Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans

DocumentMiner : A temporal text mining framework for business intelligence

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evaluation of the SRA Tool Using Data Mining Techniques

This paper describes a validation approach of a socio-technical design support system using data mining techniques. Bayesian Belief Networks (BBN) are used to assess human error and system failure [13] based on a variety of high-level operational scenarios. The System Reliability Analyser (SRA) tool automates the process by iteratively manipulating the BBN model. Data mining techniques are employed in order to identify whether the initial assumptions embedded in the system reliability model are met by results from scenario-based testing

Theodoulidis, ‘An Approach to Text Mining using Information extraction

In this paper we describe our approach to Text Mining by introducing TextMiner. We perform term and event extraction on each document to find features that are likely to have meaning in the domain, and then apply mining on the extracted features labelling each document. The system consists of two major components, the Text Analysis component and the Data Mining component. The Text Analysis component converts semi structured data such as documents into structured data stored in a database. The second component applies data mining techniques on the output of the first component. We apply our approach in the financial domain (financial documents collection) and our main targets are: a) To manage all the available information, for example classify documents in appropriate categories and b) To “mine” the data in order to “discover ” useful knowledge. This work is designed to primarily support two languages, i.e. English and Greek. The explosive growth of databases in almost every area of human activity has created a great demand for new, powerful tools for turning data into useful knowledge. To satisfy this need researchers from various technological areas, such as machine learning, pattern recognition

MetaOn - Ontology driven metadata construction and management for intelligent search in text and image collections

State of the art in multimedia technology focuses in managing data collected from various sources, including documents, images, video, and speech. Therefore the effective management, analysis and mining of such heterogeneous data requires the combination of various techniques. In this paper we present an overview of the recently funded MetaOn project. The core objective of MetaOn is to construct and integrate semantically rich metadata collections extracted from documents images and linguistic resources, to facilitate intelligent search and analysis. The proposed MetaOn framework involves, ontology-based information extraction and data mining, semi-automatic construction of domain specific ontologies, content-based image indexing and retrieval, and metadata management. The Hellenic history has been chosen as a challenging application case study

Multimedia Annotation System for Intelligent Search*

In this paper we present an overview of the intelligent multimedia annotation and search system MetaOn. The core objective is to construct and integrate semantically rich metadata, extracted from documents and images, to facilitate intelligent search and analysis. The proposed MetaOn framework involves, ontology-based information extraction and data mining, semi-automatic construction of domain specific ontologies, content-based image indexing and retrieval, and metadata management. The Hellenic history has been chosen as a challenging application case study