Abstract
Criminal investigations face a deluge of structured and unstructured data obtained from heterogeneous sources like forensic reports or wiretap transcriptions. In these cases, finding relevant information can be a complex task. Ontologies have been successfully applied to several domains including legal, cyber crime and digital forensics. In this paper it is proposed a framework based on ontology engineering, that provides an unified approach to represent and reason with the criminal investigation data. Moreover, this framework is applied to the specific use case of money laundering.

Abstract

Abstract

Abstract
Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general-purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long-term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning fromcoder-brains to reader-brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

Abstract

Abstract
A cancer registry is a standardized tool to produce population-based data on cancer incidence and survival. Cancer registries can retrieve and store information on all cancer cases occurring in a defined population. The main sources of data on cancer cases usually include: treatment and diagnostic facilities (oncology centres or hospital departments, pathology laboratories, or imaging facilities etc.) and the official territorial death registry. The aim of this paper is to evaluate the north regional cancer registry (RORENO) of Portugal using a qualitative research. We want to characterize: the main functionalities and core processes, team involved, different healthcare institutions in the regional network and an identification of issues and potential improvements. RORENO links data of thirteen-two healthcare institutions and is responsible for the production of cancer incidence and survival report for this region. In our semi-structure interviews and observation of RORENO we identified a serious problem due to a lack of an automatic integration of data from the different sources. Most of the data are inserted manually in the system and this implies an extra effort from the RORENO team. At this moment RORENO team are still collecting data from 2011. In a near future it is crucial to automatize the integration of data linking the different healthcare institutions in the region. However, it is important to think which functionalities this system should give to the institutions in the network to maximize the engagement with the project. More than a database this should be a source of knowledge available to all the collaborative oncologic network.