Dataset

This dataset was created by boomerang476

Contents

This dataset contains event logs on the Container Dwell Time process at the Container Terminal. The Dwelling Time process is the process and time calculated from the time the container is unloaded and lifted from the ship until the container leaves the port terminal through the main door.

Source: - https://www.sciencedirect.com/science/article/pii/S2352340924008692 - https://data.mendeley.com/datasets/yvp2b4rtp3/1

This dataset contains event logs on the Container Dwell Time process at the Container Terminal. The Dwelling Time process is the process and time calculated from the time the container is unloaded and lifted from the ship until the container leaves the port terminal through the main door.

Nigeria Telecom Network Event Logs (TsFile)

This dataset is an Apache TsFile conversion of electricsheepafrica/africa-synth-telecom-network-event-logs-nigeria, a synthetic Nigerian telecom dataset containing detailed network event logs such as packet loss, handover failures, congestion, and connection attempts.

  Source Dataset

Original dataset: electricsheepafrica/africa-synth-telecom-network-event-logs-nigeria Author: electricsheepafrica Category: Network… See the full description on the dataset page: https://huggingface.co/datasets/THUgewu/africa-synth-telecom-network-event-logs-nigeria.

This dataset contains results of the experiment to analyze information preservation and recovery by different event log abstractions in process mining described in: Sander J.J. Leemans, Dirk Fahland "Information-Preserving Abstractions of Event Data in Process Mining" Knowledge and Information Systems, ISSN: 0219-1377 (Print) 0219-3116 (Online), accepted May 2019 The experiment results were obtained with: https://doi.org/10.5281/zenodo.3243981

Synthetic FOI Process Event Logs

  Registry status

Registry ID: edithatogo/foi-process-event-logs Family: foi-research Repository role: synthetic_benchmark_dataset Canonical dataset: edithatogo/foi-process-event-logs Operational status: active Rights status: synthetic_reviewed_fixtures Authoritative catalog: edithatogo/dataset-estate-registry

  Origin and provenance

Origin repository: https://github.com/edithatogo/foi-process Upstream source:… See the full description on the dataset page: https://huggingface.co/datasets/edithatogo/foi-process-event-logs.

General Description

This process describes the management of customer orders within a company, comprising both the registration and payment of incoming orders, as well as the process of packing and shipping these orders. For these tasks, our company deploys staff in their sales, warehousing, and shipment departments.

This is an artificial event log according to the OCEL 2.0 Standard simulated using CPN-Tools. Both the CPN and the SQLite can be downloaded. The simulation is an extension of the order management log in the former OCEL standard.

Process Overview

At our company, customers place orders (place order) for different products in varying amounts. Each product type has a price and a weight. In the current market situation, there is an inflation that irregularly leads to an increase of prices. These price rises have a negative impact on customers’ purchasing power, i.e., on order volumes.

When a customer places an order, this order is assigned to an employee of our company’s sales department. To foster customer satisfaction, our company has a single-face-to-customer policy. This means that per customer there is one primary sales representative who ought to render all services related to that customer. If that first representative is unavailable, a second sales representative should take care of the order. Should this employee be also unavailable, the order has to be managed by another employee. The tasks of sales employees comprise the registration (confirm order) as well as payment processing (payment reminder, pay order).

In parallel to this, the shipment of goods is prepared. For this, the stock of our company is checked by an employee of the warehousing department for the availability of the ordered items. If necessary, the warehouser reorders the item (item out of stock, reorder item). Items ready for shipment are collected (pick item) for the placement into packages that are addressed to single customers. Here, it may happen that a package content relates to multiple orders, and order volumes are distributed over multiple packages.

After all items allocated to a package have been picked, the package is compiled by a warehousing employee (create package). Later on, this package is picked up by a shipment employee for transport (send package). According to another policy, a warehousing employee should provide assistance to the shipment employee in loading the package. However, oftentimes shippers act contrary to that policy and load packages alone or together with a second shipment employee.

Finally, the package is shipped. Deliveries may fail repeatedly (failed delivery) until successful delivery (package delivered).

The figure below depicts the process in a simplified manner, using an informal process notation to describe the control-flow and the involved object types. A formal description is given along with the artifacts in the next section.

Further information can be found at: https://www.ocel-standard.org/event-logs/simulations/order-management/

General Properties

An overview of log properties is given below.

Property	Value
Event Types	11
Object Types	6
Events	21008
Objects	10840

Control-Flow Behavior

The behavior of the log is described by a respective object-centric Petri net. Also, individual object types exhibit behavior that can be described by simpler Petri nets. See below.

orders	customers
items	employees
packages	products
Full object-centric Petri net

Object Relationships

The company pursues the "one-face-to-the-customer" policy, in which every customer has a dedicated sales representative as well as a deputy (secondary representative). These relationships are described in the log.

