Browsing by Subject "Data Structures"

Now showing 1 - 4 of 4

Open Access
Algorithms for efficient vectorization of repeated sparse power system network computations
(IEEE, 1995) Aykanat, Cevdet; Özgü, Ö.; Güven, N.
Standard sparsity-based algorithms used in power system appllcations need to be restructured for efficient vectorization due to the extremely short vectors processed. Further, intrinsic architectural features of vector computers such as chaining and sectioning should also be exploited for utmost performance. This paper presents novel data storage schemes and vectorization alsorim that resolve the recurrence problem, exploit chaining and minimize the number of indirect element selections in the repeated solution of sparse linear system of equations widely encountered in various power system problems. The proposed schemes are also applied and experimented for the vectorization of power mismatch calculations arising in the solution phase of FDLF which involves typical repeated sparse power network computations. The relative performances of the proposed and existing vectorization schemes are evaluated, both theoretically and experimentally on IBM 3090ArF.
Open Access
Efficient transient analysis of a class of compositional fluid stochastic petri nets
(IEEE, 2018) Buchholz, P.; Dayar, Tuğrul
Fluid Stochastic Petri Nets (FSPNs) which have discrete and continuous places are an established model class to describe and analyze several dependability problems for computer systems, software architectures or critical infrastructures. Unfortunately, their analysis is faced with the curse of dimensionality resulting in very large systems of differential equations for a sufficiently accurate analysis. This contribution introduces a class of FSPNs with a compositional structure and shows how the underlying stochastic process can be described by a set of coupled partial differential equations. Using semi discretization, a set of linear ordinary differential equations is generated which can be described by a (hierarchical) sum of Kronecker products. Based on this compact representation of the transition matrix, a numerical solution approach is applied which also represents transient solution vectors in compact form using the recently developed concept of a Hierarchical Tucker Decomposition. The applicability of the approach is presented in a case study analyzing a degrading software system with rejuvenation, restart, and replication.
Open Access
The expressive power of temporal relational query languages
(IEEE, 1997) Tansel, A. U.; Tin, E.
We consider the representation of temporal data based on tuple and attribute timestamping. We identify the requirements in modeling temporal data and elaborate on their implications in the expressive power of temporal query languages. We introduce a temporal relational data model where N1NF relations and attribute timestamping are used and one level of nesting is allowed. For this model, a nested relational tuple calculus (NTC) is defined. We follow a comparative approach in evaluating the expressive power of temporal query languages, using NTC as a metric and comparing it with the existing temporal query languages. We prove that NTC subsumes the expressive power of these query languages. We also demonstrate how various temporal relational models can be obtained from our temporal relations by NTC and give equivalent NTC expressions for their languages. Furthermore, we show the equivalence of intervals and temporal elements (sets) as timestamps in our model. © 1997 IEEE.
Open Access
A rule-based video database system architecture
(Elsevier, 2002) Dönderler, M. E.; Ulusoy, Özgür; Güdükbay, Uğur
We propose a novel architecture for a video database system incorporating both spatio-temporal and semantic (keyword, event/activity and category-based) query facilities. The originality of our approach stems from the fact that we intend to provide full support for spatio-temporal, relative object-motion and similarity-based object-trajectory queries by a rule-based system utilizing a knowledge-base while using an object-relational database to answer semantic-based queries. Our method of extracting and modeling spatio-temporal relations is also a unique one such that we segment video clips into shots using spatial relationships between objects in video frames rather than applying a traditional scene detection algorithm. The technique we use is simple, yet novel and powerful in terms of effectiveness and user query satisfaction: video clips are segmented into shots whenever the current set of relations between objects changes and the video frames, where these changes occur, are chosen as keyframes. The directional, topological and third-dimension relations used for shots are those of the keyframes selected to represent the shots and this information is kept, along with frame numbers of the keyframes, in a knowledge-base as Prolog facts. The system has a comprehensive set of inference rules to reduce the number of facts stored in the knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by rules with some extra effort. © 2002 Elsevier Science Inc. All rights reserved.