16693115
An Experimental Performance Evaluation of Join Algorithms for Parallel Object Databases
Parallel relational databases have been successful in providing scalable performance for data intensive applications, and much work has been carried out on query processing techniques in such systems. However, although many applications associated with object databases also have stringent performance requirements, there has been much less work investigating parallel object database systems. An important feature for the performance of object databases is the speed at which relationships can be explored. In queries, this depends upon the effectiveness of different join algorithms into which queries that follow relationships can be compiled. This paper presents the results of empirical evaluations of four parallel join algorithms, two value based and two pointer based. The experiments have been run on Polar, a parallel ODMG object database system.
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.
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.
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.
16057397
Measuring and modelling the performance of a parallel ODMG compliant object database server
Object database management systems (ODBMSs) are now established as the database management technology of choice for a range of challenging data intensive applications. Furthermore, the applications associated with object databases typically have stringent performance requirements, and some are associated with very large data sets. An important feature for the performance of object databases is the speed at which relationships can be explored. In queries, this depends on the effectiveness of different join algorithms into which queries that follow relationships can be compiled. This paper presents a performance evaluation of the Polar parallel object database system, focusing in particular on the performance of parallel join algorithms. Polar is a parallel, shared-nothing implementation of the Object Database Management Group (ODMG) standard for object databases. The paper presents an empirical evaluation of queries expressed in the ODMG Query Language (OQL), as well as a cost model for the parallel algebra that is used to evaluate OQL queries. The cost model is validated against the empirical results for a collection of queries using four different join algorithms, one that is value based and three that are pointer based. Copyright © 2005 John Wiley & Sons, Ltd.
1492870
Dynamic Resource Allocation Heuristics for Maximizing Robustness with an Overall Makespan Constraint in an Uncertain Environment
Heterogeneous parallel and distributed computing systems may operate in an environment where certain system performance features degrade due to unpredictable circumstances. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. This work uses a mathematic expression of robustness for a dynamic environment where task execution time estimates are known to contain errors. Several heuristic solutions to the problem are presented that utilize this expression of robustness to influence mapping decisions. These solutions are then compared to a bound on the highest attainable robustness of the described system.
30784259
Semilattice-based dualities
The paper discusses “regularisation” of dualities. A given duality between (concrete) categories, e.g. a variety of algebras and a category of representation spaces, is lifted to a duality between the respective categories of semilattice representations in the category of algebras and the category of spaces. In particular, this gives duality for the regularisation of an irregular variety that has a duality. If the type of the variety includes constants, then the regularisation depends critically on the location or absence of constants within the defining identities. The role of schizophrenic objects is discussed, and a number of applications are given. Among these applications are different forms of regularisation of Priestley, Stone and Pontryagin dualities.
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.
5116030
Automated Recovery in a Secure Bootstrap Process
Integrity is rarely a valid presupposition in many systems architectures, yet it is necessary to make any security guarantees. To address this problem, we have designed a secure bootstrap process, AEGIS, which presumes a minimal amount of integrity, and which we have prototyped on the Intel x86 architecture. The basic principle is sequencing the bootstrap process as a chain of progressively higher levels of abstraction, and requiring each layer to check a digital signature of the next layer before control is passed to it. A major design decision is the consequence of a failed integrity check. A simplistic strategy is to simply halt the bootstrap process. However, as we show in this paper, the AEGIS bootstrap process can be augmented with automated recovery procedures which preserve the security properties of AEGIS under the additional assumption of the availability of a trusted repository. We describe two means by which such a repository can be implemented, and focus our attention on a network-accessible repository.
214797878
The onion technique: indexing for linear optimization queries
This paper describes the Onion technique, a special indexing structure for linear optimization queries. Linear optimization queries ask for top-N records subject to the maximization or minimization of linearly weighted sum of record attribute values. Such query appears in many applications employing linear models and is an effective way to summarize representative cases, such as the top-50 ranked colleges. The Onion indexing is based on a geometric property of convex hull, which guarantees that the optimal value can always be found at one or more of its vertices. The Onion indexing makes use of this property to construct convex hulls in layers with outer layers enclosing inner layers geometrically. A data record is indexed by its layer number or equivalently its depth in the layered convex hull. Queries with linear weightings issued at run time are evaluated from the outmost layer inwards. We show experimentally that the Onion indexing achieves orders of magnitude speedup against sequential linear scan when N is small compared to the cardinality of the set. The Onion technique also enables progressive retrieval, which processes and returns ranked results in a progressive manner. Furthermore, the proposed indexing can be extended into a hierarchical organization of data to accommodate both global and local queries.