The paper presents a proposal for a framework for the identification, assessment and selection of open data sources based on certain quality criteria, such as accessibility, relevance, accuracy & reliability, clarity, timeliness & punctuality, and coherence & comparability. The framework concerns mainly open data sources and focuses on their quality. The open data are used to enhance existing internal data and to fuse them with data from other sources. The framework consists of few steps starting from definition of quality criteria based on review of relevant literature and user requirements, then identification of potential sources, sources assessment and selection, and finally data retrieval process. For each step, a specific approach is described, how it may be conducted in practice. The proposed framework is evaluated using a real use case scenario from the maritime domain. The main approach utilized in this use-case is the Delphi method with some characteristics of Analytic Hierarchy Process.
The article concerns integration and disambiguation of data related to the maritime domain. A developed system is described, which collects and merges data about several maritime-related entities (vessels, vessel types, ports, companies etc.) retrieved from different internet sources and feeds the data into a single database. This process is however not trivial. There are few challenges, which need to be faced to successfully conduct it. Firstly, in different sources, entities may be referenced to in different ways, for example, by using different text strings. Additionally, some of these references may be ambiguous, i.e. potentially the reference may point to more than one entity. To enable efficient analysis of data coming from different sources, such ambiguities must be resolved automatically as a preprocessing step, before the data is uploaded to the database and utilized in further computations. The aim of the disambiguation process is to assign artificial, unique identifiers to each entity and then, if possible, automatically assign these identifiers to each data item related to a given entity. In the article, developed methods for resolving such ambiguities are discussed and their evaluation is presented.
In this paper, we discuss a software architecture, which has been developed for the needs of the System for Intelligent Maritime Monitoring (SIMMO). The system bases on the state-of-the-art information fusion and intelligence analysis techniques, which generates an enhanced Recognized Maritime Picture and thus supports situation analysis and decision- making. The SIMMO system aims to automatically fuse an up-to-date maritime data from Automatic Identification System (AIS) and open Internet sources. Based on collected data, data analysis is performed to detect suspicious vessels. Functionality of the system is realized in a number of different modules (web crawlers, data fusion, anomaly detection, visualization modules) that share the AIS and external data stored in the system’s database. The aim of this article is to demonstrate how external information can be leveraged in maritime awareness system and what software solutions are necessary. A working system is presented as a proof of concept.
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