39,398 research outputs found
Deductive Optimization of Relational Data Storage
Optimizing the physical data storage and retrieval of data are two key
database management problems. In this paper, we propose a language that can
express a wide range of physical database layouts, going well beyond the row-
and column-based methods that are widely used in database management systems.
We use deductive synthesis to turn a high-level relational representation of a
database query into a highly optimized low-level implementation which operates
on a specialized layout of the dataset. We build a compiler for this language
and conduct experiments using a popular database benchmark, which shows that
the performance of these specialized queries is competitive with a
state-of-the-art in memory compiled database system
An Evaluation of Physical Disk I/Os for Complex Object Processing
In order to obtain the performance required for nonstandard database environments, a hierarchical complex object model with object references is used as a storage structure for complex objects. Several storage models for these complex objects, as well as a benchmark to evaluate their performance, are described. A cost model for analytical performance evaluation is developed, and the analytical results are validated by means of measurements on the DASDBS, complex object storage system. The results show which storage structures for complex objects are the most efficient under which circumstance
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
A nested hybridizable discontinuous Galerkin method for computing second-harmonic generation in three-dimensional metallic nanostructures
In this paper, we develop a nested hybridizable discontinuous Galerkin (HDG)
method to numerically solve the Maxwell's equations coupled with the
hydrodynamic model for the conduction-band electrons in metals. By means of a
static condensation to eliminate the degrees of freedom of the approximate
solution defined in the elements, the HDG method yields a linear system in
terms of the degrees of freedom of the approximate trace defined on the element
boundaries. Furthermore, we propose to reorder these degrees of freedom so that
the linear system accommodates a second static condensation to eliminate a
large portion of the degrees of freedom of the approximate trace, thereby
yielding a much smaller linear system. For the particular metallic structures
considered in this paper, the resulting linear system obtained by means of
nested static condensations is a block tridiagonal system, which can be solved
efficiently. We apply the nested HDG method to compute the second harmonic
generation (SHG) on a triangular coaxial periodic nanogap structure. This
nonlinear optics phenomenon features rapid field variations and extreme
boundary-layer structures that span multiple length scales. Numerical results
show that the ability to identify structures which exhibit resonances at
and is paramount to excite the second harmonic response.Comment: 31 pages, 7 figure
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Optimization of Spatial Joins Using Filters
When viewing present-day technical applications that rely on the use of database systems, one notices that new techniques must be integrated in database management systems to be able to support these applications efficiently. This paper discusses one of these techniques in the context of supporting a Geographic Information System. It is known that the use of filters on geometric objects has a significant impact on the processing of 2-way spatial join queries. For this purpose, filters require approximations of objects. Queries can be optimized by filtering data not with just one but with several filters. Existing join methods are based on a combination of filters and a spatial index. The index is used to reduce the cost of the filter step and to minimize the cost of retrieving geometric objects from disk.
In this paper we examine n-way spatial joins. Complex n-way spatial join queries require solving several 2-way joins of intermediate results. In this case, not only the profit gained from using both filters and spatial indices but also the additional cost due to using these techniques are examined. For 2-way joins of base relations these costs are considered part of physical database design. We focus on the criteria for mutually comparing filters and not on those for spatial indices. Important aspects of a multi-step filter-based n-way spatial join method are described together with performance experiments. The winning join method uses several filters with approximations that are constructed by rotating two parallel lines around the object
Multi-Step Processing of Spatial Joins
Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in twodimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last yearâs conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidates which is handled by
the following two steps. First of all, sophisticated approximations
are used to identify answers as well as to filter out false hits from
the set of candidates. For this purpose, we investigate various types
of conservative and progressive approximations. In the last step, the
exact geometry of the remaining candidates has to be tested against
the join predicate. The time required for computing spatial join
predicates can essentially be reduced when objects are adequately
organized in main memory. In our approach, objects are first decomposed
into simple components which are exclusively organized
by a main-memory resident spatial data structure. Overall, we
present a complete approach of spatial join processing on complex
spatial objects. The performance of the individual steps of our approach
is evaluated with data sets from real cartographic applications.
The results show that our approach reduces the total execution
time of the spatial join by factors
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