IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS

1-16

Authors: Jorun Børsting, Alma L. Culén and Morten C. Eike

This paper is concerned with the design of a system that handles published research literature evaluation related to clinical DNA sequencing and analysis of genetic variants. The literature handling system is part of a larger system, the Norwegian clinical genetic Analysis Platform, currently under development at the Department of Medical Genetics at Oslo University Hospital. The genAP project has inquired into data handling requirements, procedures and supportive bioinformatics tools for analysis of genetic data. Finding and evaluating relevant literature that reports on clinical classifications of genetic variants is an important part of this process. In many cases, it is a requirement to compare local assessments with those published in high-quality external references, ensuring that the correct decision on the clinical nature of the variant is reached. The implications of the decisions made as part of this process are relevant for both patients and knowledge production and its transferability. We chose to use user-centered design as our research approach, in both qualitative (walk-troughs, interviews and talk-aloud evaluations) and quantitative (questionnaire) inquiries. User involvement in design and evaluation of the reference handling prototype was important for identifying diverse usability problems and design issues, which could then be improved in later iterations of the prototype. These issues included identifying the most relevant articles for a particular genetic variant and communicating uncertainty in individual assessments. Users have also contributed to defining more general guidelines for the re-design of later versions, e.g., a need for customization, as users often have different strategies for working with references. We assert that user involvement in the design and evaluation processes, such as described in this paper, leads to system design that is more in tune with users’ needs, making the adoption and use of the system easier and improving the efficiency and quality of the analysis.

32-48

Authors: Saeed Samet, Ahoora Sadeghi Boroujerdi and Shabnam Asghari

Health informatics, using new information technology, provides a fruitful set of data resource and knowledge that is very useful for secondary data users and researchers in various health systems and applications. Privacy acts, on the other hand, prevent direct access to this information without patient's consent. Various solutions have been proposed such as data anonymization and de-identification, on-site analysis, and limited remote access, to preserve the data owner's privacy. Each of those approaches has different drawbacks and limitations. For instance, data de-identification will reduce data utility because of low precision of the final released data, and also it has a risk of data re-identification using available public data and background knowledge. On-site analysis has physical limitations, such as lack of data centers in every geographic area, and time-consuming procedures, such as background checks. Remote access increases security risks, and when data has to be pulled from multiple data resources, it requires patient consent for data disclosure. In this paper, we propose a set of privacy-preserving methods and techniques for some popular health statistical analysis methods. Using this set of secure protocols health researchers and other data users are able to issue their requests as some queries, and receive only the results of their queries from the data owners, while each data custodian can keep their sensitive data private. Proposed methods have been tested using sample data to illustrate the performance of the results in terms of computational and communication complexities. Security proof of the proposed protocols has also been provided as a proof of concept.

63-80

Authors: Andreas Hufgard and Johannes Schulz

In business IT, empirical data is very rarely in the form of corporate usage data from system analyses. This article is based on 105 ERP-systems that have been analyzed in-depth, and whose data deliver empirical conclusions about usage and the level of maturity of business processes, and quantify the primary types of activities users are involved in. The approach is applied by comparing two groups in the database: “Automotive Industry” and “Non-Automotive Industry”. Here we focus on identifying significant differences in the efficient use of business processes in purchasing between the groups. These efficiency indicators where deviated from most accepted purchasing maturity models. The research question is to validate the high evaluation of the Automotive Industry in those models against the reality we can find in the ERP-System. The usage data was gathered from usage analyses of SAP systems via RBE Plus, a reverse business engineering tool. It is based on the activities of more than 270,000 users in organizations in Europe and the USA. The findings offer enterprises a standard of comparison and reveal potential for improvement in sub-processes.

99-114

Authors: Luis Alexandre da Silva, Kelton Augusto Pontara da Costa, Patricia Bellin Ribeiro, Gustavo Henrique de Rosa and João Paulo Papa

Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by means of Restricted Boltzmann Machines (RBMs) in order to identity whether such messages contain relevant (ham) or non-relevant (spam) content. The main idea contribution of this work is to employ Harmony Search-based optimization techniques to fine-tune RBM parameters, as well as to evaluate their robustness in the context spam detection. The unsupervised learned features are then used to feed the Optimum-Path Forest classifier, being the original features extracted from e-mail content and compared against the new ones. The results have shown RBMs are suitable to learn features from e-mail data, since they obtained favorable results in the datasets considered in this work.