Source Object Type	Target Object Type	Qualifier
employees	customers	primarySalesRep
employees	customers	secondarySalesRep

Additionally, object-to-object relations can emerge at executions of specific activities:

Activity	Source Object Type	Target Object Type	Qualifier
create package	package	employee	packed by
send package	package	employee	forwarded by
send package	package	employee	shipped by

Simulation Model

The CPN used to create this event log can also be downloaded.To obtain simulated data, extract the linked ZIP file and play out the CPN therein, e.g., by using CPN Tools.

The play-out produces CSV files according to the schema of OCEL2.0. The provided jupyter notebook can be used to convert these files to an SQLite dump.

For a technical documentation of the simulation model, please open the attached CPN with CPN Tools and see the annotations therein.

Acknowledgements

Funded under the Excellence Strategy of the Federal Government and the Länder. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.

Real life business processes change over time

This is the event log created by the recorder. Further details in the publication.

DEPRECATED - current version: https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794 Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated, and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we created the DataStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.

Tenderwatch dataset of public-procurement contracts won by Event Log Limited, captured from TED.

scientific sampling event logs from research cruises

These scientific sampling event logs from the 2011 LatMix project cruises include a record of all sampling events from the cruises. Some of the instrumentation is described in the Oceanus ship operations report.

Sampling gear included: Acrobat; ADCP150 (Acoustic Doppler Current Profiler 150); calFluorometer; CTD911 (SeaBird 911plus CTD); dyeinjectionSled; Echosounder12 (12 KHz Knutsen); EM-APEX; gatewayBuoy; Glider; Hammerhead (towed profiler); lagrangianFloat; Navigation; osuMVP; Other (miscellaneous events); profileAOP; profileIOP; Ship (ship events); svpdDrifter; tREMUS (AUV); Triaxus (towed profiler); umassDrogue and uvicMVP.

Dataset

This dataset was created by Mehul Katara

Contents

This upload contains the event logs, generated by L-Sim, on which the experiments of the related paper were performed. The related paper is accepted in the journal Information Systems.

This datasets includes 9 event logs, which can be used to experiment with log completeness-oriented event log sampling methods.· exercise.xes: The dataset is a simulation log generated by the paper review process model, and each trace clearly describes the process of reviewing papers in detail.· training_log_1/3/8.xes: These 3 datasets are human-trained simulation logs for the 2016 Process Discovery Competition (PDC 2016). Each trace consists of two values, the name of the process model activity referenced by the event and the identifier of the case to which the event belongs.· Production.xes: This dataset includes process data from production processes, and each track includes data for cases, activities, resources, timestamps, and more data fields.· BPIC_2012_A/O/W.xes: These 3 dataset are derived from the personal loan application process of a financial institution in the Netherlands. The process represented in the event log is the application process of a personal loan or overdraft in a global financing organization. Each trace describes the process of applying for a personal loan for different customers.·ETM: This data set contains loan application examples and provides a brief description of the loan application process.·TSL.anon: This data set contains loan application examples and provides a brief description of the loan application process.: This data set comprises examples of a telecom company's second-line Customer Relationship Management (CRM) process.·BPIC2015_1: This data set contains loan application examples and provides a brief description of the loan application process.: This data set contains all building permit applications of the Dutch municipal government over approximately 4 years. The logs include information about the main application as well as objections in different stages, and also provide information about the resources and application costs of executing tasks.

I utilized a publicly accessible dataset (creators: Claudio Amaral, Marcelo Fantinato and Sarajane Peres), with slight modifications, for my academic work. This is an event log of an incident management process extracted from data gathered from the audit system of an instance of the ServiceNowTM platform used by an IT company. The event log is enriched with data loaded from a relational database underlying a corresponding process-aware information system. Information was anonymized for privacy.

Africa Synth Telecom Network Event Logs Nigeria | Africa (Electric Sheep Africa metadata inventory)

Size category: 100K<n<1M - Formats: parquet - Sector: technology_digital - Engineered by Electric Sheep Africa

  TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

  What This Dataset Covers… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-network-event-logs-nigeria.

The science party maintained a digital event log, recording all instrument deployments and significant events during the BaRFlux cruises.

NOTE: These data are preliminary; position corrections have not been made when needed (refer to the 'comment' column).

This dataset contains a simplified excerpt from a real event log that tracks the trajectories of patients admitted to a hospital to be treated for sepsis, a life-threatening condition. The log has been recorded by the Enterprise Resource Planning of the hospital. Additionally, the dataset contains three synthetic logs that increase the number of trajectories within the original log timespan, while maintaining other statistical characteristics. In total, the dataset contains four files in .zip format and a companion that describes the statistical method used to synthesize the logs as well as the dataset content in detail. The dataset can be used in testing the performance of event-based process-mining and log (runtime) monitoring tools against an increasing load of events.