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Mansa Musa was the ruler of the Mali Empire in West Africa. Details recorded here should be sourced; unknown information is left blank.
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This is a personal recollection on the Move fire on May 13, 1985
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In computer log management and intelligence, log analysis (or system and network log analysis) is an art and science seeking to make sense of computer-generated records (also called log or audit trail records). The process of creating such records is called data logging.
Typical reasons why people perform log analysis are:
Logs are emitted by network devices, operating systems, applications and all manner of intelligent or programmable devices. A stream of messages in time sequence often comprises a log. Logs may be directed to files and stored on disk or directed as a network stream to a log collector.
Log messages must usually be interpreted concerning the internal state of its source (e.g., application) and announce security-relevant or operations-relevant events (e.g., a user login, or a systems error).
Logs are often created by software developers to aid in the debugging of the operation of an application or understanding how users are interacting with a system, such as a search engine. The syntax and semantics of data within log messages are usually application or vendor-specific. The terminology may also vary; for example, the authentication of a user to an application may be described as a log in, a logon, a user connection or an authentication event. Hence, log analysis must interpret messages within the context of an application, vendor, system or configuration to make useful comparisons to messages from different log sources.
Log message format or content may not always be fully documented. A task of the log analyst is to induce the system to emit the full range of messages to understand the complete domain from which the messages must be interpreted.
A log analyst may map varying terminology from different log sources into a uniform, normalized terminology so that reports and statistics can be derived from a heterogeneous environment. For example, log messages from Windows, Unix, network firewalls, and databases may be aggregated into a "normalized" report for the auditor. Different systems may signal different message priorities with a different vocabulary, such as "error" and "warning" vs. "err", "warn", and "critical".
Hence, log analysis practices exist on the continuum from text retrieval to reverse engineering of software.
Pattern recognition is a function of selecting incoming messages and compare with a pattern book to filter or handle different ways.
Normalization is the function of converting message parts to the same format (e.g. common date format or normalized IP address).
Classification and tagging is ordering messages into different classes or tagging them with different keywords for later usage (e.g. filtering or display).
Correlation analysis is a technology of collecting messages from different systems and finding all the messages belonging to one single event (e.g., messages generated by malicious activity on different systems: network devices, firewalls, servers, etc.). It is usually connected with alerting systems.
Artificial Ignorance is a type of machine learning that is a process of discarding log entries that are known to be uninteresting. Artificial ignorance is a method to detect anomalies in a working system. In log analysis, this means recognizing and ignoring the regular, common log messages that result from the normal operation of the system, and therefore are not too interesting. However, new messages that have not appeared in the logs before can signal important events, and should be therefore investigated.[1][2] In addition to anomalies, the algorithm will identify common events that did not occur. For example, a system update that runs every week, has failed to run.
Log Analysis is often compared to other analytics tools such as application performance management (APM) and error monitoring. While much of their functionality is clear overlap, the difference is rooted in process. APM has an emphasis on performance and is utilized most in production. Error monitoring is driven by developers versus operations, and integrates into code in exception handling blocks.
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In computer log management and intelligence, log analysis (or system and network log analysis) is an art and science seeking to make sense of computer-generated records (also called log or audit trail records). The process of creating such records is called data logging. Typical reasons why people perform log analysis are: Compliance with security policies Compliance with audit or regulation System troubleshooting Forensics (during investigations or in response to a subpoena) Security incident response Understanding online user behavior Logs are emitted by network devices, operating systems, applications and all manner of intelligent or programmable devices. A stream of messages in time sequence often comprises a log. Logs may be directed to files and stored on disk or directed as a network stream to a log collector. Log messages must usually be interpreted concerning the internal state of its source (e.g., application) and announce security-relevant or operations-relevant events (e.g., a user login, or a systems error). Logs are often created by software developers to aid in the debugging of the operation of an application or understanding how users are interacting with a system, such as a search engine. The syntax and semantics of data within log messages are usually application or vendor-specific. The terminology may also vary; for example, the authentication of a user to an application may be described as a log in, a logon, a user connection or an authentication event. Hence, log analysis must interpret messages within the context of an application, vendor, system or configuration to make useful comparisons to messages from different log sources. Log message format or content may not always be fully documented. A task of the log analyst is to induce the system to emit the full range of messages to understand the complete domain from which the messages must be interpreted. A log analyst may map varying terminology from different log sources into a uniform, normalized terminology so that reports and statistics can be derived from a heterogeneous environment. For example, log messages from Windows, Unix, network firewalls, and databases may be aggregated into a "normalized" report for the auditor. Different systems may signal different message priorities with a different vocabulary, such as "error" and "warning" vs. "err", "warn", and "critical". Hence, log analysis practices exist on the continuum from text retrieval to reverse engineering of software.
Web log analysis software (also called a web log analyzer) is a kind of web analytics software that parses a server log file from a web server, and based on the values contained in the log file, derives indicators about when, how, and by whom a web server is visited. Reports are usually generated immediately, but data extracted from the log files can alternatively be stored in a database, allowing various reports to be generated on demand. Features supported by log analysis packages may include "hit filters", which use pattern matching to examine selected log data.
Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.)
Log management is the process of generating, transmitting, storing, accessing, and disposing of log data. Log data (or logs) is composed of entries (records), and each entry contains information related to a specific event that occurred within an organization's computing assets, including physical and virtual platforms, networks, services, and cloud environments. Logging events and logging transactions can help form an audit trail. The process of log management generally breaks down into: Log collection - a process of capturing actual data from log files, application standard output stream (stdout), network socket and other sources. Log aggregation (centralization) - a process of putting all the log data together in a single place for the sake of further analysis or/and retention. Log storage and retention - a process of handling large volumes of log data according to corporate or regulatory policies (compliance). Log monitoring and log analysis - a process that helps operations and security team to handle system performance issues and security incidents
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker