Data Civilizer: A Tool to Find, Ingest, Clean, and Integrate Diverse Data Sets
Modern organizations are faced with a massive number of heterogeneous data sets. It's not uncommon for a large enterprise to report having 10,000 or more structured databases, not to mention millions of spreadsheets, text documents, and emails. Typically these databases are not organized according to a common schema or representation. As a result, data scientists in large organizations spend 90% or more of the time just trying to find the data they need and transform it into a common representation that allows them to perform the desired analysis.
Data Civilizer includes a number of key components designed to simplify this process, including:
- Data discovery. Given some input request, this component crawls an organization’s data and returns those objects relevant to the request, employing a new graph-based approach for discovery and efficient data set indexing techniques.
- Data stitching. Putting relevant data together for user consumption (i.e., data stitching). This requires investigating several issues on how graph-based and query-driving data stitching can be accomplished.
- Data cleaning. We are investigating new data cleaning approaches along several directions: composition including an interactive dashboard and record expansion for outlier detection.
- Data transformations. Data often needs to be transformed in order to use a uniform representation. We have developed a new program-synthesis-based transformation engine.
- Entity consolidation. Our efforts in this area have focused on scaling entity resolution to very large data sets and using program synthesis to discover entity resolution rules.
- Human-in-the-loop processing. We are working on new techniques to use human effort more effectively throughout the data integration and cleaning process, prioritizing attention on that part of the pipeline where human time can be most effective.
We are currently exploring these directions, as well as a number of new problems particularly relevant to DSAIL. These include integrating structured and unstructured data (i.e., text data) using NLP techniques like word embeddings and exploring the use of Civilizer as a part of machine learning pipelines, with a particular focus on using it to assist in the cleaning and transformation of intermediate data product in machine learning workflows.
Data Civilizer is a joint project with the Qatar Computing Research Institute (QCRI).
The architecture of Data Civilizer
El Kindi Rezig, Lei Cao, Michael Stonebraker, Giovanni Simonini, Wenbo Tao, Samuel Madden, Mourad Ouzzani, Nan Tang, Ahmed Elmagarmid. 2019. Data Civilizer 2.0: A Holistic Framework for Data Preparation and Analytics. VLDB 2019. (Demo paper)
Dong Deng, Raul Castro Fernandez, Ziawasch Abedjan, Sibo Wang, Michael Stonebraker, Ahmed Elmagarmid, Ihab Ilyas, Samuel Madden, Mourad Ouzzani, Nan Tang. 2017. “The Data Civilizer System.” CIDR 2017.