A multimedia semantic framework for image understanding and retrieval

2017 ◽  
Vol 01 (01) ◽  
pp. 1650001
Author(s):  
Antonio Penta
Keyword(s):  
Semantic Information ◽  
Image Understanding ◽  
Heterogeneous Data ◽  
Multimedia Data ◽  
Image Domain ◽  
Semantic Framework ◽  
Semantic Relationships ◽  
Heterogeneous Data Sources ◽  
Semantic Properties ◽  
Provided Examples

On the grounds, ontologies have been shown to be a powerful resource for the interpretation and translation of the terminological and semantic relationships within domains of interest but it is still unclear how they can be applied in the context of multimedia data. In this paper, we describe a framework which can capture and manage semantic information related to the multimedia data by modeling in the ontology their features. In particular, the proposed ontology-based framework is organized in the following way: at the lower levels, spatial objects, colors, shapes are represented, and semantic relationships can be established among them; at the higher levels, objects with semantic properties are put into relationship among themselves as well as with the corresponding low-level objects. On this basis, we have designed an ontological system particularly suitable for image retrieval. We have also taken into account the inherent uncertainty related to the representation and detection of multimedia properties in this complex domain. Along this work, we have provided examples from the image domain; moreover, since ontologies provide a semantic means for the semantic comparison of objects and relationships across different formats, the system is easily extensible to other, heterogeneous data sources.

scholarly journals Building Semantic Information Mediator Runtime Application using Native API From Heterogeneous Data Sources

2019 ◽  
Vol 8 (11) ◽  
pp. 3416-3418
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Semantic Information ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Artificial Intelligent ◽  
Heterogeneous Data Sources ◽  
Proposed Model ◽  
Paper Author ◽  
Or Task

Semantic Information Mediator Runtime Application Using Native API(S.I.M.R.A.N.) is Artificial Intelligence based android mobile Application in which the agent is assigned to perform a particular operation or task. It is like an intermediate agent for the user who want to search the string and check given string matched or not. In this paper author provide a proposed model which is able to perform searching based on artificial Intelligent including semantic web features also

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals A multi-agent conversational system with heterogeneous data sources access

2016 ◽  
Vol 53 ◽  
pp. 172-191 ◽  
Author(s):  
Eduardo M. Eisman ◽  
María Navarro ◽  
Juan Luis Castro
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Multi Agent

scholarly journals A Framework for Visualizing Heterogeneous Construction Data Using Semantic Web Standards

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mostafa Ali ◽  
Yasser Mohamed
Keyword(s):  
Ad Hoc ◽  
3D Visualization ◽  
Heterogeneous Data ◽  
Data Merging ◽  
Levels Of Detail ◽  
Heterogeneous Data Sources ◽  
Web Standards ◽  
Visualization Process ◽  
Merging Process ◽  
Different Sources

3D Visualization provides a mean for communicating different construction activities to diverse audiences. The scope, level of detail, and time resolution of the 3D visualization process are determined based on the targeted audiences. Developing the 3D visualization requires obtaining and merging heterogeneous data from different sources (such as BIM model and CPM schedule). The data merging process is usually carried out on ad hoc basis for a specific visualization case which limits the reusability of the process. This paper discusses a framework for automatic merging of heterogeneous data to create a visualization. The paper describes developing an ontology which captures concepts related to the visualization process. Then, heterogeneous data sources that are commonly used in construction are fed into the ontology which can be queried to produce different visualization scenarios. The potential of this approach has been demonstrated by providing multiple visualization scenarios that cover different audiences, levels of detail, and time resolutions.

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