In case of a major event, such as a natural disaster, authorities need to collect and analyze big data as quickly as possible for disaster relief. Also, investigators rely on big data collection and analysis for getting actionable insights to wrap up their investigations. In all these cases, data is collected from open-sources such as the surface, deep and dark web, social media, online platforms, blogs, message boards, and online articles.

When collecting these huge volumes of OSINT data, link analysis is needed to visualize the digital footprints that e.g., a stranded or missing natural disaster victim or a threat actor leave behind. Link analysis helps to understand behaviors, tastes, and preferences for locating victims or identifying threat actors by visually connecting and mapping those digital footprints as interactive node-links. Such visualization allows analysts and investigators to detect patterns and anomalies for an in-depth understanding of the collected data. Advanced visual link analyses reveal and visualize hidden and connected web data and present it as a map displaying influencing nodes and social communities to reveal patterns.

In the case of e.g., forest fires, timeline visualization allows for visualizing sequences of outbreaks, how they progress, and how they are connected. It gives authorities a clear picture of the chronology of the fires highlighting patterns. This insight can be used for locating victims to provide assistance.

Link analysis is therefore crucial, even more so since the volume and complexity of the online data to be collected and analyzed keeps growing and growing. To address this problem, more and more enterprises, institutions, and government organizations are opting for a data visualization platform, such as the AI-powered WEBINT platform of Cobwebs, to automatically gain quick and accurate insights into the collected big data for follow-up.

Such a platform provides link analysis and timeline visualization for understanding the data connections and extracting useful and actionable insight in near real-time, allowing for:

• Smarter investigations, since all data sources are fused;
• Sharing information for informed operational decisions, such as rescue operations;
• Visualizing data collected from all intelligence sources quickly and in an advanced manner in one single interactive workspace;
• Minimizing the time spent on manual administrative tasks by creating, reviewing, and delivering actionable insights in a timely manner using an AI-powered WEBINT platform;
• Creating a centralized knowledge repository to maintain investigative intelligence that can be used by analysts in the future for other events or investigations.

To conclude, timeline visualization and link analysis enable analysts and investigators to get a clear insight into vast amounts of collected big data. They will be able to see patterns in sequences of events as well as map the connections between them for follow-up. It allows them to conduct analyses and investigations for getting the information they need to make the right decisions with limited resources in highly dynamic circumstances.