HealthFinland—A national semantic publishing network and portal for health information

Providing citizens with reliable, up-to-date and individually relevant health information on the Web is done by governmental, non-governmental, business and other organizations. Currently the information is published with little co-ordination and co-operation between the publishers. For publishers, this means duplicated work and costs due to publishing same information twice on many websites. Also maintaining links between websites requires work. From the citizens point of view, finding content is difficult due to e.g. differences in layman’s vocabularies compared to medical terminology and difficulties in aggregating information from several sites.To solve these problems, we present a national scale semantic publishing system HealthFinland which consists of (1) a centralized content infrastructure of health ontologies and services with tools, (2) a distributed semantic content creation channel based on several health organizations, and (3) an intelligent semantic portal aggregating and presenting the contents from intuitive and health promoting end-user perspectives for human users as well as for other websites and portals.     

Why current differential privacy schemes are inapplicable for correlated data publishing?

World Wide Web - Although data analysis and mining technologies can efficiently provide intelligent and personalized services to us, data owners may not always be willing to share their true data...     

Eletronic publishing and chemical text processing—II. Character and vector representations of chemical structures

Fragments for two-dimensional chemical structure diagrams can be represented by either characters or vectors. The relative merits of the two approaches are considered and it is shown that the vector representation is clearly superior.     

(<Emphasis Type="Italic">α</Emphasis>, <Emphasis Type="Italic">k</Emphasis>)-anonymous data publishing

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.