IVIS4BigData: A Reference Model for Advanced Visual Interfaces Supporting Big Data Analysis in Virtual Research Environments

Bornschlegl, Marco X.; Berwind, Kevin; Kaufmann, Michael; Engel, Felix; Walsh, Paul; Hemmje, Matthias; Riestra, Ruben

doi:10.1007/978-3-319-50070-6_1

Cited by 20 publications

(2 citation statements)

References 21 publications

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“…Another important related work is IVIS4BigData [8], where an architecture for the visualization of information is presented, which can also serve as an architectural model to process raw data into structured data, and apply analytic algorithms to it. The corresponding information model in the area of multimedia can be represented by the stratification model [9], which forms a (optionally time-based) set of different layers that segments the contextual data contained in, for example, a video, into multiple layers called strata.…”

Section: Integration Areamentioning

confidence: 99%

Smart Multimedia Information Retrieval

Wagenpfeil

Kevitt²,

Hemmje

2023

Analytics

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The area of multimedia information retrieval (MMIR) faces two major challenges: the enormously growing number of multimedia objects (i.e., images, videos, audio, and text files), and the fast increasing level of detail of these objects (e.g., the number of pixels in images). Both challenges lead to a high demand of scalability, semantic representations, and explainability of MMIR processes. Smart MMIR solves these challenges by employing graph codes as an indexing structure, attaching semantic annotations for explainability, and employing application profiling for scaling, which results in human-understandable, expressive, and interoperable MMIR. The mathematical foundation, the modeling, implementation detail, and experimental results are shown in this paper, which confirm that Smart MMIR improves MMIR in the area of efficiency, effectiveness, and human understandability.

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Section: Integration Areamentioning

confidence: 99%

Smart Multimedia Information Retrieval

Wagenpfeil

Kevitt²,

Hemmje

2023

Analytics

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“…In order to process queries such as Q1, Q2 or Q3 discussed in Section 3.2, AI4MMRA is introduced as a specialized AI algorithm to index and retrieve multimedia content. It is based on the IVIS reference model for advanced interfaces [88] and adapts this model to be compatible with the EDISON project [89].…”

Section: Ai4mmramentioning

confidence: 99%

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

Wagenpfeil

Engel

Kevitt³

et al. 2021

Information

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To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

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Supporting Data Science in Automotive and Robotics Applications with Advanced Visual Big Data Analytics

Bornschlegl

Hemmje

2020

Advances in Data Science: Methodologies and Applications

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IVIS4BigData: A Reference Model for Advanced Visual Interfaces Supporting Big Data Analysis in Virtual Research Environments

Cited by 20 publications

References 21 publications

Smart Multimedia Information Retrieval

Smart Multimedia Information Retrieval

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

Supporting Data Science in Automotive and Robotics Applications with Advanced Visual Big Data Analytics

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