294 research outputs found
Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data
Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data.
We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive
Using Data in Undergraduate Science Classrooms
Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm
Designing a Context-Sensitive Context Detection Service for Mobile Devices
This paper describes the design, implementation, and evaluation of Amoeba, a context-sensitive context detection service for mobile devices. Amoeba exports an API that allows a client to express interest in one or more context types (activity, indoor/outdoor, and entry/exit to/from named regions), subscribe to specific modes within each context (e.g., "walking" or "running", but no other activity), and specify a response latency (i.e., how often the client is notified). Each context has a detector that returns its estimate of the mode. The detectors take both the desired subscriptions and the current context detection into account, adjusting both the types of sensors and the sampling rates to achieve high accuracy and low energy consumption. We have implemented Amoeba on Android. Experiments with Amoeba on 45+ hours of data show that our activity detector achieves an accuracy between 92% and 99%, outperforming previous proposals like UCLA* (59%), EEMSS (82%) and SociableSense (72%), while consuming 4 to 6× less energy
Towards a digital mine: a spatial database for accessing historical geospatial data on mining and related activities
A Research Report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science.
Johannesburg, 2016.Countries around the world are recognising the importance of geospatial data in answering questions related to spatially varying industries such as mining activities (ongoing and discontinued). This is becoming increasingly evident with countries such as Canada, Australia, and the United Kingdom working towards establishing Abandoned Mine Lands (AML) inventories. However, the increasing need for data on mining activities is not paralleled by an increase in the availability of such data. The aim of this research therefore is to design a database for accessing historical and current geospatial data that can be used to support research, environmental management efforts as well as support decision making at all levels.
A user needs survey was conducted. Two sampling methods were employed, convenient sampling and snowball sampling method. The convenient sampling method was used mostly with all the WDMP group members and the latter was employed with the respondents from institutions and organisations outside of the university respectively. The data were then categorised so as to make analysis easier and data could be evaluated on the same basis. An evaluation of the data collected showed that although the WDMP required different types of data (spatial and non- spatial) the data feed into each other and as such it is important that there is a central repository in which to store them. Furthermore investigation also shows that there is a wealth of data on current mining activities, but not so much on historical mining activities. Although data on mining activities exists, accessibility to these data is hindered by various factors such as copyright infringements, data costs, discrepancies in the data request process.
The outcome of this research has been that of a physical database PostgreSQL database (PostGIS) and one mounted on an online platform (GeoServer). The databases can be visualised on PostgreSQL using select statements or visualisation through establishing a connection with QGIS, alternatively the database may be accessed on GeoServer.
The database is expected to be of use to at least all members of the Wits Digital Mine Project (WDMP) and stakeholders involved in the project. The database can be used for baseline studies and also as a basis for the framework used to analyse, remedy as well as predict future challenges in the mining industry. Moreover, the database can act as a central repository for all data produced from the WDMP.LG201
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e-mission: an open source, extensible platform for human mobility systems
Transportation is the single largest source of carbon emissions in the US. Decarbonizing it is challenging because it depends on individual behaviors, which in turn, depend on local land use planning. The interdisciplinary field of Computational Mobility, focusing on collecting, analysing and influencing human travel behavior, can frame solutions to this challenge.Innovation flows in interdisciplinary fields are bi-directional. The flow to the domain is focused on building a strong foundation for methodological improvements. As the improvements are deployed, they result in use-inspired computational research. This temporal dependency results in our initial focus on the modularity, accuracy and reproducibility of e-mission, an extensible platform for instrumenting human mobility. This open source platform has a modular architecture that supports power efficient duty cycling using virtual sensors, a read-only data model and a pipeline with novel algorithm adaptations for smartphone sensing.We also perform the first empirical evaluations of smartphone-based platforms in this domain. The architectural evaluation is based on three real world deployments: a classic travel diary, a crowdsourcing initiative, and a behavioral study. The accuracy evaluation is based on an novel procedure that uses artificial trips and multiple parallel phones to mitigate concerns over privacy, context sensitive power consumption and inherent sensing error. Data collected from three artifical timelines was used to evaluate the trajectory, segmentation and classification accuracies vs. power for various configurations.On computational side, challenges derived from the deployments can contribute to ongoing CS research in privacy, trustworthiness, incentivization and decision making. On the mobility side, this enables methodological innovations such as Agile Urban Planning for prototyping infrastructure changes
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