13,050 research outputs found
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages
Extracting geographical tags from webpages is a well-motivated application in
many domains. In illicit domains with unusual language models, like human
trafficking, extracting geotags with both high precision and recall is a
challenging problem. In this paper, we describe a geotag extraction framework
in which context, constraints and the openly available Geonames knowledge base
work in tandem in an Integer Linear Programming (ILP) model to achieve good
performance. In preliminary empirical investigations, the framework improves
precision by 28.57% and F-measure by 36.9% on a difficult human trafficking
geotagging task compared to a machine learning-based baseline. The method is
already being integrated into an existing knowledge base construction system
widely used by US law enforcement agencies to combat human trafficking.Comment: 6 pages, GeoRich 2017 workshop at ACM SIGMOD conferenc
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Introducing new constructs for data modelling and column generation in LP modelling languages
Through popular implementation of structured query language (SQL) and query-by-example(QBE) relational databases have become the de-facto industry standard for data modelling.We consider the indices, sets, and the declarative form of Linear Programming (LP) modelling languages and introduce new constructs which provide direct link to the database systems. The models constructed in this way are data driven and display a dynamicstructure. We then show how this approach can be naturally extended to include column generation features stated in procedural forms within an otherwise declarative modelling paradigm
Sets and indices in linear programming modelling and their integration with relational data models
LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases
Computer-aided verification in mechanism design
In mechanism design, the gold standard solution concepts are dominant
strategy incentive compatibility and Bayesian incentive compatibility. These
solution concepts relieve the (possibly unsophisticated) bidders from the need
to engage in complicated strategizing. While incentive properties are simple to
state, their proofs are specific to the mechanism and can be quite complex.
This raises two concerns. From a practical perspective, checking a complex
proof can be a tedious process, often requiring experts knowledgeable in
mechanism design. Furthermore, from a modeling perspective, if unsophisticated
agents are unconvinced of incentive properties, they may strategize in
unpredictable ways.
To address both concerns, we explore techniques from computer-aided
verification to construct formal proofs of incentive properties. Because formal
proofs can be automatically checked, agents do not need to manually check the
properties, or even understand the proof. To demonstrate, we present the
verification of a sophisticated mechanism: the generic reduction from Bayesian
incentive compatible mechanism design to algorithm design given by Hartline,
Kleinberg, and Malekian. This mechanism presents new challenges for formal
verification, including essential use of randomness from both the execution of
the mechanism and from the prior type distributions. As an immediate
consequence, our work also formalizes Bayesian incentive compatibility for the
entire family of mechanisms derived via this reduction. Finally, as an
intermediate step in our formalization, we provide the first formal
verification of incentive compatibility for the celebrated
Vickrey-Clarke-Groves mechanism
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