Search results for "Data-driven"
showing 10 items of 59 documents
From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets
2021
Abstract In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. There…
Establishing Video Game Genres Using Data-Driven Modeling and Product Databases
2015
Establishing genres is the first step toward analyzing games and how the genre landscape evolves over the years. We use data-driven modeling that distils genres from textual descriptions of a large collection of games. We analyze the evolution of game genres from 1979 till 2010. Our results indicate that until 1990, there have been many genres competing for dominance, but thereafter sport-racing, strategy, and action have become the most prevalent genres. Moreover, we find that games vary to a great extent as to whether they belong mostly to one genre or to a combination of several genres. We also compare the results of our data-driven model with two product databases, Metacritic and Mobyga…
Expectancy in Sami Yoiks revisited: The role of data-driven and schema-driven knowledge in the formation of melodic expectations
2009
This study extends a previous study concerning melodic expectations in North Sami yoiks (Krumhansl et al., 2000) in which a comparison between expert and non-expert listeners demonstrated the existence of a core set of principles governing melodic expectancies. The previous findings are reconsidered using non-Western listeners (traditional healers from South Africa) in a modeling investigation. Comparison of different models made it possible to separate the role of data-driven and schema-driven models in melodic expectancies and to reveal any possible Western bias in previous studies. The results of the experiment, in which African listeners rated the fitness of probe-tones as continuation…
Sub-Symbolic Mapping of Cyc Microtheories in Data-Driven 'Conceptual' Spaces
2007
The presented work aims to combine statistical and cognitive-oriented approaches with symbolic ones so that a conceptual similarity relationship layer can be added to a Cyc KB microtheory. Given a specific microtheory, a LSA-inspired conceptual space is inferred from a corpus of texts created using both ad hoc extracted pages from the Wikipedia repository and the built-in comments about the concepts of the specific Cyc microtheory. Each concept is projected in the conceptual space and the desired layer of sub-symbolic relationships between concepts is created. This procedure can help a user in finding the concepts that are "sub-symbolically conceptually related" to a new concept that he wan…
A Comparison between Heuristic, Statistical and Data-driven Methods in Landslide Susceptibility Assessment: an Application to the Briga and Giampilie…
2014
Susceptibility assessment concerning the estimation of areas prone to landslide is one of the most useful approach in the analysis of landslide hazard. Over the last years, in an attempt to find the best approach to evaluate landslide susceptibility, many methods have been developed. Among these, the heuristic, the statistical, and the data-driven approaches are very widespread, and they all are based on the concept that the conditions which led to landslide movements in the past will control the probability of movement occurrence in the future. This study presents an assessment of landslide susceptibility in which models of the three different methodologies, such as the heuristic approach,…
Data-driven decision support to reduce "driving-under the influence of alcohol" offenses
2018
Extracting valuable knowledge from data to support decision making is a widely practiced trend. Data-driven decision support (DDDS) provides insight for decision makers by exploring and extracting underlying patterns within a dataset. This thesis covers the process of DDDS in reducing driving under the influence of alcohol (DUI) offenses by introducing proposed prison sentences. In this thesis, DDDS is applied to a DUI dataset by analyzing patterns in the dataset and by introducing proposed prison sentences for offenders to reduce the number of DUI cases. Background theories in data mining, machine learning, optimization and decision science that are related to the thesis project are also c…
Applying data driven decision making to rank vocational and educational training programs with TOPSIS
2021
Abstract In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009–2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used is TOPSIS together with a new decision support method for assessing the influence of each criterion and its dependence on the weights assigned to them. This new method is based on a worst-best case scenario analysis and it is compared to a well known global sensitivity analysis technique based on the Pearson's correlation ratio.
Latent force models for earth observation time series prediction
2016
We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.
Corpus linguistics and its aplications in higher education
2010
The aim of this paper is to review and analyse relevant factors related to the implementation of corpus linguistics (CL) in higher education. First we set out to describe underlying principles of CL and its developments in relation to theoretical linguistics and its applications in modern teaching practices. Then we attempt to establish how different types of corpora have contributed to the development of direct and indirect approaches in language teaching. We single out Data Driven Learning (DDL) due to its relevance in applied linguistics literature, and examine in detail advantages and drawbacks. Finally, we outline problems concerning the implementation of CL in the classroom since awar…
A Data-Driven Approach to Dynamically Learn Focused Lexicons for Recognizing Emotions in Social Network Streams
2016
Opinion Mining aims at identifying and classifying subjective information in a collection of documents. A variety of approach exists in literature, ranging from Supervised Learning to Unsupervised Learning. Currently, one of the biggest opinion resource of opinionated texts existing on the Web is represented by Social Networks. Networks are not only a vast collection of documents but they also represent a dynamic evolving resource as the users keep posting their own opinions. We based our work relying on this idea of dynamicity, building an evolving model that updates itself in real time as users submit their posts. This is done through a set of supervised techniques based on a Lexi- con of…