115,225 research outputs found
Graph based management of temporal data
In recent decades, there has been a significant increase in the use of smart devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require users to adapt to new querying languages. In this thesis, we propose a method to improve the modeling and retrieval of temporal data using an existing graph database system (i.e., Neo4j) without extending with additional operators. Our work focuses on temporal data represented as intervals (event with a start and end time). We propose a novel way of storing temporal interval as cartesian points where the start time and the end time are stored as the x and y axis of the cartesian coordinate. We present how queries based on Allenβs interval relationships can be represented using our model on a cartesian coordinate system by visualizing these queries. Temporal queries based on Allenβs temporal intervals are then used to validate our model and compare with the traditional way of storing temporal intervals (i.e., as attributes of nodes). Our experimental results on a soccer graph database with around 4000 games show that the spatial representation of temporal interval can provide significant performance (up to 3.5 times speedup) gains compared to a traditional model
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λ³Έ μ°κ΅¬μ μ£Όμ κΈ°μ¬λ μ€μΊν λλ©΄μ μ¬μ©νμ¬ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€λ₯Ό ꡬμΆνκΈ° μν νλ‘μΈμ€λ₯Ό κ°λ°ν κ²μ΄λ€. ꡬ체μ μΌλ‘, μ΄λμ½μμ μ΄λμ μ΄μ μ λκ³ μ€κ³ν λ°μ΄ν° λͺ¨λΈμ κΈ°λ°μΌλ‘ ν λ°μ΄ν°λ² μ΄μ€ ꡬμΆμ΄ κ°λ₯νλ―λ‘ μ΄λμ½μμ© μ€λ΄ κΈΈμλ΄ μλΉμ€μ νμ©λ μ μλ€. λν, ν ν΄λ‘μ§ κ΅¬μΆ λ° κ·Έλν λ°μ΄ν°λ² μ΄μ€λ‘μ λ³νμ μν νμ νλ‘μμ Έλ₯Ό κ°λ°νμμΌλ©°, μ μ νλ‘μΈμ€λ ν΄λΉ νλ‘μμ Έλ€λ‘ ꡬμ±λμ΄ λλ©΄ μ
λ ₯μ ν΅ν΄ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€ ꡬμΆμ κ°λ₯νκ² νλ€. ν΄λΉ νμ νλ‘μμ Έλ€μ μλμΌλ‘ μνλ μ μμ΄ λ°μ΄ν°λ² μ΄μ€ κ΅¬μΆ μ μμλλ μκ°κ³Ό λΉμ©μ μ κ°ν μ μλ€. λν, λ€μν μ ν λ° λΉμ ν λ°μ΄ν°μ μ°κ³μ μ ν©ν κ·Έλν λ°μ΄ν°λ² μ΄μ€μ νΉμ§μ μν΄, μ μν νλ‘μΈμ€λ₯Ό ν΅ν΄ ꡬμΆν μ€λ΄ λ°μ΄ν°λ² μ΄μ€λ κΈ°μ‘΄ κ³΅κ° λͺ¨λΈμ κΈ°λ₯μ ν¬ν¨νλ©΄μ λ€μν μ νμ κΈΈμλ΄ μλΉμ€μ νμ©λ μ μμ κ²μΌλ‘ κΈ°λλλ€.Changes to the indoor environment have increased social interest in ensuring the mobility of people with disabilities. Therefore, the demand for customized indoor routing services for people with mobility disabilities (PWMD), who have many travel restrictions, is increasing. These services have progressed from spatial routing to personalized routing, which reflects personal preferences and experiences in planning an optimal path. In this regard, it is necessary to generate a database for PWMD with a flexible schema suitable for the efficient manipulation and processing of data.
This study aims to propose a technique of generating an indoor graph database for PWMD using scanned floor plans. First, a conceptual data model was developed by deriving relevant indoor features and influential factors, considering various international regulations on indoor environments. Also, the accessibility index was designed based on the data model to quantify the difficulties in accessing spaces based on each indoor spaces geometric characteristics. Next, a three-stage process was proposed: retrieving the structure of spaces from scanned floor plans through a transfer learning-based approach, retrieving topology and assessing accessibility for creating an indoor network model for PWMD, and converting the network model into a graph database. Specifically, an indoor structure map is created by fine-tuning the modified Resnet-based model with newly annotated floor plans for extracting structure information. Also, based on the spatial relationship of the extracted features, the indoor network model was created by abstracting indoor spaces with nodes and links. The accessibility of each space is determined by the proposed indices and thresholds; thereby, a feasible network for PWMD could be derived. Then, a process was developed for automatically converting an indoor network model, including accessibility property, into a graph database.
