63,252 research outputs found
Declarative Ajax Web Applications through SQL++ on a Unified Application State
Implementing even a conceptually simple web application requires an
inordinate amount of time. FORWARD addresses three problems that reduce
developer productivity: (a) Impedance mismatch across the multiple languages
used at different tiers of the application architecture. (b) Distributed data
access across the multiple data sources of the application (SQL database, user
input of the browser page, session data in the application server, etc). (c)
Asynchronous, incremental modification of the pages, as performed by Ajax
actions.
FORWARD belongs to a novel family of web application frameworks that attack
impedance mismatch by offering a single unifying language. FORWARD's language
is SQL++, a minimally extended SQL. FORWARD's architecture is based on two
novel cornerstones: (a) A Unified Application State (UAS), which is a virtual
database over the multiple data sources. The UAS is accessed via distributed
SQL++ queries, therefore resolving the distributed data access problem. (b)
Declarative page specifications, which treat the data displayed by pages as
rendered SQL++ page queries. The resulting pages are automatically
incrementally modified by FORWARD. User input on the page becomes part of the
UAS.
We show that SQL++ captures the semi-structured nature of web pages and
subsumes the data models of two important data sources of the UAS: SQL
databases and JavaScript components. We show that simple markup is sufficient
for creating Ajax displays and for modeling user input on the page as UAS data
sources. Finally, we discuss the page specification syntax and semantics that
are needed in order to avoid race conditions and conflicts between the user
input and the automated Ajax page modifications.
FORWARD has been used in the development of eight commercial and academic
applications. An alpha-release web-based IDE (itself built in FORWARD) enables
development in the cloud.Comment: Proceedings of the 14th International Symposium on Database
Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento,
Ital
MC-TESTER v. 1.23: a universal tool for comparisons of Monte Carlo predictions for particle decays in high energy physics
Theoretical predictions in high energy physics are routinely provided in the
form of Monte Carlo generators. Comparisons of predictions from different
programs and/or different initialization set-ups are often necessary. MC-TESTER
can be used for such tests of decays of intermediate states (particles or
resonances) in a semi-automated way.
Since 2002 new functionalities were introduced into the package. In
particular, it now works with the HepMC event record, the standard for C++
programs. The complete set-up for benchmarking the interfaces, such as
interface between tau-lepton production and decay, including QED bremsstrahlung
effects is shown. The example is chosen to illustrate the new options
introduced into the program. From the technical perspective, our paper
documents software updates and supplements previous documentation.
As in the past, our test consists of two steps. Distinct Monte Carlo programs
are run separately; events with decays of a chosen particle are searched, and
information is stored by MC-TESTER. Then, at the analysis step, information
from a pair of runs may be compared and represented in the form of tables and
plots.
Updates introduced in the progam up to version 1.24.3 are also documented. In
particular, new configuration scripts or script to combine results from
multitude of runs into single information file to be used in analysis step are
explained.Comment: 27 pages 4 figure
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry
Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is
âą to provide information which helps to design semiautomatic systems for the maintenance of statistical prediction models and
âą to describe a proof-of-concept implementation in an industrial application.
The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the systemâs functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions
Machine learning for early detection of traffic congestion using public transport traffic data
The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automĂĄtico basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del trĂĄfico urbano.L'objectiu principal d'aquest projecte Ă©s proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de SuĂšcia de manera per poder realitzar prediccions sobre l'estat futur del trĂ nsit urbĂ
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