Introduction:

Enterprises are routinely divided into independent business units to support agile operations. However, this leads to "siloed" information systems. Such silos generate a host of problems, such as:

DISCOVERY of relevant data to a problem at hand. For example: Merck has 4000 (+/-) Oracle databases, a data lake, large numbers of files and an interest in public data from the web. Finding relevant data in this sea of information is a challenge.

INTEGRATING the discovered data. Independently constructed schemas are never compatible.

CLEANING the resulting data. A good figure of merit is that 10% of all data is missing or wrong.

ENSURING EFFICIENT ACCESS to resulting data. At scale operations must be performed "in situ", and a good polystore system is a requirement

It is often said that data scientists spent 80% (or more) of their time on these tasks, and it is crucial to have better solutions. 

In addition, the EU has recently enacted GDPR that will force enterprises to assuredly delete personal data on request. This "right to be forgotten" is one of several requirements of GDPR, and it is likely that GDPR-like requirements will spread to other locations, for example, California. In addition, privacy and security issues are increasingly an issue for large internet platforms. In enterprises, these issues will be front and center in the distributed information systems in place today.

Lastly, enterprise access to data in practice will require queries constructed from a variety of programming models. A “one size fits all” model just won’t work in these cases.

At VLDB’18, VLDB’19, VLDB'20, VLDB'21, and VLDB'22 we organized the Poly workshop. These successful workshops brought together experts from around the world working on novel advances in the field. Poly’23 will continue to focus on the broader real-world polystore problem, which includes data management, data integration, data curation, privacy, and security. 

Research topics:

• Data discovery from heterogenous data sources (e.g., data lakes)

• Privacy, Security, and Policy in heterogenous data management. 

•  Languages/Models for integrating disparate data such as graphs, arrays, relations

• Query evaluation and optimization in polystore and other multi-DBMS systems

• Efficient data movement and scheduling, failures and recovery for polystore analytics

• High Performance/Parallel Computing Platforms for Big Data

• Data Discovery, Integration, Cleaning, and Best Practices

• Privacy and Access control in Polystore and multi-DBMS systems

• Enterprise support for GDPR and similar privacy regulations

• Policy implications of GDPR and similar privacy regulations

• Mathematics for Polystore and other multi-DBMS systems

• Demonstrations of new tools and techniques for heterogeneous data

Important Dates

June 28th, 2023: Due date for full workshop papers submission 

June 30th, 2023: Notification of paper acceptance to authors 

August 10th, 2023: Camera-ready

September 1st, 2023: Workshop

Submission page

https://cmt3.research.microsoft.com/PolyVLDB2023