20,767 research outputs found
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
Old Techniques for New Join Algorithms: A Case Study in RDF Processing
Recently there has been significant interest around designing specialized RDF
engines, as traditional query processing mechanisms incur orders of magnitude
performance gaps on many RDF workloads. At the same time researchers have
released new worst-case optimal join algorithms which can be asymptotically
better than the join algorithms in traditional engines. In this paper we apply
worst-case optimal join algorithms to a standard RDF workload, the LUBM
benchmark, for the first time. We do so using two worst-case optimal engines:
(1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype
research engine with enhanced worst-case optimal join algorithms. We show that
without any added optimizations both LogicBlox and EmptyHeaded outperform two
state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on
cyclic join queries-the queries where traditional optimizers are suboptimal. On
the remaining, less complex queries in the LUBM benchmark, we show that three
classic query optimization techniques enable EmptyHeaded to compete with RDF
engines, even when there is no asymptotic advantage to the worst-case optimal
approach. We validate that our design has merit as EmptyHeaded outperforms
MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude,
while remaining within an order of magnitude of RDF-3X and TripleBit
Flattening an object algebra to provide performance
Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent result
A Data Transformation System for Biological Data Sources
Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data
Algebraic optimization of recursive queries
Over the past few years, much attention has been paid to deductive databases. They offer a logic-based interface, and allow formulation of complex recursive queries. However, they do not offer appropriate update facilities, and do not support existing applications. To overcome these problems an SQL-like interface is required besides a logic-based interface.\ud
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In the PRISMA project we have developed a tightly-coupled distributed database, on a multiprocessor machine, with two user interfaces: SQL and PRISMAlog. Query optimization is localized in one component: the relational query optimizer. Therefore, we have defined an eXtended Relational Algebra that allows recursive query formulation and can also be used for expressing executable schedules, and we have developed algebraic optimization strategies for recursive queries. In this paper we describe an optimization strategy that rewrites regular (in the context of formal grammars) mutually recursive queries into standard Relational Algebra and transitive closure operations. We also describe how to push selections into the resulting transitive closure operations.\ud
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The reason we focus on algebraic optimization is that, in our opinion, the new generation of advanced database systems will be built starting from existing state-of-the-art relational technology, instead of building a completely new class of systems
Systematic investigation of methods for multiple freeform optimization in multi-lens imaging systems
With the development in freeform technology, it has now become more and more
feasible to use freeform surfaces in real system designs. While the freeform
surfaces helping optical designers achieve more and more challenging system
features, the methods for multiple freeform implementations are still
underdeveloped. We therefore investigate strategies to use freeform surfaces
properly in imaging optical systems with one Scheimpflug system and one
lithographic system. Based on the studies of the influences of the freeform
normalization radius, freeform order and system eccentricity, the methods of
determining the optimal location for implementing one freeform surface are
discussed. Different optimization strategies to optimize two freeform surfaces
are discussed to compare their resulting influences on the system performance.
On top of that, ways to implement more than one freeform surface in the optical
system is also investigated. In the end, a workflow is presented as guidance
for implementing multiple freeform surfaces with respect to system aberration
constitutions
Moa and the multi-model architecture: a new perspective on XNF2
Advanced non-traditional application domains such as geographic information systems and digital library systems demand advanced data management support. In an effort to cope with this demand, we present the concept of a novel multi-model DBMS architecture which provides evaluation of queries on complexly structured data without sacrificing efficiency. A vital role in this architecture is played by the Moa language featuring a nested relational data model based on XNF2, in which we placed renewed interest. Furthermore, extensibility in Moa avoids optimization obstacles due to black-box treatment of ADTs. The combination of a mapping of queries on complexly structured data to an efficient physical algebra expression via a nested relational algebra, extensibility open to optimization, and the consequently better integration of domain-specific algorithms, makes that the Moa system can efficiently and effectively handle complex queries from non-traditional application domains
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A survey of behavioral-level partitioning systems
Many approaches have been developed to partition a system's behavioral description before a structural implementation is synthesized. We highlight the foundations and motivations for behavioral partitioning. We survey behavioral partitioning approaches, discussing abstraction levels, goals, major steps, and key assumptions in each
AMaχoS—Abstract Machine for Xcerpt
Web query languages promise convenient and efficient access
to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web
query language with strong emphasis on novel high-level constructs for
effective and convenient query authoring, particularly tailored to versatile
access to data in different Web formats such as XML or RDF.
However, so far it lacks an efficient implementation to supplement the
convenient language features. AMaχoS is an abstract machine implementation
for Xcerpt that aims at efficiency and ease of deployment. It
strictly separates compilation and execution of queries: Queries are compiled
once to abstract machine code that consists in (1) a code segment
with instructions for evaluating each rule and (2) a hint segment that
provides the abstract machine with optimization hints derived by the
query compilation. This article summarizes the motivation and principles
behind AMaχoS and discusses how its current architecture realizes
these principles
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