Real-time criminal event detection system for live social data streams Jennifer Woodard sisu labs - semantic technology company specialised in real time text analysis for LEAs and other security stakeholders. - Experienced in coordination of EC projects under FP7 and as technical partner under H2020. Role: Proposal Coordinator, WP leader and S/T provider (proprietary NLP/text mining/data mining/predictive analytics technologies) Proposal activity: SEC -12 – FCT Technologies for prevention, investigation, and mitigation in the context of fight against crime and terrorism SMIG January

Concept and Features INSIKT proposes a a real-time event detection and threat prediction technology for LEA, powered by NLP, SNA and Deep Learning. INSIKT will offer: Real time analysis and detection powered by NLP, which structures unstructured social data for accurate threat detection Deep Learning algorithms for highly accurate detection and prediction capabilities + Context Awareness Operational and technical interoperability by integrating with LEA’s existing infrastructure and data sources Multilingual analysis features Evidence collection, sharing and preservation tools Available on any social source, well as other public sources or internal LEA sources Visualisation of location-based data on heat maps Analysis results converted into social graphs to visualize relationships between entities, users, locations and more SMIG January

Tech Overview and Participants Proposed coordinator: sisu labs (open to others) Other participants: Event Detection Expert (Industry - large multinational - Israel) SNA expert (SME- Hungary) SNA expert (University – Hungary) Big Data Software Development (SME – Romania) 3 LEA end users (locations confidential) SMIG January Looking for partners with the following expertise/technology/application field: -Data mining and Machine Learning -Multilingual NLP -Location data experts -Data security and evidence preservation -Terrorism and Crime Domain Experts -Technology Integrators - LEA end users