52 research outputs found
Scalability analysis of declustering methods for multidimensional range queries
Abstract—Efficient storage and retrieval of multiattribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multiattribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. In this paper, we derive formulas describing the scalability of two popular declustering methods¦Disk Modulo and Fieldwise Xor¦for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in this paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Scalability Analysis of Declustering Methods for Cartesian Product Files
Efficient storage and retrieval of multi-attribute datasets
has become one of the essential requirements for many data-intensive
applications. The Cartesian product file has been known as an effective
multi-attribute file structure for partial-match and best-match queries.
Several heuristic methods have been developed to decluster Cartesian
product files over multiple disks to obtain high performance for disk
accesses. Though the scalability of the declustering methods becomes
increasingly important for systems equipped with a large number of disks,
no analytic studies have been done so far.
In this paper we derive formulas describing the scalability
of two popular declustering methods Disk Modulo and Fieldwise Xor
for range queries, which are the most common type of queries.
These formulas disclose the limited scalability of the declustering methods
and are corroborated by extensive simulation experiments.
From the practical point of view,
the formulas given in this paper provide a simple measure
which can be used to predict the response time of a given range query
and to guide the selection of a declustering method
under various conditions.
(Also cross-referenced as UMIACS-TR-96-5
Study of Scalable Declustering Algorithms for Parallel Grid Files
Efficient storage and retrieval of large multidimensional datasets is
an important concern for large-scale scientific computations such as
long-running time-dependent simulations which periodically generate
snapshots of the state.
The main challenge for efficiently handling such datasets
is to minimize response time for multidimensional range queries.
The grid file is one of the well known access methods for
multidimensional and spatial data.
We investigate effective and scalable declustering techniques
for grid files with the primary goal of minimizing response time
and the secondary goal of maximizing the fairness of data distribution.
The main contributions of this paper are (1) analytic and experimental
evaluation of existing index-based declustering techniques and their
extensions for grid files, and (2) development of a proximity-based
declustering algorithm called {\em minimax} which is experimentally
shown to scale and to consistently achieve better response time
compared to available algorithms while maintaining perfect disk distribution.
(Also cross-referenced as UMIACS-TR-96-4
Multidimensional Range Queries on Modern Hardware
Range queries over multidimensional data are an important part of database
workloads in many applications. Their execution may be accelerated by using
multidimensional index structures (MDIS), such as kd-trees or R-trees. As for
most index structures, the usefulness of this approach depends on the
selectivity of the queries, and common wisdom told that a simple scan beats
MDIS for queries accessing more than 15%-20% of a dataset. However, this wisdom
is largely based on evaluations that are almost two decades old, performed on
data being held on disks, applying IO-optimized data structures, and using
single-core systems. The question is whether this rule of thumb still holds
when multidimensional range queries (MDRQ) are performed on modern
architectures with large main memories holding all data, multi-core CPUs and
data-parallel instruction sets. In this paper, we study the question whether
and how much modern hardware influences the performance ratio between index
structures and scans for MDRQ. To this end, we conservatively adapted three
popular MDIS, namely the R*-tree, the kd-tree, and the VA-file, to exploit
features of modern servers and compared their performance to different flavors
of parallel scans using multiple (synthetic and real-world) analytical
workloads over multiple (synthetic and real-world) datasets of varying size,
dimensionality, and skew. We find that all approaches benefit considerably from
using main memory and parallelization, yet to varying degrees. Our evaluation
indicates that, on current machines, scanning should be favored over parallel
versions of classical MDIS even for very selective queries
Partial Replica Location And Selection For Spatial Datasets
As the size of scientific datasets continues to grow, we will not be able to store enormous datasets on a single grid node, but must distribute them across many grid nodes. The implementation of partial or incomplete replicas, which represent only a subset of a larger dataset, has been an active topic of research. Partial Spatial Replicas extend this functionality to spatial data, allowing us to distribute a spatial dataset in pieces over several locations. We investigate solutions to the partial spatial replica selection problems. First, we describe and develop two designs for an Spatial Replica Location Service (SRLS), which must return the set of replicas that intersect with a query region. Integrating a relational database, a spatial data structure and grid computing software, we build a scalable solution that works well even for several million replicas. In our SRLS, we have improved performance by designing a R-tree structure in the backend database, and by aggregating several queries into one larger query, which reduces overhead. We also use the Morton Space-filling Curve during R-tree construction, which improves spatial locality. In addition, we describe R-tree Prefetching(RTP), which effectively utilizes the modern multi-processor architecture. Second, we present and implement a fast replica selection algorithm in which a set of partial replicas is chosen from a set of candidates so that retrieval performance is maximized. Using an R-tree based heuristic algorithm, we achieve O(n log n) complexity for this NP-complete problem. We describe a model for disk access performance that takes filesystem prefetching into account and is sufficiently accurate for spatial replica selection. Making a few simplifying assumptions, we present a fast replica selection algorithm for partial spatial replicas. The algorithm uses a greedy approach that attempts to maximize performance by choosing a collection of replica subsets that allow fast data retrieval by a client machine. Experiments show that the performance of the solution found by our algorithm is on average always at least 91% and 93.4% of the performance of the optimal solution in 4-node and 8-node tests respectively
DISTRIBUTED MULTIDIMENSIONAL INDEXING FOR SCIENTIFIC DATA ANALYSIS APPLICATIONS
Scientific data analysis applications require large scale computing power to
effectively service client queries and also require large storage repositories
for datasets that are generated continually from sensors and simulations.
These scientific datasets are growing in size every day, and are becoming truly
enormous. The goal of this dissertation is to provide efficient multidimensional
indexing techniques that aid in navigating distributed scientific datasets.
In this dissertation, we show significant improvements in accessing
distributed large scientific datasets.
The first approach we took to improve access to subsets of large
multidimensional scientific datasets, was data chunking. The contents of
scientific data files typically are a collection of multidimensional arrays,
along with the corresponding metadata. Data chunking groups data elements into
small chunks of a fixed, but data-specific, size to take advantage of
spatio-temporal locality since it is not efficient to index individual data
elements of large scientific datasets.
The second approach was the design of an efficient multidimensional index for
scientific datasets. This work investigates how existing multidimensional
indexing structures perform on chunked scientific datasets, and compares their
performance with that of our own indexing structure, SH-trees. Since R-trees
were proposed, various multidimensional indexing structures have been proposed.
However, there are a relatively small number of studies focused on improving
the performance of indexing geographically distributed datasets, especially
across heterogeneous machines. As a third approach, in an attempt to
accelerate indexing performance for distributed datasets, we proposed several
distributed multidimensional indexing schemes: replicated centralized indexing,
hierarchical two level indexing, and decentralized two level indexing.
Our experimental results show that great performance improvements
are gained from distribution of multidimensional index. However, the design
choices for distributed indexing, such as replication, partitioning, and
decentralization, must be carefully considered since they may decrease the overall
performance in certain situations. Therefore, this work provides performance
guidelines to aid in selecting the best distributed multidimensional indexing
scheme for various systems and applications. Finally, we describe how a
distributed multidimensional indexing scheme can be used by a distributed
multiple query optimization middleware as a case-study application to
generate better query plans by leveraging information about the contents of
remote caches
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