Verena Kantere

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Maria Krommyda

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Publications

2021
[j16]
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Maria Krommyda, Verena Kantere:
Spatial Data Management in IoT Systems: Solutions and Evaluation. Int. J. Semantic Comput. 15(1): 117-139 (2021)
[j14]
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Konstantinos Tsitseklis, Maria Krommyda, Vasileios Karyotis, Verena Kantere, Symeon Papavassiliou:
Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases. IEEE Trans. Netw. Sci. Eng. 8(2): 1269-1282 (2021)
[c69]
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Maria Krommyda, Verena Kantere:
SPARQL-vision: A Platform for Querying, Visualising and Exploring SPARQL endpoints. CIKM 2021: 4730-4733
[c68]
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  - conf/dexa/TzanikosKK21
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Marinos Tzanikos, Maria Krommyda, Verena Kantere:
A Highly Modular Architecture for Canned Pattern Selection Problem. DEXA (2) 2021: 77-83
[c66]
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Maria Krommyda, Konstantinos Tsitseklis, Verena Kantere, Vasileios Karyotis, Symeon Papavassiliou:
Visualizing and Exploring Big Datasets based on Semantic Community Detection. EDBT 2021: 678-681
2020
[j13]
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Maria Krommyda, Verena Kantere:
Semantic Analysis for Conversational Datasets: Improving Their Quality Using Semantic Relationships. Int. J. Semantic Comput. 14(3): 395-422 (2020)
[c60]
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Maria Krommyda, Verena Kantere:
Visualization Systems for Linked Datasets. ICDE 2020: 1790-1793
[c59]
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Maria Krommyda, Verena Kantere, Yannis Vassiliou:
Efficient Representation of Very Large Linked Datasets as Graphs. ICEIS (1) 2020: 106-115
2019
[c56]
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  - conf/gc/KrommydaK19
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Maria Krommyda, Verena Kantere:
Understanding SPARQL Endpoints through Targeted Exploration and Visualization. GC 2019: 21-28
[c55]
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Maria Krommyda, Verena Kantere, Yannis Vassiliou:
IVLG: Interactive Visualization of Large Graphs. ICDE 2019: 1984-1987
[c54]
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Maria Krommyda, Verena Kantere, Yannis Vassiliou:
Divide and Conquer Technique for Large Linked Datasets. MEDES 2019: 120-125

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