Search results

(1 - 2 of 2)
PROVENANCE FOR TRANSACTIONAL UPDATES
Title
PROVENANCE FOR TRANSACTIONAL UPDATES
Creator
Arab, Bahareh Sadat
Date
2019
Description
Database provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions...
Show moreDatabase provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions require understanding of how the current state of a database was derived by a transactional history. We introduce an approach for capturing the provenance of transactions. Our approach does not just work for serializable concurrency control protocols but also for non-serializable protocols including snapshot isolation. The main drivers of our approach are a provenance model for queries, updates, and transactions and reenactment, a novel technique for retroactively capturing the provenance of tuple versions. We introduce the MV-semirings provenance model for updates and transactions as an extension of the existing semiring provenance model for queries. Our reenactment technique exploits the time travel and audit logging capabilities of modern DBMS to replay parts of a transactional history using queries. Importantly, our technique requires no changes to the transactional workload or underlying DBMS and results in only moderate runtime overhead for transactions. We discuss how our MV-semirings model and reenactment approach can be used to serve a wide variety of applications and use cases including answering of historical what-if queries which determine the effect of hypothetical changes to past operations of a business, post-mortem debugging of transactions, and to create private data workspaces for exploration. We have implemented our approach on top of a commercial DBMS and our experiments confirm that by applying novel optimizations we can efficiently capture provenance for complex transactions over large data sets.
Show less
Enhancing Explanation Generation in the CaJaDE system using Interactive User Feedback
Title
Enhancing Explanation Generation in the CaJaDE system using Interactive User Feedback
Creator
Lee, Juseung
Date
2022
Description
In today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several...
Show moreIn today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several manipulation steps, especially on a large database. There is a need for automated techniques that explain query results in a meaningful way. A recent study, CaJaDE(Context-Aware Join-Augmented Deep Explanations), presents a novel approach to generating explanations of query results including crucial contextual information. However, it becomes difficult to interpret explanations since the search space increases exponentially.In this thesis, we propose a new approach that introduces a user interaction model for a purpose of enhancing the generation of explanations in the CaJaDE system. We implemented a user interaction model that consists of three modules: User Selection, Recommendation Score, and User Rating. With these modules, our approach guides a user while exploring relevant join graphs, and lets them be involved in the decision-making process while generating join graphs. We demonstrate through performance experiments and user study that our approach is an effective method for users to understand explanations.
Show less