{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853986","patent":{"patent_number":"US-9853986","title":"Clustering event data by multiple time dimensions","assignee":null,"inventors":[],"filing_date":"2012-01-26T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04L","G06F","G06F","G06F","G06F","H04L","G06F","G06F","H04L"],"num_claims":20,"abstract":"Systems and methods for processing log data are provided. A set of data chunks is determined. Each data chunk is associated with a set of events, which are grouped according to a primary time dimension field of each event of the set of events. A metadata structure is determined for each of the data chunks. The metadata structure includes comprises a range of the primary time dimension field of all of the events in the data chunk and a range of a secondary time dimension field of all of the events in the data chunk. A subset of the data chunks is selected. A data chunk associated with at least one event of the plurality of events is generated according to the secondary time dimension field of the at least one event."},"analysis":{"summary":"The Clustering Event Data by Multiple Time Dimensions patent details a system and method for processing log data more efficiently. The core innovation lies in grouping events based on multiple time dimensions, allowing for a more nuanced analysis of event sequences and correlations. This approach addresses the problem of overwhelming log data volumes and the difficulty in identifying meaningful patterns using traditional methods. The key technical approach involves determining data chunks associated with events, grouping these events according to a primary time dimension, and then generating metadata structures that include ranges of both primary and secondary time dimensions. This allows for the selection of data chunk subsets and the generation of new data chunks based on the secondary time dimension.\n\nThe business value of this technology is significant. It enables organizations to improve their cybersecurity posture by identifying malicious activity patterns, optimize IT operations by quickly troubleshooting system issues, and enhance business analytics by gaining a more comprehensive view of event data. The market opportunity is vast, as the need for efficient log data analysis is growing across various industries, including finance, healthcare, and technology. By providing a more granular and insightful approach to data analysis, this technology empowers businesses to make better decisions, optimize processes, and improve overall performance. This innovation represents a significant advancement in log data processing, offering a competitive edge to organizations that adopt it. The ability to analyze data across multiple time dimensions unlocks new insights and opportunities for data-driven decision-making, making it a valuable asset for businesses of all sizes. This method significantly improves the efficiency and accuracy of log data analysis, providing a clear return on investment for organizations struggling with data overload.","layman_explanation":"The Clustering Event Data by Multiple Time Dimensions patent addresses the challenge of analyzing vast amounts of log data generated by computer systems and applications. These logs contain records of events that occur over time, such as user logins, system errors, and network traffic. Analyzing this data is crucial for identifying security threats, troubleshooting performance issues, and understanding user behavior. However, the sheer volume and complexity of log data can make it difficult to extract meaningful insights.\n\nTraditional log analysis methods often involve searching for specific keywords or patterns in the data. While this can be effective for identifying known issues, it may not be sufficient for detecting more subtle or complex patterns. Existing solutions often fall short in providing a comprehensive view of event sequences and correlations, making it difficult to identify the root cause of problems quickly.\n\nThis patent introduces a new approach to log analysis that involves grouping events based on multiple time dimensions. Instead of just looking at the timestamp of an event, this technology considers other factors such as the source of the event, the type of event, and the user associated with the event. By clustering events based on these multiple dimensions, the system can identify patterns and anomalies that might be missed by traditional methods. Imagine sorting your mail not just by the date it was sent, but also by the sender and the type of mail it is. This allows you to see relationships and patterns that you wouldn't notice if you just sorted by date.\n\nThis technology matters because it enables organizations to make better decisions, optimize processes, and improve overall performance. For example, a security team could use this technology to identify patterns of malicious activity by analyzing log data from various sources. An IT operations team could use it to troubleshoot system issues by analyzing log data from servers, applications, and network devices. A marketing team could use it to understand user behavior by analyzing log data from websites and mobile apps. The future applications of this technology are vast. As the volume and complexity of log data continue to grow, this innovation promises to play a vital role in unlocking the full potential of this valuable resource. Market adoption is expected to increase as organizations recognize the benefits of multi-dimensional log analysis. This innovation offers significant investment implications for companies looking to improve their