The event logs in CSV format. The dataset contains both correlated and uncorrelated logs

Raw LOG file datasets (.csv) from 'Darwin Board' microcomputers.

Dataset

This dataset was created by Ayush2222

Released under MIT

Contents

This dataset contains log information of a cloud computing infrastructure based on OpenStack.Three different files are available, including the nova, cinder, and glance log files. Due to the fact that the data is unbalanced, a CSV file containing log information of the three OpenStack applications is provided. This can be used for testing in case the log files are used for a machine learning purpose. These data were collected from the Federated Genominc (FEDGEN) cloud computing infrastructure hosted in Covenant Unversity under the Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE) project funded by the World Bank.

CSV file containing column "log" and "type" with device log line samples

Type CSV, CEF, syslog, etc.

Caution there could be some classification errors in the labels themselves. Hopefully I've cleaned them all up.

This dataset was acquired during Cyber Czech – a hands-on cyber defense exercise (Red Team/Blue Team) held in March 2019 at Masaryk University, Brno, Czech Republic. Network traffic flows and a high variety of event logs were captured in an exercise network deployed in the KYPO Cyber Range Platform.

Contents

The dataset covers two distinct time intervals, which correspond to the official schedule of the exercise. The timestamps provided below are in the ISO 8601 date format.

• Day 1, March 19, 2019
  • Start: 2019-03-19T11:00:00.000000+01:00
  • End: 2019-03-19T18:00:00.000000+01:00
• Day 2, March 20, 2019
  • Start: 2019-03-20T08:00:00.000000+01:00
  • End: 2019-03-20T15:30:00.000000+01:00

The captured and collected data were normalized into three distinct event types and they are stored as structured JSON. The data are sorted by a timestamp, which represents the time they were observed. Each event type includes a raw payload ready for further processing and analysis. The description of the respective event types and the corresponding data files follows.

• cz.muni.csirt.IpfixEntry.tgz – an archive of IPFIX traffic flows enriched with an additional payload of parsed application protocols in raw JSON.
• cz.muni.csirt.SyslogEntry.tgz – an archive of Linux Syslog entries with the payload of corresponding text-based log messages.
• cz.muni.csirt.WinlogEntry.tgz – an archive of Windows Event Log entries with the payload of original events in raw XML.

Each archive listed above includes a directory of the same name with the following four files, ready to be processed.

• data.json.gz – the actual data entries in a single gzipped JSON file.
• dictionary.yml – data dictionary for the entries.
• schema.ddl – data schema for Apache Spark analytics engine.
• schema.jsch – JSON schema for the entries.

Finally, the exercise network topology is described in a machine-readable NetJSON format and it is a part of a set of auxiliary files archive – auxiliary-material.tgz – which includes the following.

• global-gateway-config.json – the network configuration of the global gateway in the NetJSON format.
• global-gateway-routing.json – the routing configuration of the global gateway in the NetJSON format.
• redteam-attack-schedule.{csv,odt} – the schedule of the Red Team attacks in CSV and ODT format. Source for Table 2.
• redteam-reserved-ip-ranges.{csv,odt} – the list of IP segments reserved for the Red Team in CSV and ODT format. Source for Table 1.
• topology.{json,pdf,png} – the topology of the complete Cyber Czech exercise network in the NetJSON, PDF and PNG format.
• topology-small.{pdf,png} – simplified topology in the PDF and PNG format. Source for Figure 1.

These data encompass data classified from the borehole textures. The U.S Geological Survey (USGS) classified the Well Log Database of the Central Valley, California, 2021, into Central_Valley_Borehole_with_Lithology_Classification.csv. Lithologic log descriptions served as the basis for division into discrete binary texture classifications of either “coarse grained” or “fine grained” intervals. Values of 1 (coarse grained), 0 (fine grained) or 2 (unknown) were determined using the table found in Central_Valley_Lithology_Classifier.csv. Percent Coarse values for each well log were calculated at 15.24 m for the entire Central valley (Percent_Coarse_meters_wells.csv).

This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.

We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),

were active for at least one year (365 days) before the first build with Travis CI (before_ci),

used Travis CI at least for one year (during_ci),

had commit or merge activity on the default branch in both of these phases, and

used the default branch to trigger builds.

To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.

We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).

We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:

have Java or Ruby as their project language

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)

have commit activity for at least two years (730 days)

are engineered software projects (at least 10 watchers)

were not in the TravisTorrent dataset

In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.

