Our solution

Our solution is based on an automated technology that can construct easily manageable databases from large amount of poorly structured, machine-generated data and requires only the minimal amount of human intervention to identify and correlate to the original event flow model and identify non-customary elements.

The obtained information can help decision-makers to take sound measures, consider risks and bottlenecks, the current future states of processes and systems.

The system can handle complexity so that previously unfeasible analysis of vast amount of data generated by IT systems becomes daily routine.

Such complex situations include:

  • Continuous flow of unstructured, non-uniform data.
  • Increasing amount of information to be analysed.
  • Various log formats and less and less time for comprehensive analysis.
  • Separation of noise and valuable information is difficult and will slow down the work of experts.

Modules

Our solution is modular, so our users can always implement a cost-effective analytical system that truly serves their real needs.
Selected modules can be implemented according to your business requirements.

Data collection

This module collects unstructured machine generated data and converts it into analysable data sets in a cost-effective manner. The basis for our solution is scalable according to the users’ individual needs, providing them with a cost-effective solution.

Data management

The module can generate compressed, anonymized aggregates from the extracted data for effective analysis.

During normalization

During normalization – we determine the effective data structure, eliminate redundancy, thus reducing the need for maintenance and storage. The resulting information clusters will accurately describe the given part of the database and allow the modification of the database at a single point of interaction. At the end of day you will have store less data without discarding any valuable information.

Compression

Compression – storage needs are significantly reduced. We perform lossless compression, so the process can be reversed, your original raw data can always be recovered. Compression accelerates data transfer, so in a business intelligence environment the speed of communication increases.

Anonymization

Anonymization – sensitive data are transformed into codes to protect personality rights, business secrecy and confidentiality. The development guarantees that the anonymized database still contains the exact same information and is ready for forecasts and further analysis.

Analysis and modelling

For the forecast module, the input is the database generated by the data collection and data management modules.
This module ultimately makes your business processes transparent, therefore provides valuable support for your business decisions, controlling processes, resource planning. Our forecasting analysis rely on a wide array of statistical methods. These tried and tested mathematical methods act like building blocks which can be implemented according to the needs of the developers. Our models are validated and extensively tested in order to ensure seamless functionality even in extreme conditions.

To eliminate errors, it is highly important to keep the models up to date at all times. The registration, testing and validation processes of predictive models, and the continuous monitoring of the life cycle, eliminates all possible errors. The solution integrates many self-learning improvements and supports multiple query languages so that updating the models requires only the minimum human resources from your organisation.

Prediction

For the forecast module, the input is the database generated by the logging and processing module.

Our forecasting analysis rely on a wide array of statistical methods. These tried and tested mathematical methods act like building blocks which can be implemented according to the needs of the developers.  Our models are validated and extensively tested in order to ensure seamless functionality even in extreme conditions.

To eliminate errors, it is highly important to keep the models up to date at all times. The registration, testing and validation processes of predictive models, and the continuous monitoring of the life cycle, eliminates all possible errors. The solution integrates many self-learning improvements and supports multiple query languages so that updating the models requires only the minimum human resources from your organisation.