technique for efficiently executing multiple independent search processes on a class of graphs called ordered h-level graphs. We show how this technique can be utilized to implement a segment tree on a hypercube, thereby obtaining O(long2n) time algorithms for solving the next element search problem, the trapezoidal composition problem, and the triangulation problem

RCUBE: Parallel multi-dimensional ROLAP indexing

This article addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present RCUBE, a distributed multidimensional ROLAP indexing scheme which is practical to implement, requires only a small communication volume, and is fully adapted to distributed disks. Our solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Our method is also incrementally maintainable. Using "surrogate" group-bys, it allows for the efficient processing of arbitrary OLAP queries on partial cubes, where not all of the group-bys have been materialized. Our experiments with RCUBE show that the ROLAP advantage of better scalability, in comparison to MOLAP, can be maintained while providing a fast and flexible index for OLAP queries. Copyrigh

The cgmCUBE project: Optimizing parallel data cube generation for ROLAP

On-line Analytical Processing (OLAP) has become one of the most powerful and prominent technologies for knowledge discovery in VLDB (Very Large Database) environments. Central to the OLAP paradigm is the data cube, a multi-dimensional hierarchy of aggregate values that provides a rich analytical model for decision support. Various sequential algorithms for the efficient generation of the data cube have appeared in the literature. However, given the size of contemporary data warehousing repositories, multi-processor solutions are crucial for the massive computational demands of current and future OLAP systems. In this paper we discuss the cgmCUBE Project, a multi-year effort to design and implement a multi-processor platform for data cube generation that targets the relational database model (ROLAP). More specifically, we discuss new algorithmic and system optimizations relating to (1) a thorough optimization of the underlying sequential cube construction method and (2) a detailed and carefully engineered cost model for improved parallel load balancing and faster sequential cube construction. These optimizations were key in allowing us to bu

Computing partial data cubes for parallel data warehousing applications

In this paper, we focus on an approach to On-Line Analytical Processing (OLAP) that is based on a database operator and data structure called the datacube. The datacube is a relational operator that is used to construct all possible views of a given data set. Efficient algorithms for computing the entire datacube – both sequentially and in parallel – have recently been proposed. However, due to space and time constraints, the assumption that all 2d (where d = dimensions) views should be computed is often not valid in practice. As a result, algorithms for computing partial datacubes are required. In this paper, we describe a parallel algorithm for computing partial datacubes and provide preliminary experimental results based on an implementation in C and MPI

Parallel querying of ROLAP cubes in the presence of Hierarchies

Online Analytical Processing is a powerful framework for the analysis of organizational data. OLAP is often supported by a logical structure known as a data cube, a multidimen-sional data model that offers an intuitive array-based per-spective of the underlying data. Supporting efficient index-ing facilities for multi-dimensional cube queries is an issue of some complexity. In practice, the difficulty of the in-dexing problem is exacerbated by the existence of attribute hierarchies that sub-divide attributes into aggregation layers of varying granularity. In this paper, we present a hierar-chy and caching framework that supports the efficient and transparent manipulation of attribute hierarchies within a parallel ROLAP environment. Experimental results verify that, when compared to the non-hierarchical case, very little overhead is required to handle streams of arbitrary hierar-chical queries