5,418 research outputs found
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
Geographica: A Benchmark for Geospatial RDF Stores
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently
been defined and corresponding geospatial RDF stores have been implemented.
However, there is no widely used benchmark for evaluating geospatial RDF stores
which takes into account recent advances to the state of the art in this area.
In this paper, we develop a benchmark, called Geographica, which uses both
real-world and synthetic data to test the offered functionality and the
performance of some prominent geospatial RDF stores
XWeB: the XML Warehouse Benchmark
With the emergence of XML as a standard for representing business data, new
decision support applications are being developed. These XML data warehouses
aim at supporting On-Line Analytical Processing (OLAP) operations that
manipulate irregular XML data. To ensure feasibility of these new tools,
important performance issues must be addressed. Performance is customarily
assessed with the help of benchmarks. However, decision support benchmarks do
not currently support XML features. In this paper, we introduce the XML
Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from
the relational decision support benchmark TPC-H. It is mainly composed of a
test data warehouse that is based on a unified reference model for XML
warehouses and that features XML-specific structures, and its associate XQuery
decision support workload. XWeB's usage is illustrated by experiments on
several XML database management systems
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