List of Top Schools of Proceedings of The International Conference on Data Science and Official Statistics sorted by citations.

List of Top Institutions of Proceedings of The International Conference on Data Science and Official Statistics sorted by citations.

Occupational safety training course and conference monthly statistical data.

Data extracted from the user, event and group profile for delegates in the 9th R users conference in Spain

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Archived website of the XVIth International Conference of the Association for History and Computing, Amsterdam, the Netherlands, 14-17th September 2005.

The XVIth Conference of the international AHC aims to bring together specialists from three broad streams:
- Scholars, using computers in historical and related studies (history of art, archaeology, literary studies, etc.)
- Information and computing scientists, working in the domain of cultural heritage and the humanities
- Professionals, working in cultural heritage institutes (archives, libraries, museums) who use ICT to preserve and give access to their collectionsThe subject matter of the conference is primarily oriented at methodological issues, and not restricted to one particular domain within historical sciences and the humanities. Preferably, sessions will consist of a mix of these three interest groups and fields. There will be numerous cross links between the streams.

Topics for sessions and papers include:- Data access, retrieval and presentation: Data bases in historical/humanities research;
- Data mining, data harvesting and data syndication;
- Digital data archives & longevity of digital heritage;
- Personalization and presentation of heritage information;
- Virtual libraries and virtual collaboratories in the humanities;
- Enriching data: Digital source editions; Knowledge enrichment and encoding methods;
- Metadata standards and semantic interoperability for access to cultural heritage;
- Images & multimedia: Image analysis and visual culture;
- Content based and other image retrieval methods;
- Digital photo/image/video collections;
- Digital museums;
- Geographical Information Systems: GIS Applications in the humanities and historical studies;
- GIS methods and techniques; GIS for access to heritage information;- Qualitative & Quantitative data analysis: Advanced statistics in historical research;
- Models and simulations;
- Exploratory analysis and visualization techniques- Digitization of heritage information: Large digitization projects of historical sources;
- Optical character and document recognition for historical materials;
- Handwriting recognition and script analysis tools
- Text analysis and retrieval: Applications of text analysis in the humanities;
- Methodological issues of text mining and text analysis;
- Digital text archives
- Theoretical, methodological and educational issues: e-Science, e-Humanities and e-History;
- Historiography of humanities computing;
- Educational issues

Low Countries Organization Committee:
- Onno Boonstra (Humanities computing, University of Nijmegen)
- Leen Breure (Computer and Information Science, University of Utrecht)
- Peter Doorn (NIWI - Netherlands Institute for Scientific Information Services, Amsterdam)- Jaap van den Herik (Computer Science, Universities of Leiden and Limburg)- Bart de Nil (Amsab - Institute for Social History, Gent, Belgium)
- Paula Witkamp (European Commission on Preservation and Access, Amsterdam)

Organizing institutions:
- Netherlands Institute for Scientific Information Services (NIWI)
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Vereniging voor Geschiedenis en Informatica (VGI)
- The Association for History and Computing (AHC)
- Dutch Research School for Information and Knowledge Systems (SIKS) The content of the website has been saved in three PDF packages with information over the conference and the collections of abstracts and posters.