data analysis capabilities.","technical_analysis":"The Clustering Event Data by Multiple Time Dimensions patent presents a system for processing log data by clustering events based on multiple time dimensions. The technical architecture involves several key components working in concert. First, a data ingestion module receives log data from various sources. This data is then divided into data chunks by a data chunking module, where each chunk is associated with a set of events. The events within each chunk are initially grouped according to a primary time dimension field, such as the timestamp of the event. A metadata structure is then determined for each data chunk. This metadata includes the range of the primary time dimension field for all events in the chunk, as well as the range of a secondary time dimension field. The secondary time dimension can be any other relevant field in the log data, such as the source IP address, user ID, or event type.\n\nThe algorithm then selects a subset of these data chunks based on certain criteria. This selection process aims to focus the analysis on the most relevant data. Following selection, a new data chunk is generated based on the secondary time dimension field of the events within the selected subset. This effectively re-clusters the data based on the secondary time dimension, allowing for analysis across different time scales and perspectives. Implementation details would involve careful consideration of data structures for efficient storage and retrieval of log data and metadata. The system would likely utilize indexing techniques to speed up the selection and clustering processes. Integration patterns would involve APIs for receiving log data from various sources and for exposing the results of the analysis to other systems.\n\nThe performance characteristics of this system would depend on the volume of log data, the complexity of the clustering algorithm, and the efficiency of the data structures and indexing techniques. Code-level implications include the need for robust error handling and scalability to handle large volumes of data. The system should be designed to be fault-tolerant and able to recover from failures. The use of parallel processing techniques could further improve performance. This patent provides a solid foundation for building a powerful and flexible log data analysis system. The multi-dimensional clustering approach allows for a more nuanced and insightful analysis of event data, with potential applications in various domains, including cybersecurity, IT operations, and business analytics.","business_analysis":"The Clustering Event Data by Multiple Time Dimensions patent addresses a significant market need: the efficient and effective analysis of log data. The market opportunity is substantial, as organizations across various industries are generating massive amounts of log data that need to be analyzed for security, performance, and business intelligence purposes. The competitive advantages of this technology stem from its ability to group events based on multiple time dimensions, providing a more granular and insightful view of event sequences and correlations. This allows for the identification of patterns and anomalies that might be missed by traditional methods.\n\nThe revenue potential for this technology is significant. It can be monetized through various business models, such as software licensing, subscription services, and consulting services. Strategic positioning would involve targeting organizations that are struggling with log data overload and need a more efficient and effective analysis solution. This innovation offers a clear return on investment (ROI) by reducing downtime, improving security, and enabling better decision-making. ROI projections would depend on the specific application and the size of the organization. For example, a large financial institution could potentially save millions of dollars per year by using this technology to improve its cybersecurity posture.\n\nThis patent offers a compelling value proposition for businesses looking to improve their log data analysis capabilities. Its ability to group events based on multiple time dimensions provides a more nuanced and insightful view of event data, enabling organizations to make better decisions, optimize processes, and improve overall performance. As the volume and complexity of log data continue to grow, this technology promises to play a vital role in unlocking the full potential of log data. The strategic implications of this patent are far-reaching, offering a competitive advantage to organizations that adopt it.","faqs":null,"topics":["log data analysis","event clustering","time dimension","data processing","cybersecurity"],"tech_cluster":null},"seo":{"title":"Clustering Event Data by Multiple Time Dimensions - Patent US-9853986","description":"Discover how Clustering Event Data by Multiple Time Dimensions revolutionizes log analysis. Explore the patent's technical details, claims, and applications.","keywords":["log data analysis","event clustering","time dimension","data processing","cybersecurity","IT operations","business analytics","patent","patent US-9853986"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853986","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9853986","citation_suggestion":"Patentable. \"Clustering event data by multiple time dimensions\" (US-9853986). https://patentable.app/patents/US-9853986","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853986","json":"https://patentable.app/api/llm-context/US-9853986","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T17:58:22.460Z"}