This dataset contains the following files:

tr_projects_sample_filtered_2.csv A CSV file with information about the 113 selected projects.

tr_sample_commits_default_branch_before_ci.csv tr_sample_commits_default_branch_during_ci.csv One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_"). branch: The branch to which the commit was made. hash_value: The SHA1 hash value of the commit. author_name: The author name. author_email: The author email address. author_date: The authoring timestamp. commit_name: The committer name. commit_email: The committer email address. commit_date: The commit timestamp. log_message_length: The length of the git commit messages (in characters). file_count: Files changed with this commit. lines_added: Lines added to all files changed with this commit. lines_deleted: Lines deleted in all files changed with this commit. file_extensions: Distinct file extensions of files changed with this commit.

tr_sample_merges_default_branch_before_ci.csv tr_sample_merges_default_branch_during_ci.csv One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_"). branch: The destination branch of the merge. hash_value: The SHA1 hash value of the merge commit. merged_commits: Unique hash value prefixes of the commits merged with this commit. author_name: The author name. author_email: The author email address. author_date: The authoring timestamp. commit_name: The committer name. commit_email: The committer email address. commit_date: The commit timestamp. log_message_length: The length of the git commit messages (in characters). file_count: Files changed with this commit. lines_added: Lines added to all files changed with this commit. lines_deleted: Lines deleted in all files changed with this commit. file_extensions: Distinct file extensions of files changed with this commit. pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message). source_user: GitHub login name of the user who initiated the pull request (extracted from log message). source_branch : Source branch of the pull request (extracted from log message).

comparison_project_sample_800.csv A CSV file with information about the 800 projects in the comparison sample.

commits_default_branch_before_mid.csv commits_default_branch_after_mid.csv One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.

merges_default_branch_before_mid.csv merges_default_branch_after_mid.csv One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.

This is a E-commerce website logs data created for helping the data analysts to practice exploratory data analysis and data visualization. The dataset has data on when the website was accessed, IP address of the source, Country, language in which website was accessed, amount of sales made by that IP address.

Included columns:

Time and duration of of accessing the website
Country, Language & Platform in which it was accessed
No. of bytes used & IP address of the person accessing website
Sales or return amount of that person

There are 21 event logs generated using the 21 process model in https://figshare.com/articles/dataset/_/20732095. The logs are available in mxml and csv formats

This realistic incident management event log simulates a common IT service process and includes key inefficiencies found in real-world operations. You'll uncover SLA violations, multiple reassignments, bottlenecks, and conformance issues—making it an ideal dataset for hands-on process mining, root cause analysis, and performance optimization exercises.

You can find more event logs + use case handbooks to guide your analysis here: https://processminingdata.com/

Standard Process Flow: Ticket Created -> Ticket Assigned to Level 1 Support -> WIP - Level 1 Support -> Level 1 Escalates to Level 2 Support -> WIP - Level 2 Support -> Ticket Solved by Level 2 Support -> Customer Feedback Received -> Ticket Closed

Total Number of Incident Tickets: 31,000+

Process Variants: 13

Number of Events: 242,000+

Year: 2023

File Format: CSV

File Size: 65MB

Dallas Police Active Calls

Dataset for "Comparing Transaction Logs to ILL requests to Determine the Persistence of Library Patrons In Obtaining Materials" article. Excel file contains all data in four worksheets Zip file contains four csv files, one for each worksheet: - Comparing Transaction Logs to ILL - 2016 ILL Raw ...Data.csv - Comparing Transaction Logs to ILL - 2015 ILL Raw Data.csv - Comparing Transaction Logs to ILL - 2016 Zero Search Raw Data.csv - Comparing Transaction Logs to ILL - 2015 Zero Search Raw Data.csv [more]

A CSV file prepared from application logs stemming from an SAP BI Warehouse system. This realistic dataset was generated as a means to showcase the SAP pilot use-case of the TOREADOR project. Each line corresponds to a user action. Extid, object and subobject were extracted from the BI system logs, along with the user name and event date. Role was retrieved from the standard user actions Label indicates whether the event is benign or malign - Elevation_of_privileges is an event the user should not be able to perform within the boundaries of his role. Priv_abuse is about a privileged account performing an action breaching a confidentiality clause (e.g. an administrator reading sensitive data). Forgotten_user is about a user who stayed inactive for a long time before being used again (e.g. an employee who left the company where the account was not terminated). Records where no malign activity was detected were marked 'benign'. Outbushours was computed from the time of the action, and mapped to 'inside' or 'outside' business hours.

The dataset contains 47000 RGB input images of 47 signs (10 digits, 37 letters) of Bangla Sign Language. The images have been processed via MediaPipe framework, which is designed to detect predefined 21 hand key-points from a sample and provide normalized x & y coordinate values and an estimated depth value. The 3D coordinate values were stored in .csv files (1 file contains information of 100 image sample of the same sign). The dataset contains 470 .csv files in total, and 47000 corresponding output images with hand key-points being detected.

