Graphs provide powerful abstractions, and are widely used in different areas. There has been an increasing demand in using the graph data model to represent data in many applications such as network management, web page analysis, knowledge graphs, social networks. These graphs are usually dynamic and represent the time evolving relationships between entities. Enforcing and maintaining data quality in graphs is a critical task for decision making, operational efficiency and accurate data analysis as recent studies have shown that data scientists spend 60-80% of their time cleaning and organizing data [2]. This effort motivates the need for effective data cleaning tools to reduce the user burden. The study of data quality management focuses along a set of dimensions, including data consistency, data deduplication, information completeness, data currency, and data accuracy. Achieving all these data characteristics is often not possible in practice due to personnel costs, and for performance reasons. In this thesis, we focus on tackling three problems in two data quality dimensions: data consistency and data deduplication.
To address the problem of data consistency over temporal graphs, we present a new class of data dependencies called Temporal Graph Functional Dependency (TGFDs). TGFDs generalize functional dependencies to temporal graphs as a sequence of graph snapshots that are induced by time intervals, and enforce both topological constraints and attribute value dependencies that must be satisfied by these snapshots. We establish the complexity results for the satisfiability and implication problems of TGFDs. We propose a sound and complete axiomatization system for TGFDs. We also present efficient parallel algorithms to detect inconsistencies in temporal graphs as violations of TGFDs. To address the data deduplication problem, we first address the problem of key discovery for graphs. Keys for graphs use topology and value constraints to uniquely identify entities in a graph database and keys are the main tools for data deduplication in graphs. We present two properties that define a key, including minimality and support and an algorithm to mine keys over graphs via frequent subgraph expansion. However, existing key constraints identify entities by enforcing label equality on node types. These constraints can be too restrictive to characterize structures and node labels that are syntactically different but semantically equivalent. Lastly, we propose a new class of key constraints, Ontological Graph Keys (OGKs) that extend conventional graph keys by ontological subgraph matching between entity labels and an external ontology. We study the entity matching problem with OGKs. We develop efficient algorithms to perform entity matching based on a Chase procedure. The proposed dependencies and algorithms in this thesis improve consistency detection in temporal graphs, automate the discovery of keys in graphs, and enrich the semantic expressiveness of graph keys. / Dissertation / Doctor of Science (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27744 |
| Date | January 2022 |
| Creators | Alipourlangouri, Morteza |
| Contributors | Chiang, Fei, Computing and Software |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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