162,851 research outputs found
Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer
The D-Wave adiabatic quantum annealer solves hard combinatorial optimization
problems leveraging quantum physics. The newest version features over 1000
qubits and was released in August 2015. We were given access to such a machine,
currently hosted at NASA Ames Research Center in California, to explore the
potential for hard optimization problems that arise in the context of
databases.
In this paper, we tackle the problem of multiple query optimization (MQO). We
show how an MQO problem instance can be transformed into a mathematical formula
that complies with the restrictive input format accepted by the quantum
annealer. This formula is translated into weights on and between qubits such
that the configuration minimizing the input formula can be found via a process
called adiabatic quantum annealing. We analyze the asymptotic growth rate of
the number of required qubits in the MQO problem dimensions as the number of
qubits is currently the main factor restricting applicability. We
experimentally compare the performance of the quantum annealer against other
MQO algorithms executed on a traditional computer. While the problem sizes that
can be treated are currently limited, we already find a class of problem
instances where the quantum annealer is three orders of magnitude faster than
other approaches
Application of shape grammar theory to underground rail station design and passenger evacuation
This paper outlines the development of a computer design environment that generates station ‘reference’ plans for analysis by designers at the project feasibility stage. The developed program uses the theoretical concept of shape grammar, based upon principles of recognition and replacement of a particular shape to enable the generation of station layouts. The developed novel shape grammar rules produce multiple plans of accurately sized infrastructure faster than by traditional means. A finite set of station infrastructure elements and a finite set of connection possibilities for them, directed by regulations and the logical processes of station usage, allows for increasingly complex composite shapes to be automatically produced, some of which are credible station layouts at ‘reference’ block plan level. The proposed method of generating shape grammar plans is aligned to London Underground standards, in particular to the Station Planning Standards and Guidelines 5th edition (SPSG5 2007) and the BS-7974 fire safety engineering process. Quantitative testing is via existing evacuation modelling software. The prototype system, named SGEvac, has both the scope and potential for redevelopment to any other country’s design legislation
Learning Effective Changes for Software Projects
The primary motivation of much of software analytics is decision making. How
to make these decisions? Should one make decisions based on lessons that arise
from within a particular project? Or should one generate these decisions from
across multiple projects? This work is an attempt to answer these questions.
Our work was motivated by a realization that much of the current generation
software analytics tools focus primarily on prediction. Indeed prediction is a
useful task, but it is usually followed by "planning" about what actions need
to be taken. This research seeks to address the planning task by seeking
methods that support actionable analytics that offer clear guidance on what to
do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set
of actionable plans within and across projects. Each of these plans, if
followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu
Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution
Cloud-based data analysis is nowadays common practice because of the lower
system management overhead as well as the pay-as-you-go pricing model. The
pricing model, however, is not always suitable for query processing as heavy
use results in high costs. For example, in query-as-a-service systems, where
users are charged per processed byte, collections of queries accessing the same
data frequently can become expensive. The problem is compounded by the limited
options for the user to optimize query execution when using declarative
interfaces such as SQL. In this paper, we show how, without modifying existing
systems and without the involvement of the cloud provider, it is possible to
significantly reduce the overhead, and hence the cost, of query-as-a-service
systems. Our approach is based on query rewriting so that multiple concurrent
queries are combined into a single query. Our experiments show the aggregated
amount of work done by the shared execution is smaller than in a
query-at-a-time approach. Since queries are charged per byte processed, the
cost of executing a group of queries is often the same as executing a single
one of them. As an example, we demonstrate how the shared execution of the
TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google
BigQuery than using a query-at-a-time approach while achieving a higher
throughput
- …