DOI: 10.3897/jucs.97822

Authors: Herminia Beatriz Parra de Gallo, Marcela Vegetti

Abstract: This paper shows the research conducted to respond to a continuous requirement of justice regarding the application of scientifically supported forensic tools. Considering ontological engineering as the appropriate framework to respond to this requirement, the article presents OntoFoCE (Spanish abbreviation for Ontology for Electronic Mail Forensics), a specific ontology for the forensic analysis of emails. The purpose of this ontology is to help the computer expert in the validation of an email presented as judicial evidence. OntoFoCE is the fundamental component of the ObE Forensics (Ontology-based Email Forensics) tool. Although there are numerous forensic tools to analyze emails, the originality of the one proposed here lies in the implementation of semantic technologies to represent the traceability of the email transmission process. From that point on, it is possible to provide answers to the items of digital evidence subject to the expert examination. These answers make it possible to support these evidence items in the forensic analysis of an email and to guarantee the gathering of scientifically and technically accepted results that are valid for justice. Thus, the research question that is tried to be answered is: Is it possible to apply ontological engineering as a scientific support to design and develop a forensic tool that allows automatic answers to the evidence items subject to the expert examination in the forensic analysis of emails?

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DOI: 10.3897/jucs.2020.020

Authors: Jeppe Rasmussen, Lars Hvam, Katrin Kristjansdottir, Niels Mortensen

Abstract: Product configuration systems (PCSs) are increasingly being used in various industries to manage product knowledge and create the required specifications of customized products. Companies applying PCS face significant challenges in modelling, structuring and documenting the systems. Some of the main challenges related to PCSs are formalising product knowledge conceptually and structuring the product features. The modelling techniques predominantly used to visualise and structure PCSs are the Unified Modelling Language (UML) notations, Generic Bill of Materials (GBOM) and Product Variant Master (PVM), associated with class collaboration cards (CRC-cards). These methods are used to both analyse and model the products and create a basis for implementation to a PCS by using an object-oriented approach. However, the modelling techniques do not consider that most commercial PCSs are not fully object-oriented, but rather, they are expert systems with an inference engine and a knowledge base; therefore, the constructed product models require modifications before implementation in the configuration software. The consequences are that what is supposedly a feasible structure of the product model is not always appropriate for the implementation in standard PCS software. To address this challenge, this paper investigates the best practice in modelling and implementation techniques for PCSs in standard software and alternative structuring methods used in object-oriented software design. The paper proposes a method for a modular design of a PCS in not fully object-oriented standard PCS software using design patterns. The proposed method was tested in a case company that suffered from a poorly structured product model in a not fully object-oriented PCS. The results show that its maintainability can be improved by using design patterns in combination with an agile documentation approach.

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DOI: 10.3217/jucs-017-14-2009

Authors: Roberto Konow, Wayman Tan, Luis Loyola, Javier Pereira, Nelson Baloian

Abstract: There has been a continuous development of new clustering and prediction techniques that help customers select products that meet their preferences and/or needs from an overwhelming amount of available choices. Because of the possible huge amount of available data, existing Recommender Systems showing good results might be difficult to implement and may require a lot of computational resources to perform in this scenario. In this paper, we present a more simple recommender system than the traditional ones, easy to implement, and requiring a reasonable amount of resources to perform. This system clusters users according to the frequency an item has been visited by users belonging to the same cluster, performing a collaborative filtering scheme. Experiments were conducted to evaluate the accuracy of this method using the Movielens dataset. Results obtained, as measured by the F-measure value, are comparable to other approaches found in the literature which are far more complex to implement. Following this, we explain the application of this system to an e-content site scenario for advertising. In this context, a filtering tool is shown which has been developed to filter and contextualize recommended items.

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DOI: 10.3217/jucs-014-02-0193

Authors: Benjamin Burkard, Georg Vogeler, Stefan Gruner

Abstract: This article is a revised and extended version of [VBG, 07]. We conjecture that the digitalization of historical text documents as a basis of data mining and information retrieval for the purpose of progress in the history sciences is urgently needed. We present a novel, specialist XML tool-suite supporting the working historian in the transcription of original medieval charters into a machine-readable form, and we also address some latest developments which can be found in the field since the publication of [VBG, 07].

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