Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 54 for “"Graph data"”.
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Multi-Dimensional Analysis of Graph Data
… intuitive and insightful knowledge discovery on graphs, especially when the data is large and complex. Given the emerging trend of huge information networks as listed above, it is an important research topic to devote more efforts to. We point out a few possible future works, especially …
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Low latency queries on big graph data
The availability of large datasets and on-demand system capacity to analyze these datasets has led to exciting new applications in the context of big graph data. Many big graph data applications --- social search and ranking, personalized and socially-sensitive search, social network analysis, …
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Mining, Indexing and Similarity Search in Large Graph Data Sets
… may significantly deepen the understanding of data mining principles in structural pattern discovery, interpretation and search. The formulation of a general graph information system through this study could provide fundamental supports to graph-intensive applications in multiple domains.
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User profiling in social networks based on graph data: bridging local and global structures
Made available in DSpace on 2021-09-17T01:11:15Z (GMT). No. of bitstreams: 2 JAVARI-DISSERTATION-2021.pdf: 2535622 bytes, checksum: c0afcf53f00f76e059513d16f27f17af (MD5) LICENSE.txt: 4208 bytes, checksum: 1593f443f549886e1a9f79e13243a0b3 (MD5) Previous issue date: 2021-04-23
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Privacy, Access Control, and Integrity for Large Graph Databases
Graph data are extensively utilized in social networks, collaboration networks, geo-social networks, and communication networks. Their growing usage in cyberspaces poses daunting security and privacy challenges. Data publication requires privacy-protection mechanisms to guard against information …
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A framework for incremental view graph maintenance
Nowadays, graph data models are employed, when relationships between entities have to be stored and are in the scope of queries. For each entity, this graph data model locally stores relationships to adjacent entities. Users employ graph queries to query and modify these entities and relationships. …
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Generative Models Driven Graph Outlier Detection
Graph data are pervasive across various domains, including social networks, biological networks, and communication systems. The detection of outliers in graph data—substructures that significantly deviate from the norm—is crucial for uncovering fraudulent activities, network vulnerabilities, and …
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Towards Storing 3D Model Graphs in Relational Databases
The increasing relevance of massive graph data reinforces the need for adequate graph data management. While several graph database engines have been developed, the storage of graph data in a relational database management system, and therefore the seamless integration into existing information …
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Visual programming using graphics, relations, and classes
… However, little progress has been made in using graphics to support ""real-world"" programming. The GRClass system provides a solution by combining Graphics, Relations, and Classes to provide a visual interface for programming graph data structures within an object-oriented framework. This is …
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G-hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases
Structured data such as graphs and networks have posed significant challenges to fundamental aspects of data management including efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important …
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A comprehensive and efficient framework for subgraph matching
… aims to develop a comprehensive and efficient subgraph matching framework to support graph analytic tasks. In the era of information technology, graphs, consisting of vertices and edges, are widely used to model real-world entities and their relationships, offering new opportunities to understand …
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Large-Scale Constraint-Based Pattern Mining
… pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these patterns. For item-set data, we …
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Context-aware pedestrian intent prediction for connected and automated vehicles
… deep learning. Research in deep learning on graphs is gaining momentum, showcasing the powerful descriptive capabilities of graph structures. These structures provide essential relationship data among various elements, proving invaluable across diverse learning applications. Our study …
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Enhancing Interpretability: The Role of Concept-based Explanations Across Data Types
… how similar approaches transfer to the temporal data modality. In particular, we introduce our Model Explanations via Model Extraction (MEME) framework, a first-of-its-kind temporal CbE framework capable of extracting Concept-based Models from Recurrent Neural Networks (RNNs). Using MEME, we …
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ScaleGPS: Scalable Graph Parallel Sampling via Data-centric Performance Engineering
Graph sampling extracts representative samples of a graph, so that approximate graph algorithms can be used in place of expensive, exact algorithms while still achieving highquality results. Thus, graph sampling plays an important role in many modern graph-based applications, such as graph machine …
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Mogway: um arcabouço para bancos de dados múltiplos grafos
Graphs are a powerful representation technique, capable of capturing the relationship between entities. They are useful to understand a wide variety of data sets from many areas like science, government and business. In the last years has resurged the interest in storing and managing graph data due …
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Algorithms for Logic Design Automation (Rtcad, Synthesis)
… a collection of algorithms that transform a graph data structure derived from a behavior description into a logic design at the register transfer level.
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Splinter : practical private queries on public data
… of the Function Secret Sharing (FSS) cryptographic primitive. I worked on a library in Golang that applied an optimized FSS protocol, and exposed an API to generate and evaluate different kinds of queries. I then built a system with servers that handle queries to the database, and clients …
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Infrastructure for modeling and inference engineering with 3D generative scene graphs
… of 3D scene geometry called a scene graph. However, there remain several challenges in the practical implementation of scene graph models, including human-editable specification, visualization, priors, structure inference, hyperparameters tuning, and benchmarking. In this thesis, I …
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Parallel Algorithms, Optimizations, and Benchmarks for Metric and Graph Clustering
… task of detecting groups of similar objects in data. Clustering can be used to identify the underlying substructures of data and can detect essential functional groups, such as people with similar interests, news articles on similar topics, or proteins with similar utilities, which can then be …
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