The proposed technique was applied to the Seoul National University dataset to generate an indoor graph database for PWMD. Two scenario-based routing tests were conducted using the generated database to verify the utility of results: multi-floor routing and integrated indoor-outdoor routing. As a result, compared with the path for general pedestrians, the optimal path for PWMD was derived by avoiding inaccessible spaces, including vertical movement using elevators rather than the nearest stairs. In other words, applying the proposed technique, a database that adequately described an indoor environment in terms of PWMD with sufficient mobile constraint information could be constructed. Moreover, an integrated indoor-outdoor routing could be conducted by only creating an entrance-labeled relationship, without scale and coordinate transformation. This result reflects the usability of the generated graph database and its suitability regarding the incorporation of multiple individual data sources.
The main contribution lies in the development of the process for generating an indoor graph database for PWMD using scanned floor plans. In particular, the database for PWMD routing can be generated based on the proposed data model with PWMD-related features and factors. Also, sub-procedures for topology retrieval and graph database conversion are developed to generate the indoor graph database by the end-to-end process. The developed sub-procedures are performed automatically, thereby reducing the required times and costs. It is expected that the target database of the proposed process can be generated considering utilization for various types of routing since the graph database is easily integrated with multiple types of information while covering the existing spatial models function.1. Introduction 1
1.1 Objectives and contributions 1
1.2 Related works 7
1.2.1 Indoor environment conceptualization 7
1.2.2 Indoor data construction 11
1.2.3 Accessibility assessment 19
1.3 Research scope and flow 22
2. Conceptual modeling 26
2.1 Relevant features and factors 28
2.2 Proposed data model 30
2.3 Space accessibility for PWMD 36
2.3.1 Influential factors within indoor environments 37
2.3.2 Accessibility index 41
3. Indoor graph database for PWMD from scanned floor plans 43
3.1 Retrieving structure of indoor spaces 43
3.1.1 Pre-trained model for detecting indoor geometry 45
3.1.2 Dataset with new annotation 47
3.1.3 Transfer learning-based approach 52
3.2 Generating the indoor network model for PWMD 56
3.2.1 Definition of nodes and links in the network model 60
3.2.2 The classification rule of space polygons 63
3.2.3 Connection between general spaces and doors 68
3.2.4 Node-link generation for horizontal transition spaces 71
3.2.5 Vertical link generation 75
3.2.6 Connectivity and accessibility information generation 79
3.3 Indoor graph database for PWMD 80
3.3.1 Graph representation of indoor environments 80
3.3.2 Conversion of network model into graph database 83
3.4 Entire process 87
4. Experiment and results 89
4.1 Experimental setup and test data 89
4.2 Evaluation for retrieved information 92
4.2.1 Results of structure retrieval 92
4.2.2 Results of topology retrieval 99
4.3 Generated indoor graph database for PWMD 128
4.3.1 Results of the indoor graph database for PWMD 128
4.3.2 Query-based routing 136
5. Conclusion 147
References 150
Appendix 166
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GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
Recent research on pattern discovery has progressed from mining frequent
patterns and sequences to mining structured patterns, such as trees and graphs.
Graphs as general data structure can model complex relations among data with
wide applications in web exploration and social networks. However, the process
of mining large graph patterns is a challenge due to the existence of large
number of subgraphs. In this paper, we aim to mine only frequent complete graph
patterns. A graph g in a database is complete if every pair of distinct
vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining
algorithm developed to explore interesting pruning techniques to extract
maximal complete graphs from large spatial dataset existing in Sloan Digital
Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high
efficiency especially in the presence of large number of patterns. In this
paper, we describe GCG that can mine not only simple co-location spatial
patterns but also complex ones. To the best of our knowledge, this is the first
algorithm used to exploit the extraction of maximal complete graphs in the
process of mining complex co-location patterns in large spatial dataset.Comment: 1
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