- Knowledge Integration Simil allows the user to search documents that are collected in various repositories. It enables the integration of several ITSs (Issue Tracking Systems), repositories with technical documentation and any other systems thanks to open architecture. Very often the document life cycle ends along with the project with which it was connected. These documents often contain information on problems that are currently being resolved. Thanks to data integration, it is easier to find this information and reuse it. Simil is typically used in problem and incident reporting systems, where effective marking of duplicates is essential in order to ensure the smooth functioning of the team carrying out IT support services. RIS Simil learns through errors and previously collected knowledge.
- Knowledge Reusability Simil allows the user to search documents that are collected in various repositories. It enables the integration of several ITSs (Issue Tracking Systems), repositories with technical documentation and any other systems thanks to open architecture. Very often the document life cycle ends along with the project with which it was connected. These documents often contain information on problems that are currently being resolved. Thanks to data integration, it is easier to find this information and reuse it. Simil is typically used in problem and incident reporting systems, where effective marking of duplicates is essential in order to ensure the smooth functioning of the team carrying out IT support services. RIS Simil learns through errors and previously collected knowledge.
- Flexbility Simil has a flexible architecture that allows the users to define repositories and types of documents without interfering with the system code. The user can define document attributes and assign them different significance levels, which can, for example, increase the significance of title or description as well as lower the significance of comments throughout the “learning” cycle. Algorithms of knowledge discovery used by our system are given parameters and consist of autonomous fragments which the user can use or discard. This enables the user to adjust the way the knowledge is presented to the size and character of stored data.
- Uncompromising Performance Simil is fast, efficient and thorough at the same time. We offer an efficient solution where speed is not achieved through compromising effectiveness. For one of our clients we are indexing more than 7 million terms collected in almost 40 thousand documents. All the functions are performed in real time, maintaining all the key features of our algorithms of knowledge discovery.
- Transparent GUI The system’s functionality is accessible through a web application that offers simple and clear interface. Thanks to the use of JQuery, the application’s GUI is flexible and responsive.
System Features:
- Full-text search in a selected collection of documents
- Indicating, with high probability, possible causes of problems
- Calculating similarity between documents, searching for similar documents
- Classification of queries and selected documents
- Configurable and self-learning classifier
- Configurable document definitions with the option to assign different significance level to individual sections
- Support for various data sources: SVN, Quality Center, File system (local or with access after ssh), Any database available after JDBC
- Support for multiple file formats: Txt, Microsoft Office, Open Office, Jpg, bmp, png, gif, PDF