16226748
Polar: an architecture for a parallel ODMG compliant object database
Object database management systems (ODBMS) are now established as the database management technology of choice for a range of challenging data intensiv e applications. Furthermore, the applications associated with object databases typically have stringen t performance requirements, and some are associated with very large data sets. Ho w ever, despite the demands made on object databases by applications, there has been surprisingly little work on parallel object databases. This paper presents the arc hitecture and some preliminary performance results for the Polar ODMG compliant parallel object database. The architecture described has been implemented in a shared-nothing environment on a network of PCs. The paper describes how OQL queries are compiled, parallelized and executed in this environment, and includes some preliminary performance results for OQL queries using the 007 benchmark.
13121681
A parallel algebra for object databases
The paper describes an algebra for use with parallel object databases, and in particular ODMG compliant databases with OQL. Although there have been many proposals for parallel relational database systems, there has been much less work on parallel object databases, and on parallel query processing for object databases. The parallel algebra presented in the paper is an extension of an existing physical algebra for OQL, and has an important role during query optimization, and for describing execution plans. The paper presents not only the algebra, but also its role in the architecture of a parallel database.
18173287
Performance of Fault-Tolerant Data and Compute Intensive Programs over a Network of Workstations
A well-known structuring technique for a wide class of parallel applications is the bag of tasks, which allows a computation to be partitioned dynamically between a collection of concurrent processes. This paper describes a fault-tolerant implementation of this structure using atomic actions (atomic transactions) to operate on persistent objects, which are accessed in a distributed setting via a Remote Procedure Call (RPC). The system developed is suited to parallel execution of data and compute intensive programs that require persistent storage and fault tolerance facilities. The suitability of the system is examined in the context of the measured performance of three specific applications; ray tracing, matrix multiplication and Cholesky factorisation. The measured performance compares well with that predicted by analysis of the applications based on benchmark measurement of hardware parameters. The same analysis allows modelling of hardware upgrades.
37610017
Validated Cost Models for Parallel OQL Query Processing
Query cost models are widely used, both for performance analysis and for comparing execution plans during query optimisation. In essence, a cost modelp redicts where time is being spent during query evaluation. Although many cost models have been proposed, for serial, parallel and distributed database systems, surprisingly few of these have been validated against real systems. This paper presents cost models for the parallel evaluation of ODMG OQL queries, which have been compared with experimental results obtained using the Polar object database system. The paper describes the validation of the cost model for a collection of queries, using three join algorithms over the OO7 benchmark database. The results show that the cost model generally both ranks alternative plans appropriately, and gives a useful indication of the response times that can be expected from a plan. The paper also illustrates the application of the cost model by highlighting the contributions of different features and operations to query response times.
2339969
Interactive search fusion methods for video database retrieval
In this paper, we investigate a new method for video database retrieval using interactive search fusion. Recent video analysis techniques have enabled the extraction of a variety of descriptors of features, concepts, clusters, classification results, speech and textual terms, MPEG-7 metadata, and so on. However, given an information need users are faced with a daunting task of trying to formulate queries over these multiple disparate data sources in order to retrieve the desired video content. In this paper, we explore a novel approach based on search fusion in which the user interactively builds a query by sequentially choosing among the descriptors and data sources and by selecting from various combining and score aggregation functions to fuse results of individual searches. For example, the system allows building of queries such as "retrieve video clips that have color of beach scenes, the detection of sky, and detection of water". In this paper we present the search fusion method and evaluate the performance on a large video database.
2766632
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning “Gene Linkage” within the context of genetic optimisation, that is to say, the problem of learning which groups of co-adapted genes should be inherited together during the recombination process. These may be seen within a wider context as a search for appropriate relations which delineate the search space and “guide” heuristic optimisation, or, alternatively, as a part of a comprehensive body of work into Adaptive Evolutionary Algorithms.In this paper, we consider the learning of Gene Linkage as an emergent property of adaptive recombination operators. This is in contrast to the behaviour observed with fixed recombination strategies in which there is no correspondence between the sets of genes which are inherited together between generations, other than that caused by distributional bias. A discrete mathematical model of Gene Linkage is introduced, and the common families of recombination operators, along with some well known linkage-learning algorithms, are modelled within this framework. This model naturally leads to the specification of a recombination operator that explicitly operates on sets of linked genes.Variants of that algorithm, are then used to examine one of the important concepts from the study of adaptivity in Evolutionary Algorithms, namely that of the level (population, individual, or component) at which learning takes place. This is an aspect of adaptation which has received considerable attention when applied to mutation operators, but which has been paid little attention in the context of adaptive recombination operators and linkage learning. It is shown that even with the problem restricted to learning adjacent linkage, the population based variants are not capable of correctly identifying building blocks. This is in contrast to component level adaptation which outperforms conventional operators whose bias is ideal for the problems considered.
32676071
Exploring semantic dependencies for scalable concept detection
Semantic concept detection from multimedia features enables high-level access to multimedia content. While constructing robust detectors is feasible for concepts with sufficient training samples, concepts with fewer training samples are hard to train efficiently. Comparable performance may be possible if the dependence of these concepts on the ones that can be robustly modeled is exploited. In this paper we show this phenomenon using the TREC Video 2002 Corpus as a test bed. Using a basic set of 12 semantic concepts modeled with support vector machines, we predict presence of 4 other concepts. We then compare the performance of these predictors with direct SVM models for these 4 concepts and observe improvements of up to 150% in average precision.
33110075
Report of the Purdue Workshop on Grand Challenges in Computer Architecture for the Support of High Performance Computing
Abstract The “Purdue Workshop on Grand Challenges in Computer Architecture for the Support of High Performance Computing” was sponsored by the National Science Foundation to identify critical research topics in computer architecture as they relate to high performance computing. Following a wide-ranging discussion of the computational characteristics and requirements of the grand challenge applications, the workshop identified four major computer architecture grand challenges as crucial to advancing the state of the art of high performance computation in the coming decade. These are: (1) idealized parallel computer models; (2) usable peta-ops (1015 ops) performance; (3) computers in an era of HDTV, gigabyte networks, and visualization; and (4) infrastructure for prototyping architectures. This report overviews some of the demands of the grand challenge applications and presents the above four grand challenges for computer architecture.
6609571
Efficient Techniques for Feature-Based Image/Video Access and Manipulation
Efficient, effective methods for indexing, searching, and retrieving images and videos from large archives are critical techniques required in visual information systems (VIS) applications. Users need a more powerful method for searching images than just traditional text-based query (e.g., keywords). Manual creation of keywords is too time-consuming for many practical applications. Subjective descriptions based on users’ input will be neither consistent nor complete. Also, the vocabulary used in describing visual contents is usually domain specific.