ACM02DataQAssesment -Pipino

"Data Quality Assessment", L.Pipino, Y.Lee, R.Wang

Completeness can be defined as:

• schema completeness : entities and attributes are not missing from the schema
• column completeness : a function of missing values in a column of a table
• population completeness : given the set of values a column should have, count how many are missing
  

All can be measured as a ratio =1- #incomplet items/total #of items

WWW03qDriven-Web-Zeng

"Quality driven web services composition" , L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, Q. Sheng, WWW Conference, 2003

• Quality criteria used: price, duration, reputation, reliability, availability
• Composite service =an aggregation of all the component services
• Composite service is modeled as a statechart with initial state, final state and paths to travel from the init-state to fin-state
• price =the amount of money that a service requester has to pay for executing the operation (come from the web service providers)
• duration =expected delay between the moment when a request is sent and the moment when the result are received; is the sum of the processing time and the transmission time
  
• reliability =the probability that a request is correctly responded within a maximum expected time frame (computed from historical data)
• availability =the probability that the service is accessible ( assumption: web services send notifications to the system about their running states )
• reputation: the average ranking given to the service by the end users
• MAIN IDEA: algorithms to select the optimal execution plans in terms of quality based on linear programing
• an experiment was conducted using synthetic data.
  

IQ2000infoQ-Naumann

"Assesment Methods for Information Quality Criteria"
Felix Naumann, Claudia Rolker -In Proceedings of the International Conference on Information Quality, 2000

• Previous work: IQ scores rely on questionnaires; IQ based on soudness and completeness of information sources. Algorithm provided to compute the IQ score but is still based on user inout to decide weather some information is correct or not.
• 3 IQ classes: the user, the source and the query process
• 3 assessment-oriented IQ criteria classes: subject-criteria scores, process-criteria scores, Object-criteria scores
  

COMMENTS:

• no experiments are provided
• User input can be seen as how many time someone accessed a particular source and how long did they stayed on that source (if this information can be extracted) or how many times user returned to that source
  

VLDB99QDrivenIntegration -Naumann

"Quality-driven Integration of Heterogenous Information Systems", Felix Naumann, Ulf Leser, Johann Christoph Freytag

• Incorporate the information quality aspect into query planning
• Quality criteria: source-specific, query-specific and attribute-specific
• IQ (Information Quality): is the aggregation of the scores used to rank the sources and plans
• Three phase approach to quality-driven information integration
  

Journal of MIS1996BeyondAccuracy-Wang-Strong

"Beyond Accuracy: What Data Quality Means to Data Consumers" - Richard Y. Wang, Diane M. Strong ( MIT )

• Approaches to study data quality: intuitive, theoretical, empirical
• Attributes of the quality of data: accuracy, timeliness, precision, reliability, currency, completeness, relevancy, accessibility, interpretability
• Overall the most important attributes: accuracy and correctness