INTELLIGENT INFORMATION SYSTEMS, QUO VADIS?

ABSTRACT Based on its most popular incarnations, Intelligen

FRIOD: a deeply integrated feature-rich interactive system for effective and efficient outlier detection

In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient

Gestión eficiente de reconocimiento del iris en bases de datos objetos-relacionales

Biometric applications have grown significantly in recent years, particularly iris-based systems. In the present work, an extension of an Object Relational Database Management System for the integral management of a biometric system based on the human iris was presented. Although at present, there are many database extensions for different domains, in no case for biometric applications. The proposed extension includes both the extension of the type system and the definition of domain indexes for performance improvement. The aim of this work is to provide a tool that facilitates the development of biometric applications based on the iris feature. Its development is based on a reference architecture that includes both the management of images of the iris trait, its associated metadata and the necessary methods for both manipulation and queries. An implementation of the extension is performed for PostgreSQL DBMS, and SP-GiST framework is used in the implementation of a domain index. Experiments were carried out to evaluate the performance of the proposed index, which shows improvements in query execution times.Las aplicaciones biométricas han crecido significativamente en los últimos años, en particular los sistemas basados en el iris. En el presente trabajo se presenta una ampliación de un Sistema de Gestión de Base de Datos Objeto-Relacional para la gestión integral de un sistema biométrico basado en el iris humano. Aunque en la actualidad existen muchas extensiones de bases de datos para diferentes dominios, en ningún caso existen para aplicaciones biométricas. La extensión propuesta incluye tanto la extensión del sistema de tipos como la definición de índices de dominio para la mejora del rendimiento. El objetivo de este trabajo es proporcionar una herramienta que facilite el desarrollo de aplicaciones biométricas basadas en el iris. Su desarrollo se basa en una arquitectura de referencia que incluye tanto la gestión de las imágenes del rasgo del iris, sus metadatos asociados y los métodos necesarios, tanto para la manipulación como para las consultas. Se realiza una implementación de la extensión para PostgreSQL DBMS, y se utiliza el framework SPGiST en la implementación de un índice de dominio. Se realizaron experimentos para evaluar el desempeño del índice propuesto, que muestra mejoras en los tiempos de ejecución de las consultas.Facultad de Informátic

Gestión eficiente de reconocimiento del iris en bases de datos objetos-relacionales

Biometric applications have grown significantly in recent years, particularly iris-based systems. In the present work, an extension of an Object Relational Database Management System for the integral management of a biometric system based on the human iris was presented. Although at present, there are many database extensions for different domains, in no case for biometric applications. The proposed extension includes both the extension of the type system and the definition of domain indexes for performance improvement. The aim of this work is to provide a tool that facilitates the development of biometric applications based on the iris feature. Its development is based on a reference architecture that includes both the management of images of the iris trait, its associated metadata and the necessary methods for both manipulation and queries. An implementation of the extension is performed for PostgreSQL DBMS, and SP-GiST framework is used in the implementation of a domain index. Experiments were carried out to evaluate the performance of the proposed index, which shows improvements in query execution times.Las aplicaciones biométricas han crecido significativamente en los últimos años, en particular los sistemas basados en el iris. En el presente trabajo se presenta una ampliación de un Sistema de Gestión de Base de Datos Objeto-Relacional para la gestión integral de un sistema biométrico basado en el iris humano. Aunque en la actualidad existen muchas extensiones de bases de datos para diferentes dominios, en ningún caso existen para aplicaciones biométricas. La extensión propuesta incluye tanto la extensión del sistema de tipos como la definición de índices de dominio para la mejora del rendimiento. El objetivo de este trabajo es proporcionar una herramienta que facilite el desarrollo de aplicaciones biométricas basadas en el iris. Su desarrollo se basa en una arquitectura de referencia que incluye tanto la gestión de las imágenes del rasgo del iris, sus metadatos asociados y los métodos necesarios, tanto para la manipulación como para las consultas. Se realiza una implementación de la extensión para PostgreSQL DBMS, y se utiliza el framework SPGiST en la implementación de un índice de dominio. Se realizaron experimentos para evaluar el desempeño del índice propuesto, que muestra mejoras en los tiempos de ejecución de las consultas.Facultad de Informátic

Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

What Geographers Research: An Analysis of Geography Topics, Clusters, and Trends Using a Keyword Network Analysis Approach and the 2000-2019 AAG Conference Presentations

The spectrum of geographic research topics is very broad, and several thousands of research projects are presented at AAG annual conferences. This research aims at analyzing geography research topics, clusters, and trends using conference presentation data. We analyzed the 2000-2019 AAG conference presentations with keyword network analysis methods. The most frequently used keywords during the 20-year span were GIS, followed by Remote Sensing, Climate Change, Urban, China, Education, Political Ecology, Migration, Gender, and Agriculture. Results showed that geographic research has focused on six major clusters during 2000-2019: GIS, Urban, Climate Change, Political Ecology, People, and Education. About 68.6 percent of keywords were about the GIS, People, and Urban issues. The GIS keyword showed very strong connections with Remote Sensing, Urban, Spatial, Education, Climate Change, and Health. Over the 2015-2019 period, big data analysis and artificial intelligence became popular as emerging fields. This research also shows that the keyword network analysis is an effective method to summarize research trends in geography using conference presentation data. To some fellow geographers, the findings in this research may also cast meaningful insights into what geography is and where it is heading

ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully