Pre-Event

Event

Solvex25

# Problem Statement:
Application logs are critical for debugging and monitoring, but their volume and complexity often
overwhelm teams. The challenge is to create an AI-driven log analyzer that processes logs across multiple
sources, extracts actionable insights, and automates log analysis.
**Key Features for the Application AI Log Analyzer:**
1. Log Parsing and Aggregation:
- Collect logs from multiple sources (application servers, databases, web servers).
- Support various log formats (JSON, XML, plain text).
- Automatically group related logs for better context.
2. AI-Powered Log Analysis:
- Use NLP and ML to extract insights from unstructured logs.
- Detect anomalies, recurring issues, and patterns for root cause analysis.
- Use clustering algorithms to group similar errors or warnings.
3. Ability to Train Log Meanings:
- Provide a feature to train the system on the meaning and structure of specific log messages.
- Allow users to label log types (e.g., error, warning, info) to improve accuracy.
- Support custom rules or models to interpret logs better.
4. Visualization and Reporting:
- Interactive dashboards to display key trends, error frequencies, and system performance.
- Drill-down views for deeper analysis of issues.
- Export reports in PDF or CSV for documentation.
5. Alerts and Notifications:
- Real-time alerts for critical errors or unusual patterns (via email, Slack, or SMS).
- Configurable thresholds to avoid alert fatigue.
6. Search and Query:
- Advanced search functionality using keywords, filters, or regular expressions.
- Query logs by application, module, or error type for targeted analysis.
7. Predictive Insights:
- Use historical data to forecast potential failures or bottlenecks.
- Suggest preventive measures based on observed trends.
8. Role-Based Access Control:
- Implement role-based access for developers, admins, and managers.
- Ensure log visibility is controlled per project or team.
9. Customizable Modules:
- Offer plugin support to extend functionality for specific applications or environments.
- Allow users to integrate third-party tools for enhanced analysis.

AI Application Log Analyzer

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Ethereum Track

ETHIndia

# Problem Statement:
Operating system (OS) issues can be complex and frustrating for users to troubleshoot. Delays in resolving these problems can affect productivity. The challenge is to create an AI-driven agent that can diagnose OS-level problems (e.g., performance issues, software conflicts, or network errors) and suggest or implement solutions efficiently.

**Key Features for the AI Agent:**
1. Problem Diagnosis: Identify software performance issues and driver problems. Monitor system logs for error patterns.
2. Solution Recommendations: Provide step-by-step guidance, suggest updates, driver installations, or optimizations. Automate fixes where possible.
3. User Interaction: Use Natural Language Processing (NLP) for chatbot-based user-friendly interactions, including voice-based assistance.
4. Predictive Analysis: Warn users about potential failures based on system trends and suggest preventive actions (e.g., clearing temporary files, scheduling updates).
5. Customization: Allow users to set thresholds for alerts (e.g., CPU usage above 90%) and tailor recommendations based on expertise.
6. Reporting & Insights: Generate detailed reports on diagnosed issues and actions taken. Provide system health scores and improvement tips.
7. Security Alerts: Identify and mitigate unauthorized software or malware threats.