As with any custom software development project or enterprise software application,there were many moving parts. All aspects of YABEX such as database integration,software performance, and intuitive user interface must work together to achieve thedesired results.

how was the journey to build YABEX?A: it begun with requirement analysis, planning and design, data processing,development, testing, deployment, AI and machine learning integration and finally ledto maintenance.

This involved creating a comprehensive project plan that outlines timelines, resourceallocations, and budgeting. Tools like Gantt charts, project management software (e.g.,JIRA, Asana, Trello), and resource management tools often used here. The plan shouldinclude milestones, deliverables, and a clear timeline for each phase of the project.

The technical architecture of YABEX is designed in this stage. This includes decidingon the programming languages such as PHP and Python. The development team usuallydrew robust and complicated diagrams of the software’s architecture that can behelpful in visualizing the system’s structure.

Deciding on the data structures and algorithms for data indexing, searching, and ranking was vital. This stage might involved using algorithm simulation tools or flowchart software to map out the logic and flow of these key components.

Choosing the right development methodology was crucial for a smooth and agile development process. Agile methodologies like Scrum or Kanban were popular choices as they allow for flexibility, iterative development, and continuous feedback. These methodologies facilitated quick adjustments based on testing results or changing requirements.

YABEX typically do not host data themselves, but rather aggregate data from various other search engines or databases. YABEX rely heavily on APIs (Application Programming Interfaces) to pull real-time data from various sources. For example, it uses APIs to pull flight, hotel, and car rental information from various travel sites and airlines. also, data collection may involve web crawlers that scrape data from various travel-related websites, including airlines, hotels, and review sites. They may also integrate APIs for specific data sources where scraping is not feasible or where real-time data is required.

Once the data is collected, it needs to be indexed. This involves organizing the data in a way that makes it easily searchable. The approach to indexing may differ based on the volume and variety of data. This is crucial for ensuring data quality. It involves removing duplicates, correcting errors, and converting data into a consistent format. For travel search engines, this might include standardizing date formats, location names, and pricing information.

YABEX used web crawlers, but their application would vary. In YABEX, crawlers might be more extensive and complex, designed to extract detailed information from various sources. In YABEX, crawlers used more for updating links or integrating new sources. APIs are essential for us, YABEX rely on them to fetch data from external sources. It also utilizes APIs, especially for components like flight status updates, where real-time data is critical.

The choice of YABEX database (SQL or NoSQL) and its design will depend on the travel data’s nature. we used relational database which is more suitable for structured data with clear relationships, while NoSQL could be better for more varied or unstructured data.

How do you use AI and machine learning in YABEX? A: We provide the most comprehensive travel data mining platform that provides the opportunities of comparing and selecting the most trustable and affordable OTA, We created a genius engine that could predict flight prices in the following days by analyzing massive collected travel data during the last two years and using artificial intelligence. Machine learning helped us explore customers' behaviors in purchasing flight tickets and created a travel decision-making feature that could decide to buy flight tickets now or later.

meta search engine systems struggling with scalability limitations due to its architecture, which relied on SQL Server machines and was incapable of efficient horizontal scaling. To address these issues, YABEX implemented a solution that included an in-memory data grid (IMDG) platform for the availability search function, reducing the reliance on the SQL Server database. This new system allowed the necessary data to be stored in memory, supported by a custom data loading mechanism and a message queue pipeline for incremental intraday data updates. The results were significant: an 80% reduction in infrastructure costs, improved horizontal scalability with 70% efficiency, and the ability to handle 50% more traffic.