30,959 research outputs found
Assessing XML Data Management with XMark
We discuss some of the experiences we gathered during the development and deployment of XMark, a tool to assess the infrastructure and performance of XML Data Management Systems. Since the appearance of the first XML database prototypes in research institutions and development labs, topics like validation, performance evaluation and optimization of XML query processors have received significant interest. The XMark benchmark follows a tradition in database research and provides a framework to assess the abilities and performance of XML processing system: it helps users to see how a query component integrates into an application and how it copes with a variety of query types that are typically encountered in real-world scenarios. To this end, XMark offers an application scenario and a set of queries; each query is intended to challenge a particular aspect of the query processor like the performance of full-text search combined with structural information or joins. Furthermore, we have designed and made available a benchmark document generator that allows for efficient generation of databases of different sizes ranging from small to very large. In short, XMark attempts to cover the major aspects of XML query processing ranging from small to large document and from textual queries to data analysis and ad hoc queries
Content-based Image Retrieval by Information Theoretic Measure
Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.Defence Science Journal, 2011, 61(5), pp.415-430, DOI:http://dx.doi.org/10.14429/dsj.61.117
The Train Benchmark: cross-technology performance evaluation of continuous model queries
In model-driven development of safety-critical
systems (like automotive, avionics or railways), well-
formedness of models is repeatedly validated in order to
detect design flaws as early as possible. In many indus-
trial tools, validation rules are still often implemented by
a large amount of imperative model traversal code which
makes those rule implementations complicated and hard
to maintain. Additionally, as models are rapidly increas-
ing in size and complexity, efficient execution of validation rules is challenging for the currently available tools.
Checking well-formedness constraints can be captured by
declarative queries over graph models, while model update
operations can be specified as model transformations. This
paper presents a benchmark for systematically assessing the
scalability of validating and revalidating well-formedness
constraints over large graph models. The benchmark defines
well-formedness validation scenarios in the railway domain:
a metamodel, an instance model generator and a set of well-
formedness constraints captured by queries, fault injection
and repair operations (imitating the work of systems engi-
neers by model transformations). The benchmark focuses
on the performance of query evaluation, i.e. its execution
time and memory consumption, with a particular empha-
sis on reevaluation. We demonstrate that the benchmark
can be adopted to various technologies and query engines,
including modeling tools; relational, graph and semantic
databases. The Train Benchmark is available as an open-
source project with continuous builds from
https://github.
com/FTSRG/trainbenchmark
Towards a Scalable Dynamic Spatial Database System
With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.Comment: (2012
Experiences with Some Benchmarks for Deductive Databases and Implementations of Bottom-Up Evaluation
OpenRuleBench is a large benchmark suite for rule engines, which includes
deductive databases. We previously proposed a translation of Datalog to C++
based on a method that "pushes" derived tuples immediately to places where they
are used. In this paper, we report performance results of various
implementation variants of this method compared to XSB, YAP and DLV. We study
only a fraction of the OpenRuleBench problems, but we give a quite detailed
analysis of each such task and the factors which influence performance. The
results not only show the potential of our method and implementation approach,
but could be valuable for anybody implementing systems which should be able to
execute tasks of the discussed types.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014
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