195,677 research outputs found
DATA MINING LANGUAGES STANDARDS
The increasing of the database dimension creates many problems, especially when we need to access, use and analyze data. The data overflow phenomenon in database environments imposes the application of different data mining methods, in order to find relevant information from large databases. A lot of data mining tools emerged in the last years. The standardization of data mining languages become in the last years a very important topic. The paper presents Predictive Model Markup Language (PMML) standards from the Data Mining Group. PMML, a standard language for defining data mining models, which allows users to develop models within one vendor's application, and use other vendors' applications to visualize, analyze, evaluate or otherwise use the models.
An R package for parametric estimation of causal effects
This article explains the usage of R package CausalModels, which is publicly
available on the Comprehensive R Archive Network. While packages are available
for sufficiently estimating causal effects, there lacks a package that provides
a collection of structural models using the conventional statistical approach
developed by Hernan and Robins (2020). CausalModels addresses this deficiency
of software in R concerning causal inference by offering tools for methods that
account for biases in observational data without requiring extensive
statistical knowledge. These methods should not be ignored and may be more
appropriate or efficient in solving particular problems. While implementations
of these statistical models are distributed among a number of causal packages,
CausalModels introduces a simple and accessible framework for a consistent
modeling pipeline among a variety of statistical methods for estimating causal
effects in a single R package. It consists of common methods including
standardization, IP weighting, G-estimation, outcome regression, instrumental
variables and propensity matching
Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process
Identifying Agile Requirements Engineering Patterns in Industry
Agile Software Development (ASD) is gaining in popularity in today´s business world. Industry is adopting agile methodologies both to accelerate value delivery and to enhance the ability to deal with changing requirements. However, ASD has a great impact on how Requirements Engineering (RE) is carried out in agile environments. The integration of Human-Centered Design (HCD) plays an important role due to the focus on user and stakeholder involvement. To this end, we aim to introduce agile RE patterns as main objective of this paper. On the one hand, we will describe our pattern mining process based on empirical research in literature and industry. On the other hand, we will discuss our results and provide two examples of agile RE patterns. In sum, the pattern mining process identifies 41 agile RE patterns. The accumulated knowledge will be shared by means of a web application.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2016-76956-C3-2-RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
Standardization and Control for Confounding in Observational Studies: A Historical Perspective
Control for confounders in observational studies was generally handled
through stratification and standardization until the 1960s. Standardization
typically reweights the stratum-specific rates so that exposure categories
become comparable. With the development first of loglinear models, soon also of
nonlinear regression techniques (logistic regression, failure time regression)
that the emerging computers could handle, regression modelling became the
preferred approach, just as was already the case with multiple regression
analysis for continuous outcomes. Since the mid 1990s it has become
increasingly obvious that weighting methods are still often useful, sometimes
even necessary. On this background we aim at describing the emergence of the
modelling approach and the refinement of the weighting approach for confounder
control.Comment: Published in at http://dx.doi.org/10.1214/13-STS453 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Towards the Usage of MBT at ETSI
In 2012 the Specialists Task Force (STF) 442 appointed by the European
Telcommunication Standards Institute (ETSI) explored the possibilities of using
Model Based Testing (MBT) for test development in standardization. STF 442
performed two case studies and developed an MBT-methodology for ETSI. The case
studies were based on the ETSI-standards GeoNetworking protocol (ETSI TS 102
636) and the Diameter-based Rx protocol (ETSI TS 129 214). Models have been
developed for parts of both standards and four different MBT-tools have been
employed for generating test cases from the models. The case studies were
successful in the sense that all the tools were able to produce the test suites
having the same test adequacy as the corresponding manually developed
conformance test suites. The MBT-methodology developed by STF 442 is based on
the experiences with the case studies. It focusses on integrating MBT into the
sophisticated standardization process at ETSI. This paper summarizes the
results of the STF 442 work.Comment: In Proceedings MBT 2013, arXiv:1303.037
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