Thank you for your interest in the QWS Dataset, the first web services' dataset that measured real web services' Quality of Service (QoS) introduced in 2007 and is part of Eyhab Al-Masri's PhD thesis work. The QWS Dataset has been widely accepted across the research community and downloaded over 9,000 times since its first introduction in 2007. The main goal of this dataset is to offer a basis for web service researchers. The web services were collected using the Web Service Crawler Engine (WSCE) and the majority of these services were obtained from public sources on the web including Universal Description, Discovery, and Integration (UDDI) registries, search engines and service portals.
The QWS Dataset ver 2.0 includes a set of 2,507 web services and their Quality of Web Service (QWS) measurements that were conducted during the year of 2008 using our Web Service Broker (WSB) framework. Each row in this dataset represents a web service and its corresponding nine QWS measurements (separated by commas). The first nine elements are QWS metrics that were measured using multiple Web service benchmark tools over a six-day period. The QWS values represent averages of the measurements collected during that period. The last two parameters represent the service name and reference to the WSDL document.
The QWS Dataset ver 1.0 contains measurements of nine Quality of Service (QoS) per web service for 365 web services and includes two additional attributes: (a) a rank of web services based on our Web Service Relevancy Function (WsRF) and (b) a class which classifies web services based on their overall performance.
Each row in the dataset corresponds to an existing Web service implementation. The dataset contains a collection of web services from our service repository that were continuously monitored for particular service qualities including:
ID |
Parameter Name |
Description |
Units |
1 |
Response Time |
Time taken to send a request and receive a response |
ms |
2 |
Availability |
Number of successful invocations/total invocations |
% |
3 |
Throughput |
Total Number of invocations for a given period of time |
invokes/second |
4 |
Successability |
Number of response / number of request messages |
% |
5 |
Reliability |
Ratio of the number of error messages to total messages |
% |
6 |
Compliance |
The extent to which a WSDL document follows WSDL specification |
% |
7 |
Best Practices |
The extent to which a Web service follows WS-I Basic Profile |
% |
8 |
Latency |
Time taken for the server to process a given request |
ms |
9 |
Documentation |
Measure of documentation (i.e. description tags) in WSDL |
% |
10 |
WsRF |
Web Service Relevancy Function: a rank for Web Service Quality |
% |
11 |
Service Classification |
Levels representing service offering qualities (1 through 4) |
Classifier |
12 |
Service Name |
Name of the Web service |
- |
13 |
WSDL Address |
Location of the Web Service Definition Language (WSDL) file on the Web |
- |
This section defines key concepts used in the QWS dataset.
Quality of Web Service (QWS) Web service’s ability to provide selective treatment to various clients in the most effective manner.
Web Service Relevancy Function (WsRF) WsRF is used to measure the quality ranking of a Web service based on quality metrics (1 through 9 above).
Documentation One of the main properties of Web services is having proper documentation. The documentation QWS property provides a measure to the extent of which a Web service is self-describable and is based on examining WSDL documents including service name, description, operation name, description, message name, and message description tags.
Service Classification The service classification represents various levels of service offering qualities. There are four service classifications:
The classification is based on the overall quality rating provided by our WsRF. Using WsRF values obtained for each Web services, we use a classification scheme to associate each Web services to a particular service group. The classification can be helpful to differentiate between various services that offer the same functionality.
The QWS dataset is available free of charge for educational and non-commercial purposes. In exchange, we kindly request that you make available to us the results of running the QWS dataset. Please use the following references in citing the dataset:
Al-Masri, E., and Mahmoud Q. H., "Investigating web services on the world wide web", ACM 17th international conference on World Wide Web (WWW '08), pp.795-804.
Al-Masri, E., and Mahmoud, Q. H., "QoS-based Discovery and Ranking of Web Services", IEEE 16th International Conference on Computer Communications and Networks (ICCCN), 2007, pp. 529-534.
Al-Masri, E., and Mahmoud, Q. H., "Discovering the best web service", ACM 16th International Conference on World Wide Web (WWW), 2007, pp. 1257-1258.
Downloading and using the QWS Data will indicate your acceptance to enter into a GNU General Public License agreement. Should the QWS Data be used in any scientific or educational study/research the authors will be accredited as the source of the data with any of the references listed above in citing the data. Redistribution of this data to any other third party or on the Web is not permitted.
The QWS dataset has been applied to a wide range of research projects/theses in areas covered by services computing including (but not limited to) the following:
Web Service Classification
Web Service Composition
Web Service QoS Performance
Web Service QoS Prediction
Web Service Ranking
Web Service Discovery
Web Service Modeling
Web Services' Resource Management
Web Service Coordination
Service Oriented Analysis
Web Service Transaction
Business process Integration and Management
We thank the research community for their valuable support throughout the usage of this dataset. Since its first release in 2007, the QWS Dataset has been widely used in research and industry projects. The QWS Dataset has been downloaded over 9,000 times so far. For a glimpse of some of these projects that were able to collect from the web over the last few years, please click here.
Your comments and suggestions are welcome.
Please send your comments by email: Eyhab Al-Masri (qwsdata[AT]yahoo.com)
Copyright (c) 2007-2020 QWS Dataset, University of Guelph & University of Washington Tacoma.