Recuperación retrospectiva de un archivo policíaco: el “Casellario Politico Centrale”

The research illustrates utilization strategies of description standards and advanced technologies that are the basis of recovery ("recuperación retrospectiva") methodologies of archival finding aids, experienced in overcoming the problems associated with communicating and sharing resources. Considering interoperability, both technological and semantic, and long-term preservation of information as primary objectives of information systems, it is considered that recovery operations have to always be characterized by the choice of reference to specific standard solutions, technological (XML as data format) and descriptive encoding (Ead and Eac-Cpf in the archival area)

Recuperación retrospectiva de un archivo policíaco: el “Casellario Politico Centrale”

The research illustrates utilization strategies of description standards and advanced technologies that are the basis of recovery ("recuperación retrospectiva") methodologies of archival finding aids, experienced in overcoming the problems associated with communicating and sharing resources. Considering interoperability, both technological and semantic, and long-term preservation of information as primary objectives of information systems, it is considered that recovery operations have to always be characterized by the choice of reference to specific standard solutions, technological (XML as data format) and descriptive encoding (Ead and Eac-Cpf in the archival area)

Facilitating design learning through faceted classification of in-service information

The maintenance and service records collected and maintained by engineering companies are a useful resource for the ongoing support of products. Such records are typically semi-structured and contain key information such as a description of the issue and the product affected. It is suggested that further value can be realised from the collection of these records for indicating recurrent and systemic issues which may not have been apparent previously. This paper presents a faceted classification approach to organise the information collection that might enhance retrieval and also facilitate learning from in-service experiences. The faceted classification may help to expedite responses to urgent in-service issues as well as to allow for patterns and trends in the records to be analysed, either automatically using suitable data mining algorithms or by manually browsing the classification tree. The paper describes the application of the approach to aerospace in-service records, where the potential for knowledge discovery is demonstrated

Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes

Recommender systems are an increasingly important technology and researchers have recently argued for incorporating different kinds of data to improve recommendation quality. This paper presents a novel approach to generating recommendations and evaluates its effectiveness. First, we review evidence that item viewing time can reveal user preferences for items. Second, we model item preference as a weighted function of preferences for item attributes. We then propose a method for generating recommendations based on these two propositions. The results of a laboratory evaluation show that the proposed approach generated estimated item ratings consistent with explicit item ratings and assigned high ratings to products that reflect revealed preferences of users. We conclude by discussing implications and identifying areas for future research

Datenintegration, Wissensrepräsentation und Datenanalyse – Werkzeuge zur systematischen Untersuchung von Einflussfaktoren auf das Langzeit-Outcome nephrologischer Patienten

Das Gesundheitssystem wird sich durch die Digitalisierung in der Zukunft radikal ändern. Besserer Informationsaustausch durch vernetzte Fallakten, neue Versorgungsformen wie z.B. Telemedizinanwendungen können zukünftig das Problem des Fachärztemangels in strukturschwachen Gegenden vermindern. Neue Mobile Health Anwendungen werden die Patienten aktiver in Therapiemöglichkeiten einbinden und das Patient Empowerment verbessern. Zusätzlich werden durch die Digitalisierung immer mehr Daten produziert, die einen Betrag zur medizinischen Forschung und Verbesserung von Therapien leisten können. Neben den Herausforderungen zum Datenschutz und zur Datensicherheit, müssen auch Fragen zur Interoperabilität, Nutzen und Transparenz geklärt werden. Diese Arbeit untersucht exemplarisch an drei konkreten Beispielen (zur Datenintegration, Wissensrepräsentation und Datenanalyse), welche Herausforderungen und Lösungen möglich sind, um medizinische Daten effektiv zu nutzen und die Forschung und Routineversorgung zu verbessern. In der Studie zur Datenintegration wurde untersucht, inwieweit sich eine auf einem relationalen Datenbankschema basierende medizinische Routinedatenbank mit Langzeitdaten von transplantierten Patienten, in eine Ontologie-basierte Forschungsdatenbank wie i2b2, ohne Informationsverlust überführen lässt. Des Weiteren wurde in der Studie zur Wissensrepräsentation untersucht, wie sich mit Hilfe von Open Source Entwicklungswerkzeugen eine Applikation zur Visualisierung von Informationen aus strukturierten und unstrukturierten medizinischen Daten implementieren lässt. Mit der entwickelten Applikation kann das medizinische Personal ohne Programmierkenntnisse Informationen aus dem medizinischen Datenpool extrahieren und systematisch analysieren. Das Thema Datenanalyse wurde durch die Studie zum akuten Nierenversagen näher beleuchtet. In dieser Studie wurde ein Algorithmus implementiert, der in einer großen Kohorte aus stationären Patientendaten, das Ereignis akutes Nierenversagen (ANV) detektieren kann. Nach der statistischen Auswertung der Ergebnisse dieses Algorithmus, konnte die Kohorte im Hinblick auf das Auftreten von akuten Nierenversagen und den damit verbundenen Krankheitscharakteristika und Risikoassoziationen umfassend beschrieben werden.The digitalization will radically transform the healthcare system in the future. New forms of health care e.g. telemedicine or interconnected health records have the capability to reduce the problem of the shortage of medical experts in rural areas. New mobile health applications will involve patients more actively in their treatment options and will improve patient empowerment. Furthermore, the digitalization is producing more and more data, which should foster medical research and further improve of therapies. In addition to the challenges of data protection and data security, questions about interoperability, medical value and transparency must also be addressed. This thesis is based on three concrete examples (for data integration, knowledge representation and data analysis) and investigates which challenges and solutions are possible to use medical data effectively and to improve research and routine medical care. The study on data integration examined the extent to which a relational database for routine medical care with long-term data from transplanted patients can be transferred to an ontology-based research database such as i2b2 without loss of information. The study on the representation of knowledge examined the implementation of an application for the visualization of information from structured and unstructured medical data by using open source development tools. With the fully developed application, medical personnel can now extract information from the medical data base and easily analyse data without programming knowledge. The study on acute kidney failure examined the topic of data analysis in more detail. In this study, an algorithm was implemented that can detect the event of acute kidney failure in a large cohort of inpatient hospital data. After the statistical analysis of the results of this algorithm, the cohort could be comprehensively described with regard to the occurrence of acute kidney failure and the associated disease characteristics and risk associations

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