Biblio

In recent years, new types of cyber attacks called targeted attacks have been observed. It targets specific organizations or individuals, while usual large-scale attacks do not focus on specific targets. Organizations have published many Word or PDF files on their websites. These files may provide the starting point for targeted attacks if they include hidden data unintentionally generated in the authoring process. Adhatarao and Lauradoux analyzed hidden data found in the PDF files published by security agencies in many countries and showed that many PDF files potentially leak information like author names, details on the information system and computer architecture. In this study, we analyze hidden data of PDF files published on the website of police agencies in Japan and compare the results with Adhatarao and Lauradoux's. We gathered 110989 PDF files. 56% of gathered PDF files contain personal names, organization names, usernames, or numbers that seem to be IDs within the organizations. 96% of PDF files contain software names.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that continues to gain global popularity through the integration of analysis-ready data (ARD) on cloud computing frameworks. In 2021, CEOS released a new ODC sandbox that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open source toolset allows users to run application algorithms using a Google Colab Python notebook environment. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. Basic operation of the tool will support many users but can also be scaled in size and scope to support enhanced user needs. The creation of the ODC sandbox was prompted by the migration of many CEOS ARD satellite datasets to the cloud. The combination of these datasets in an interoperable data cube framework will inspire the creation of many new application products and advance open science.

Semantic data has semantic information about the relationship between information and resources of data collected in a smart city so that all different domains and data can be organically connected. Various services using semantic data such as public data integration of smart cities, semantic search, and linked open data are emerging, and services that open and freely use semantic data are also increasing. By using semantic data, it is possible to create a variety of services regardless of platform and resource characteristics. However, despite the many advantages of semantic data, it is not easy to use because it requires a high understanding of semantics such as SPARQL. Therefore, in this paper, we propose a semantic framework for users of semantic data so that new services can be created without a high understanding of semantics. The semantics framework includes a template-based annotator that supports automatically generating semantic data based on user input and a semantic REST API that allows you to utilize semantic data without understanding SPAROL.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.

Despite the growing promotion of the “open data” movement, the collection, cleaning, management, interpretation, and dissemination of open data is laborious and cost intensive, particularly for non-profits with limited resources. In this paper, we describe how non-profit organizations (NPOs) use open data, building on prior literature that focuses on understanding challenges that NPOs face. Based on 15 interviews of staff from 10 NPOs, our results suggest that NPOs use data to develop narratives to build a case for support from grantors and other stakeholders. We then present empirical results based on the usage of a data portal we created, which suggests that technologies should be designed to not only make data accessible, but also to facilitate communication and support relationships between expert data analysts and NPOs.

Open data is publicly available data that can be universally and readily accessed, used, and redistributed. Open data holds particular potential in the health and social sectors but, presently, health and social data are often published in a 'closed' format. There are different tools that allow to 'open' data, clean, structure and process them in order to elaborate them and build advanced services but, unfortunately, there is no single tool that can be used to perform all different tasks. We believe that the availability of Open Data in the health and social fields should be greatly increased and a way for creating new health and social services should be provided. In this paper, we present a framework that allows to create health and social Open Data starting from whatever is available on the web and to easily build advanced services based on those data.