394 research outputs found
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
From internationalization to global citizenship: Dialogues in international higher education
As we consider assessment and, by implication, graduation, the question of
what sort of graduate we are sending out into the world arises. A university
education is not simply more stuff than A-level: it is, we hope, part of the
transformation of a student into the adult they were always capable of
being, realizing their potential. But as our opening chapter argued, there
has to be a selection process for what is going to be emphasized: just being
knowledgeable is a recipe for narrowness, and for our new graduate to be
wrong-footed by a world that is far more complex than their university
life prepared them for. Universities were âglobalâ long before almost any
other ventures, with international collaboration on research going back
centuries; our students come from all over the world, and our graduates
go just about everywhere. We would be irresponsible not to consider how
best to prepare them for that fact, but it is not straightforward â there are
competing versions of what it is to be a âglobal citizenâ, as this chapter
explores
A Cost-based Optimizer for Gradient Descent Optimization
As the use of machine learning (ML) permeates into diverse application
domains, there is an urgent need to support a declarative framework for ML.
Ideally, a user will specify an ML task in a high-level and easy-to-use
language and the framework will invoke the appropriate algorithms and system
configurations to execute it. An important observation towards designing such a
framework is that many ML tasks can be expressed as mathematical optimization
problems, which take a specific form. Furthermore, these optimization problems
can be efficiently solved using variations of the gradient descent (GD)
algorithm. Thus, to decouple a user specification of an ML task from its
execution, a key component is a GD optimizer. We propose a cost-based GD
optimizer that selects the best GD plan for a given ML task. To build our
optimizer, we introduce a set of abstract operators for expressing GD
algorithms and propose a novel approach to estimate the number of iterations a
GD algorithm requires to converge. Extensive experiments on real and synthetic
datasets show that our optimizer not only chooses the best GD plan but also
allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201
Evaluation of an eBook for Oral Health Literacy© to Promote Child Health: Readability, Suitability, Understandability, Actionability, and Gist-Based Message
The purpose of the study was to evaluate an oral health curriculum called an eBook for Oral Health Literacy© to determine its effectiveness for promoting child health. A secondary purpose was to describe and explain the design characteristics of readability, suitability, understandability, and actionability of the 17 chapters of the eBook. A third purpose was to conduct evaluations on verbatim representations (or literal facts) that are presented in the eBook chapters, including the gist representations that are not explicitly presented but inferred by the reader from the chapter information. Results found that the eBook for Oral Health Literacy© had acceptable, and in many cases, favorable scores, for the five design elements of readability, suitability, understandability, actionability, and gist comprehension. Ongoing dissemination of the eBook for Oral Health Literacy© curriculum has the potential to boost children who are âlearning to readâ and âreading to learnâ about oral health hygiene and nutrition. Future studies should use one or more chapters from the curriculum as an intervention to test this educational premise as an explanatory basis for functional health literacy
LINVIEW: Incremental View Maintenance for Complex Analytical Queries
Many analytics tasks and machine learning problems can be naturally expressed
by iterative linear algebra programs. In this paper, we study the incremental
view maintenance problem for such complex analytical queries. We develop a
framework, called LINVIEW, for capturing deltas of linear algebra programs and
understanding their computational cost. Linear algebra operations tend to cause
an avalanche effect where even very local changes to the input matrices spread
out and infect all of the intermediate results and the final view, causing
incremental view maintenance to lose its performance benefit over
re-evaluation. We develop techniques based on matrix factorizations to contain
such epidemics of change. As a consequence, our techniques make incremental
view maintenance of linear algebra practical and usually substantially cheaper
than re-evaluation. We show, both analytically and experimentally, the
usefulness of these techniques when applied to standard analytics tasks. Our
evaluation demonstrates the efficiency of LINVIEW in generating parallel
incremental programs that outperform re-evaluation techniques by more than an
order of magnitude.Comment: 14 pages, SIGMO
Consistency in scalable systems
[EN] While eventual consistency is the general consistency guarantee ensured in cloud environments, stronger guarantees are in fact achievable. We show how scalable and highly available systems can provide processor, causal, sequential and session consistency during normal functioning. Failures and network partitions negatively affect consistency and generate divergence. After the failure or the partition, reconciliation techniques allow the system to restore consistency.This work has been supported by EU FEDER and Spanish MICINN under research grants TIN2009-14460-C03-01 and TIN2010-17193.Ruiz Fuertes, MI.; PallardĂł Lozoya, MR.; Muñoz-EscoĂ, FD. (2012). Consistency in scalable systems. En On the Move to Meaningful Internet Systems: OTM 2012. Springer Verlag (Germany). 7566:549-565. https://doi.org/10.1007/978-3-642-33615-7_7S5495657566Ahamad, M., Bazzi, R.A., John, R., Kohli, P., Neiger, G.: The power of processor consistency. 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