An extensive archive containing more than 15,000 historical (1918–2020) geophysical logs collected in conjunction with studies done by various entities and more than 2,000 additional donated well and geophysical logs are stored in hard-copy at the U.S. Geological Survey Oklahoma-Texas Water Science Center (OTWSC) in Austin, Texas. The dataset described herein addresses the need to preserve these records electronically by providing a scanned and indexed collection of 15,475 of these logs. Detailed well header information for each record is provided as both a comma-separated value (CSV) text file and a Microsoft Access database in ACCDB format. Also included are zipped files containing the geophysical-log scans in Portable Document Format (PDF). The original dataset was published in January 2024 with 6,058 log scans and an initial revision was made in September 2024, incorporating 5,113 additional log scans with header information into the original dataset. A final revision was made ...

This is a yearly log for freedom of information requests made under MFIPPA and it matches content required for annual report to IPC. The source of the data is the Office of the City Clerk - Archives division. The dataset includes the following:# - request number assigned as requests are receivedDated Opened – date request receivedDate Completed – date request is closedDue Date – 30 calendar days from date opened as specified by MFIPPADivision – CTB Division(s) with responsive recordsOutcome – final access decision, or withdrawal/abandonment by requesterType of Information – is request made for general or personal information?Requester Type – is the request made by an individual, an individual’s representative and organization, a business, media, academic, etc.Correction – is the request made for a correction of personal informationTime to Complete – how many calendar days was it from the date of receipt to date of closing requestNotice Issued – was a notice issued for: extension of the 30 day period, affected 3rd party, or high fee estimateExemption Used – what section of MFIPPA, if any, was used to exempt records from accessApplication Fee Collected – was the mandatory $5.00 application fee collectedFees collected – amount of fees collected in addition to application feeReasons for Fees – search time, copying, redaction time, invoiced costs, shipping/mailing, computer costs, fees waived Data was collected on Jan 1 2019.

Public dataset from the Dojo experiment by Eyal Krief. Includes weekly SheepRank vs Domain Authority results, CSV logs, and reproducible graphs.

This dataset includes TIFF (Tagged Image File Format) images of graphic drill core logs showing associated drill core information, a TIFF image of the explanation for the lithology and structure sections of the logs, an Esri shapefile of the locations of the drill holes, and 17 individual .csv files of tabular data that were compiled from handwritten drill core logs. The drill core is from the Mountain View area of the Stillwater Complex, Montana and was drilled from 1988 to 1990 by the Chrome Corporation of America. The data shown in the graphic drill logs and contained within the .csv files includes lithologic, structure, grain size, sulfide, chromite, chromium, platinum, palladium, copper, and nickel mineralization information. The graphic drill logs were created using Golden software's Strater 5 drill core visualization software and are provided with both logarithmic and linear scales. The graphic drill logs are plotted using the depth recorded in the drill logs and do not reflect true thickness. Two of the drill holes, MV89-550 and MV89-551, were logged by two different geologists (Dr. Roger Cooper and Rob Kell). The data shown in the graphic logs for those two holes were taken from Roger Cooper's logs, with the exception of the interval from 0 to 133 feet for drill hole MV89-550, which was taken from Rob Kell's log, as Roger Cooper did not log that interval for that particular drill hole. The .csv file names for Roger Cooper's logs contain "RC" in the file name and Rob Kell's contain "RK". Some intervals of the drill core that correlate to chromitite zones were logged at two different scales: 1"=1' and 1"=10'. All instances of question marks ("?") represent original data as written by the geologist. In areas where the hand-written notes were unreadable, the notation of "[unreadable]" was used. See USGS SIR 2014-5183 (https://pubs.usgs.gov/sir/2014/5183/) for report and spatial data relating to the Stillwater Complex.

Log PM is a log parser benchmark emphasizing precise in-message parameter detection rather than template-based message clustering. This dataset is a combination of smaller datasets used for this benchmark. Datasets are collected from LogHub, parsed using handcrafted regexes, and stored in CSV files. Each CSV file contains no header and three columns. The first one is the message, the second is the parameter mask, and the third one is the index of the matching regex. The necessary dataset parts are downloaded automatically in the LogPM benchmark, so no direct download is required for benchmarking.

The benchmark includes the following datasets:

• Android
• Apache
• Hadoop
• HDFS
• HPC
• Linux
• OpenStack
• Proxifier
• SSH
• ZooKeeper