8,064 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
The Future is Big Graphs! A Community View on Graph Processing Systems
Graphs are by nature unifying abstractions that can leverage
interconnectedness to represent, explore, predict, and explain real- and
digital-world phenomena. Although real users and consumers of graph instances
and graph workloads understand these abstractions, future problems will require
new abstractions and systems. What needs to happen in the next decade for big
graph processing to continue to succeed?Comment: 12 pages, 3 figures, collaboration between the large-scale systems
and data management communities, work started at the Dagstuhl Seminar 19491
on Big Graph Processing Systems, to be published in the Communications of the
AC
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Benchmarking for Bayesian Reinforcement Learning
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.Comment: 37 page
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