Dynamic Parsing Rules
The XpoLog Virtual Data Engine (VDE) has a powerful data virtualization technology that allows for a dynamic creation of parsing rules.
With autodetection of log formats, our parsing technology brings an easy-to-use approach to creating plugins and adapters for any source.
XpoLog VDE allows virtual manipulation of data sets, on-parse computation, data masking, semantic terms extraction, and much more.
With XpoLog, you can choose between sitting back and letting us do the parsing for you, or you can take charge and make your own parsing rules.
XpoLog Viritual Data Engine:
We give you the tools. You give us the rules.
Autodetected Augmented Analytic Layers
Accelerate time to action with the XpoLog Analytic Engine. Manual searches are limited to the pre-knowledge of the users.
XpoLog lets you amplify and augment your intelligence with unique autodetected layers of in-depth analytics.
These In-Depth Analytics Insights offer valuable information in the context of manual and data search, they even come with a severity level so you know what needs urgent attention, and users from various groups within your organization discover anomalies faster.
Users discover unknown issues and patterns they never could have guessed were there, and these issues can have a critical effect on your IT and business success.
XpoLog Analytic Engine:
Accelerate time to action.
Autotag errors and severity levels
The XpoLog In-Depth Semantic processing identifies each log event, scans it, and compares its content against an ontology (XpoLog’s unique proprietary dictionary).
Our technology helps identify whether the content of the event indicates there is an error, and what the severity of this error is.
Every unique signed event is tracked, aggregated, and constantly monitored for its appearance across all sources.
By applying this technology, XpoLog automatically assigns every anomaly a severity level.
This awesome automatic severity level tagging feature is unique to XpoLog.
XpoLog In-Depth Auto-tagging:
Every error is automatically distributed a severity level.
Finding the unknown patterns
Continuous delivery and constant creativity is a complex challenge as everything is changing all the time, and the more alterations, the more room for errors.
XpoLog Anomaly patterns tackle those challenges with a unique approach to computing anomalies.
XpoLog anomaly detection is based on specific system management use cases, combining the technology for unique messages profiling of log data with multivariant statistic models, thus focusing on detecting and finding all unknown patterns in your applications and IT data.
XpoLog Anomaly Detection:
No log left unturned, no data left adjourned.
In the midst of intense technology, to us, you are still unique
XpoLog In-Depth Analytic technologies allow users to modify priorities and variants in order to calibrate our technology to their own unique needs.
Along with this approach, we have developed an internal audit mechanism that constantly tracks user searches, operations, and errors in order to dynamically modify and calibrate our algorithms to the ever-changing state of your apps and systems.
XpoLog Machine Learning Technology:
Keeping the complex, yet simplifying it for you.
High potential growth of your data? We already took care of it.
Data complexity and source numbers will increase exponentially in the near future, with more devices reporting data to your platform. In order to be able to manage, orchestrate, secure, and quickly browse through millions of logs and data sources, we have developed a robust, dynamic virtual structure mechanism that helps govern complex environments.
XpoLog brings a fresh approach to data management, by creating virtual structured tags to data sources along with the ability to create virtual data sources so that administrators can greatly simplify how the data is organized from a user-friendly UI. Later these structures can be used for security, management, search, reports, and more.
XpoLog Data Structure:
Because size doesn’t matter.
Build the foundations for knowledge reuse
In order to build apps that will work out of the box and in order to be able to reuse knowledge and data science, we have created an abstraction layer for advanced tagging.
Our tagging mechanism is built with multi-purpose in mind, to tag errors and events, and also to create virtual flows, structures, and descriptors for log patterns, sources, events, information, and more.
Our indexing engine utilizes business application tags and data tagging to build a rich metadata indexing structure that later is used with other computation algorithms.
Knowing when and what to recycle.