View source for Dynatrace LLC (20240232158). Compact Probabilistic Data Structure For Storing Log Data simplified abstract

=Compact Probabilistic Data Structure For Storing Log Data=


==Organization Name==

[[:Category:Dynatrace LLC|Dynatrace LLC]]


[[Category:Dynatrace LLC]]

==Inventor(s)==

[[:Category:Julian Reichinger of Sankt Johann Am Wimberg (AT)|Julian Reichinger of Sankt Johann Am Wimberg (AT)]][[Category:Julian Reichinger of Sankt Johann Am Wimberg (AT)]]

[[:Category:Renee Trisberg of Muraste (EE)|Renee Trisberg of Muraste (EE)]][[Category:Renee Trisberg of Muraste (EE)]]

==Compact Probabilistic Data Structure For Storing Log Data - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240232158 titled 'Compact Probabilistic Data Structure For Storing Log Data

The abstract of the patent application describes a computer-implemented method for storing log data in a distributed computing environment. The method involves receiving log lines, tokenizing them using different rules, combining the tokens, storing them using hash functions, updating a listing of computing entities, and storing the hash values in a token map table of a probabilistic data structure.

* Receiving log lines associated with computing sources
* Tokenizing log lines using different rules to create base, combination, and n-gram tokens
* Storing tokens using hash functions to generate hash values
* Updating a listing of computing entities with computing sources
* Storing hash values and addresses in a token map table of a probabilistic data structure

Potential Applications:
- Data storage and retrieval in distributed computing environments
- Log data analysis and monitoring in large-scale systems
- Security and access control in computing networks

Problems Solved:
- Efficient storage and retrieval of log data in distributed environments
- Scalability and performance issues in handling large volumes of log data
- Identification and tracking of computing sources producing log data

Benefits:
- Improved data organization and management
- Enhanced security and access control mechanisms
- Scalable and efficient log data storage and retrieval

Commercial Applications:
Title: "Efficient Log Data Storage and Management System for Distributed Computing Environments"
This technology can be utilized in cloud computing platforms, big data analytics companies, cybersecurity firms, and network monitoring solutions providers. It offers a robust solution for handling log data in complex computing environments, enhancing operational efficiency and data security.

Questions about the technology:
1. How does the method of tokenization improve log data storage efficiency?
2. What are the advantages of using a probabilistic data structure for storing hash values in log data management systems?


==Original Abstract Submitted==

a computer-implemented method is presented for storing log data generated in a distributed computing environment. the method includes: receiving a log line, where the log line is associated with a given computing source producing the log line; applying a first tokenization rule to create a plurality of base tokens; applying a second tokenization rule to create a plurality of combination tokens; applying a third tokenization rule to create a plurality of n-gram tokens; combining the plurality of tokens into a set of tokens; for each token in the set of tokens, storing a given token by applying a hash function to the given token to generate a hash value, where the given token is associated with a given computing source at which the log line was produced; updating a listing of computing entities with the given computing source, where entries in the listing of computing entities can identify more than one computing sources and each entry in the listing of computing entities specifies a unique set of computing sources; and storing the hash value, along with an address, in a token map table of a probabilistic data structure, where the address maps the hash value to an entry in the listing of computing entities.



[[Category:G06F16/22]]
[[Category:CPC_G06F16/2255]]