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Theses

Querying and Mining Multigraphs

Vijay Ingalalli 1
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : With the ever-increasing growth of data and information, extracting the right knowledge has become a real challenge.Further, the advanced applications demand the analysis of complex, interrelated data which cannot be adequately described using a propositional representation. The graph representation is of great interest for the knowledge extraction community, since graphs are versatile data structures and are one of the most general forms of data representation. Among several classes of graphs, textit{multigraphs} have been captivating the attention in the recent times, thanks to their inherent property of succinctly representing the entities by allowing the rich and complex relations among them.The focus of this thesis is streamlined into two themes of knowledge extraction; one being textit{knowledge retrieval}, where we focus on the subgraph query matching aspects in multigraphs, and the other being textit{knowledge discovery}, where we focus on the problem of frequent pattern mining in multigraphs.This thesis makes three main contributions in the field of query matching and data mining.The first contribution, which is very generic, addresses querying subgraphs in multigraphs that yields isomorphic matches, and this problem finds potential applications in the domains of remote sensing, social networks, bioinformatics, chemical informatics. The second contribution, which is focussed on knowledge graphs, addresses querying subgraphs in RDF multigraphs that yield homomorphic matches. In both the contributions, we introduce efficient indexing structures that capture the multiedge information. The query matching processes introduced have been carefully optimized, w.r.t. the time performance and the heuristics employed assure robust performance.The third contribution is in the field of data mining, where we propose an efficient frequent pattern mining algorithm for multigraphs. We observe that multigraphs pose challenges while exploring the search space, and hence we introduce novel optimization techniques and heuristic search methods to swiftly traverse the search space.For each proposed approach, we perform extensive experimental analysis by comparing with the existing state-of-the-art approaches in order to validate the performance and correctness of our approaches.In the end, we perform a case study analysis on a remote sensing dataset. Remote sensing dataset is modelled as a multigraph, and the mining and query matching processes are employed to discover some useful knowledge.
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Vijay Ingalalli. Querying and Mining Multigraphs. Other [cs.OH]. Université Montpellier, 2017. English. ⟨NNT : 2017MONTS080⟩. ⟨tel-01951969⟩

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