Mostafa Milani

PhD in Copmuter Science
Department of Computer Science
Carleton University


Office: ICICS/CS Building, 2366 Main Mall, Vancouver, BC V6T 1Z4 Canada, Room 353

I recently joined the Department of Computer Science at Western University as an assistant professor.

Postdoctoral Researcher

I am a postdoctoral research fellow at the Department of Computer Science, the University of British Columbia. Prior to this, I was a postdoctoral researcher at the Data Science Lab, McMaster University.

My research interests are data management, data quality, data cleaning and applications of AI in data management. My past research was about rule-based query languages, ontology-based data access and multidimensional databases.

Recent Research Projects



Pastwatch is a data summarization, explanation and visualization framework for the provenance of aggregate queries. Data provenance includes any information about the origin of a piece of data and the process that led to its creation. The provenance of a query over a database is the data in the database that contributed to the query answer. For aggregate queries that apply mathematical functions, such as sum and average, the provenance of a query answer usually contains a large number of database records which makes it difficult for a database user to explore and understand it. Pastwatch facilitates database access by providing provenance summarization of queries, which helps users to understand the query answers. [link]



CurrentClean is a probabilistic system for the detection and cleaning of stale data. It learns spatio-temporal update patterns for values in a database via past update queries. CurrentClean applies inference rules to model the causal and co-occurrence update patterns seen in real data and estimates currency of values and recommends spatio-temporal-aware repairs for stale values. We applied several optimization techniques that improve the inference run-time in the system and we conducted extensive experiments and studied CurrentClean's comparative accuracy to detect stale values in real data, as well as its repair effectiveness. [link]

Privacy-Aware data Cleaning-As-a-Service (PACAS)


PACAS is a framework for facilitating data cleaning between a client and a service provider. The goal of this framework is to improve data accuracy with respect to a master database owned by the service provider. The interaction between the client and the service provider is done via a data-pricing scheme where the service provider charges the client for each disclosed value, according to its adherence to the privacy model. In PACAS, we introduced a new privacy model based on data publishing that considers the data semantics while providing stronger privacy protection. We also presented a data-cleaning algorithm that resolves errors by updating them to their true values in the service provider data. [link]

Recent Publications

1- Facilitating SQL Query Composition and Analysis [link]

Z. Zolaktaf, M Milani and R. Pottinger

To Appear in ACM International Conference on Management of Data, 2020 (SIGMOD '20)

2- Pastwatch: On the Usability of Provenance Data in Relational Databases [short paper] [link]

O. AlOmeir, E. Y. Lai, M. Milani and R. Pottinger

To Appear in IEEE International Conference on Data Engineering, 2020 (ICDE '20)

3- CurrentClean: Interactive Change Exploration and Cleaning of Stale Data [demo paper] [link]

Z. Zheng, T. Quach, Z. Jin, M. Milani, F. Chiang

ACM International Conference on Information and Knowledge Management, 2019 (CIKM'19)

4- Improvement of SQL Recommendation on Scientific Database [short paper] [link]

J. Liu, Z. Zolaktaf, R. Pottinger, M. Milani

International Conference on Scientific and Statistical Database Management, 2019 (SSDBM '19)

5- CurrentClean: Cleaning Stale Data with Spatio-Temporal Repairs [link]

M. Milani, Z. Zheng and F. Chiang

IEEE International Conference on Data Engineering, 2019 (ICDE '19)

6- PACAS: Privacy-Aware, Data Cleaning-as-a-Service [short paper] [link]

Y. Huang, M. Milani and F. Chiang

IEEE International Conference on Big Data, 2018 (BigData '18)

7- Ontological Multidimensional Data Models and Contextual Data Quality [journal paper] [link]

L. Bertossi and M. Milani

Journal of Data and Information Quality, 2018 (JDIQ '18)


1- Carleton University, PhD in Computer Science [Jan 2017]
2- Amirkabir University, MSc in Artificial Intelligence [Jun 2010]
3- University of Tehran, BS in Software Engineering [Jun 2007]


Web Systems and Web Computing [COMPSCI 4WW3]

McMaster University, Department of Computing and Software, Fall 2018