Ying Yang 

Ying Yang

Research Assistant/Ph.D. Candidate

Department of Computer Science and Engineering

The State University of New York at Buffalo

Phone: (716)-239-6890
Email: yyang25@buffalo.edu


Research Summary (Under Construction)

Representative work: [VLDB14, VLDB BIRTE14, VLDB15]

Design and implementation of a general, extensible infrastructure for on-demand data curation tasks based on probabilistic query processing. My contribution involves:

Convergent Inference Algorithm
Representative work: [EDBT17]
[Presentation Slides]

Proposed a new family of inference algorithms called Convergent Inference algorithms (CIAs), which enjoy the benefits of both exact and approximate inference algorithms by providing approximate results over the course of inference, and eventually converging to an exact inference result. My contribution involves:

Adaptive Schema Databases
Representative work: [CIDR17]
[Presentation Slides]

we propose a new paradigm where the database system takes a more active role in schema development and data integration. We refer to this approach as adaptive schema databases (ASDs). An ASD ingests semi-structured or unstructured data directly using a pluggable combination of extraction and data integration techniques. Over time it discovers and adapts schemas for the ingested data using information provided by data integration and information extraction techniques, as well as from queries and user-feedback. In contrast to relational databases, ASDs maintain multiple schema workspaces that represent individualized views over the data, which are fine-tuned to the needs of a particular user or group of users. A novel aspect of ASDs is that probabilistic database techniques are used to encode ambiguity in automatically generated data extraction workflows and in generated schemas. ASDs can provide users with context-dependent feedback on the quality of a schema, both in terms of its ability to satisfy a user's queries, and the quality of the resulting answers