A Unified Knowledge Graph to Permit Interoperability of Heterogeneous Digital Evidence
- URL: http://arxiv.org/abs/2402.13746v1
- Date: Wed, 21 Feb 2024 12:17:33 GMT
- Title: A Unified Knowledge Graph to Permit Interoperability of Heterogeneous Digital Evidence
- Abstract summary: This paper presents a novel approach in the form of a modern unified data graph.
The proposed approach aims to seamlessly integrate, harmonise, and unify evidence data.
It enables cross-platform interoperability, efficient data queries, and improved digital investigation performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The modern digital world is highly heterogeneous, encompassing a wide variety of communications, devices, and services. This interconnectedness generates, synchronises, stores, and presents digital information in multidimensional, complex formats, often fragmented across multiple sources. When linked to misuse, this digital information becomes vital digital evidence. Integrating and harmonising these diverse formats into a unified system is crucial for comprehensively understanding evidence and its relationships. However, existing approaches to date have faced challenges limiting investigators' ability to query heterogeneous evidence across large datasets. This paper presents a novel approach in the form of a modern unified data graph. The proposed approach aims to seamlessly integrate, harmonise, and unify evidence data, enabling cross-platform interoperability, efficient data queries, and improved digital investigation performance. To demonstrate its efficacy, a case study is conducted, highlighting the benefits of the proposed approach and showcasing its effectiveness in enabling the interoperability required for advanced analytics in digital investigations.
Related papers
- Enhancing Multimodal Retrieval via Complementary Information Extraction and Alignment [51.96615529872665]
We propose CIEA, a novel multimodal retrieval approach that transforms both text and images in documents into a unified latent space.<n>We optimize CIEA using two complementary contrastive losses to ensure semantic integrity and effectively capture the complementary information contained in images.
arXiv Detail & Related papers (2026-01-08T04:02:49Z) - Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysis [24.70488591952602]
Social media platforms generate massive volumes of heterogeneous data.<n>We present SIA (Social Insight Agents), an agent system that links heterogeneous multi-modal data.<n>We show that SIA effectively discovers diverse and meaningful insights from social media.
arXiv Detail & Related papers (2025-10-30T06:22:49Z) - Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
arXiv Detail & Related papers (2025-06-11T03:29:18Z) - GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights [0.0]
This paper introduces GridMind, a framework that unifies structured, semi-structured, and unstructured data through Retrieval-Augmented Generation (RAG) and large language models (LLMs)
This approach aligns with the evolving field of multimodal representation learning, where unified models are increasingly essential for real-time, cross-modal interactions.
arXiv Detail & Related papers (2025-03-24T18:33:36Z) - Multimodal Alignment and Fusion: A Survey [7.250878248686215]
Multimodal integration enables improved model accuracy and broader applicability.
We systematically categorize and analyze existing alignment and fusion techniques.
This survey focuses on applications in domains like social media analysis, medical imaging, and emotion recognition.
arXiv Detail & Related papers (2024-11-26T02:10:27Z) - Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach [10.376378437321437]
We propose a Multimedia Misinformation Detection framework for detecting misinformation from video content by leveraging cross-modal entity consistency.
Our results demonstrate that MultiMD outperforms state-of-the-art baseline models.
arXiv Detail & Related papers (2024-08-16T16:14:36Z) - Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression [0.6345523830122168]
Conventional approaches struggle to cope with the escalating complexity and scale of multimedia data.
We proposed framework addresses this challenge by fusing AI-native multi-modal search capabilities with neural image compression.
Our work marks a significant advancement towards scalable and efficient multi-modal search systems in the era of big data.
arXiv Detail & Related papers (2024-04-16T02:29:00Z) - Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method [3.0903319879656084]
This paper introduces an innovative approach to feature alignment that revolutionizes the fusion of multimodal information.
Our method employs a novel iterative process of telescopic displacement and expansion of feature representations across different modalities, culminating in a coherent unified representation within a shared feature space.
arXiv Detail & Related papers (2023-06-29T13:49:06Z) - Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
multimodal machine learning that incorporates data from various sources has become an increasingly popular research area.
We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions.
We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
arXiv Detail & Related papers (2022-10-05T13:14:57Z) - Latent Heterogeneous Graph Network for Incomplete Multi-View Learning [57.49776938934186]
We propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning.
By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized.
To avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks.
arXiv Detail & Related papers (2022-08-29T15:14:21Z) - Causal Reasoning Meets Visual Representation Learning: A Prospective
Study [117.08431221482638]
Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models.
Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms.
This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
arXiv Detail & Related papers (2022-04-26T02:22:28Z) - Multimodal Image Synthesis and Editing: The Generative AI Era [131.9569600472503]
multimodal image synthesis and editing has become a hot research topic in recent years.
We comprehensively contextualize the advance of the recent multimodal image synthesis and editing.
We describe benchmark datasets and evaluation metrics as well as corresponding experimental results.
arXiv Detail & Related papers (2021-12-27T10:00:16Z) - A Variational Information Bottleneck Approach to Multi-Omics Data
Integration [98.6475134630792]
We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations.
Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target.
Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
arXiv Detail & Related papers (2021-02-05T06:05:39Z) - Digital Twins: State of the Art Theory and Practice, Challenges, and
Open Research Questions [62.67593386796497]
This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin.
The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
arXiv Detail & Related papers (2020-11-02T19:08:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.