Farzad Parvinzamir

Management of Scientific Documents and Visualization of Citation Relationships using Weighted Key Scientific Terms

Effective management and visualization of scientific and research documents can greatly assist researchers by improving understanding of relationships (e.g. citations) between the documents. This paper presents work on the management and visualization of large corpuses of scientific papers in order to help researchers explore their citation relationships. Term selection and weighting are used for mining citation relationships by identifying the most relevant. To this end, we present a variation of the TF-IDF scheme, which uses external domain resources as references to calculate the term weighting in a particular domain; document weighting is taken into account in the calculation of term weighting from a group of citations. A simple hierarchical word weighting method is also presented. The work is supported by an underlying architecture for document management using NoSQL databases and employs a simple visualization interface.

Data Mining, Management and Visualization in Large Scientific Corpuses

Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.

MyHealthAvatar lifestyle management support for cancer patients

MyHealthAvatar (MHA) is built on the latest information and communications technology with the aim of collecting lifestyle and health data to promote citizen’s wellbeing. According to the collected data, MHA offers visual analytics of lifestyle data, contributes to individualised disease prediction and prevention, and supports healthy lifestyles and independent living. The iManageCancer project aims to empower patients and strengthen self-management in cancer diseases. Therefore, MHA has contributed to the iManageCancer scenario and provides functionality to the iManageCancer platform in terms of its support of lifestyle management of cancer patients by providing them with services to help their cancer management. This paper presents two different MHA-based Android applications for breast and prostate cancer patients. The components in these apps facilitate health and lifestyle data presentation and analysis, including weight control, activity, mood and sleep data collection, promotion of physical exercise after surgery, questionnaires and helpful information. These components can be used cooperatively to achieve flexible visual analysis of spatiotemporal lifestyle and health data and can also help patients discover information about their disease and its management.

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Patient empowerment delivers health and social care services that enable people to gain more control of their healthcare needs. With the advancement of sensor technologies, it is increasingly possible to monitor people’s health with dedicated wearable sensors. The consistent measurements from a variety of wearable sensors imply that a huge amount of data may be exploited to monitor and predict people’s health using medically proven models. In the process of health data representation and analysis, visualisation can be employed to promote data analysis and knowledge discovery via mature visual paradigms and well-designed user interactions. In this paper, we introduce the role of visualisation for individualised health monitoring and risk management in the background of a European Commission funded project, which aims to provide self-management of cardiorenal diseases with the assistance of wearable sensors. The visualisation components of health monitoring, risk model exploration, and risk analysis are presented to achieve personalised health and risk monitoring and to promote people’s wellbeing. It allows the patients not only to view existing risks, but also to gain awareness of the right pathway to change their lifestyles in order to reduce potential health risks.Graphical Abstract.

Visual Analytics for Health Monitoring and Risk Management in CARRE

With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health.

MyHealthAvatar: A Lifetime Visual Analytics Companion for Citizen Well-being

MyHealthAvatar is a European Commission funded project aimed to design a lifetime companion for citizens to collect, track and store lifestyle and health data to promote citizen well-being. MyHealthAvatar collects and aggregates life-logging data from wearable devices and mobile apps by integrating a variety of life-logging resources, such as Fitbit, Moves, Withings, etc. As a lifelong companion, the data collected will be too large for citizens, patients and doctors to understand and utilise without proper visual presentation and user interaction. This paper presents the key interactive visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3D avatar, dashboard, diary, timeline, clockview and map to achieve flexible spatio-temporal lifestyle visual analysis to promote citizen well-being.

Topic-Aware Visual Citation Tracing via Enhanced Term Weighting for Efficient Literature Retrieval

Efficient retrieval of scientific literature related to a certain topic plays a key role in research work. While little has been done on topic-enabled citation filtering in traditional citation tracing, this paper presents visual citation tracing of scientific papers with document topics taken into consideration. Improved term selection and weighting are employed for mining the most relevant citations. A variation of the TF-IDF scheme, which uses external domain resources as references is proposed to calculate the term weighting in a particular domain. Moreover document weight is also incorporated in the calculation of term weight from a group of citations. A simple hierarchical word weighting method is also presented to handle keyword phrases. A visual interface is designed and implemented to interactively present the citation tracks in chord diagram and Sankey diagram.

MyHealthAvatar and CARRE: case studies of interactive visualisation for internet-enabled sensor-assisted health monitoring and risk analysis

With the progress of wearable sensor technologies, more wearable health sensors have been made available on the market, which enables not only people to monitor their health and lifestyle in a continuous way but also doctors to utilise them to make better diagnoses. Continuous measurement from a variety of wearable sensors implies that a huge amount of data needs to be collected, stored, processed and presented, which cannot be achieved by traditional data processing methods. Visualisation is designed to promote knowledge discovery and utilisation via mature visual paradigms with well-designed user interactions and has become indispensable in data analysis. In this study the authors introduce the role of visualisation in wearable sensor-assisted health analysis platforms by case studies of two projects funded by the European Commission: MyHealthAvatar and CARRE. The former focuses on health sensor data collection and lifestyle tracking while the latter aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The roles of visualisation components including timeline, parallel coordinates, map, node-link diagrams, Sankey diagrams, etc. are introduced and discussed.

MyHealthAvatar: A Case Study of Web-Based Interactive Visual Analytics of Lifestyle Data

MyHealthAvatar is a project designed to collect and track lifestyle and health data to promote citizen wellbeing. As a lifetime companion of citizens, the amount of data collected will be huge. It is almost impossible for citizen, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Interactive visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the interactive visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3d avatar, dashboard, diary, timeline, clock view and map. These components can be integrated to achieve flexible visual analysis of spatio-temporal lifestyle data.