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KOGNAC: Efficient Encoding of Large Knowledge Graphs [article]

Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum
2016 arXiv   pre-print
Many Web applications require efficient querying of large Knowledge Graphs (KGs).  ...  We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques.  ...  Given the encoding runtime, this improvement comes at little cost. Conclusions We proposed KOGNAC, an algorithm for efficient encoding of RDF terms in large Knowledge Graphs.  ... 
arXiv:1604.04795v2 fatcat:ejs3c7kg45dqhpxd6lypidvxoi

A large-scale spatio-temporal data analytics system for wildfire risk management

Ziyuan Wang, Hoang Tam Vo, Mahsa Salehi, Laura Irina Rusu, Claire Reeves, Anna Phan
2017 Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data - GeoRich '17  
In this paper, we describe a large-scale data-driven system for personalized risk mitigation, fire response's resource optimization and dynamic evacuation planning.  ...  Wildfires have been a significant concern for communities and fire response agencies in many countries.  ...  algorithms that many machine learning applications rely on.  ... 
doi:10.1145/3080546.3080549 dblp:conf/sigmod/WangVSRRP17 fatcat:guty2c6cabbfxg46jbll23ltr4

Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification

Liang Xie, Xi Fang, Alessandro Severino
2021 Journal of Advanced Transportation  
In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology.  ...  In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario.  ...  supported by the National Natural Science Foundation of China (no. 61702388), Equipment Pre-Research Fund (JZX7Y20190253036101), Equipment Pre-Research Ministry of Education Joint Fund (6141A02033703), and  ... 
doi:10.1155/2021/5038832 fatcat:penkzm5cwndjdad4rnni3vdsri

Schemaless and structureless graph querying

Shengqi Yang, Yinghui Wu, Huan Sun, Xifeng Yan
2014 Proceedings of the VLDB Endowment  
Our experimental results show that this new graph querying paradigm is promising: It identifies high-quality matches for both keyword and graph queries over real-life knowledge graphs, and outperforms  ...  Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users.  ...  This becomes even more challenging when multiple transformations are applicable to the same query, and the answer pool becomes very large.  ... 
doi:10.14778/2732286.2732293 fatcat:dn6hgjwzsjcoratfatleqoim44

An analysis of the graph processing landscape [article]

Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
2021 arXiv   pre-print
and different definitions related to the potential for a graph to be updated.  ...  This survey is aimed at both the experienced software engineer or researcher as well as the newcomer looking for an understanding of the landscape of solutions (and their limitations) for graph processing  ...  A static graph over which we want to perform analytics is a scenario different from maintaining a large graph available for separate queries and susceptible to updates.  ... 
arXiv:1911.11624v3 fatcat:t44dfa5cvfbk7exz4s2synm5z4

Semantics-Based Resource Discovery in Large-Scale Grids [chapter]

Juan Li, Samee U. Khan, Nasir Ghani
2013 Large Scale Network-Centric Distributed Systems  
), applications, and services are on grid networks.  ...  However, resource discovery in a global-scale grid is challenging due to the considerable diversity, large number, dynamic behavior, and geographical distribution of the resources.  ...  For comparison, we also implement the learningbased ShortCut algorithm and random-walk based Gnutella algorithm.  ... 
doi:10.1002/9781118640708.ch17 fatcat:4bs2bivxxbektcfymzpvg5r2si

Recent Advances in Graph Partitioning [chapter]

Aydın Buluç, Henning Meyerhenke, Ilya Safro, Peter Sanders, Christian Schulz
2016 Lecture Notes in Computer Science  
We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions.  ...  Acknowledgements We express our gratitude to Bruce Hendrickson, Dominique LaSalle, and George Karypis for many valuable comments on a preliminary draft of the manuscript.  ...  After each greedy step, v c and v p are removed from their respective graphs, and the communication cost and distance values of the remaining nodes are updated.  ... 
doi:10.1007/978-3-319-49487-6_4 fatcat:4zamxcmgvfbaxndjgxv6jog6km

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

Tahir Emre Kalaycı, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer, Jelena Rubeša-Zrim
2021 Sustainability  
Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation  ...  This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing.  ...  Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su13031583 fatcat:437trojb7nfrtfx4u52vhdjs2m

Recent Advances in Graph Partitioning [article]

Aydin Buluc, Henning Meyerhenke, Ilya Safro, Peter Sanders, Christian Schulz
2015 arXiv   pre-print
We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions.  ...  Acknowledgements We express our gratitude to Bruce Hendrickson, Dominique LaSalle, and George Karypis for many valuable comments on a preliminary draft of the manuscript.  ...  Global sum type cost functions do not have the drawback of requiring global updates.  ... 
arXiv:1311.3144v3 fatcat:zmvhlkh7ynbzvm353fv22f2gnq

Efficient distributed reachability querying of massive temporal graphs

Tianming Zhang, Yunjun Gao, Lu Chen, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
2019 The VLDB journal  
We also present algorithms that exploit TVL to achieve efficient support for distributed reachability querying over temporal graphs in Pregel-like systems.  ...  and query processing.  ...  It is also of interest to develop efficient indexes for supporting fast vertex/edge deletion and temporal interval update operations.  ... 
doi:10.1007/s00778-019-00572-x fatcat:psu3y6ejezct3itfsnkxnrqcvu

Spatio-Temporal Top-k Similarity Search for Trajectories in Graphs [article]

Lutz Oettershagen, Anne Driemel, Petra Mutzel
2020 arXiv   pre-print
This distance function is the basis for our index structures, which can be constructed efficiently, need only linear memory, and can quickly answer queries for the top-k most similar trajectories.  ...  Our evaluation on real-world and synthetic data sets shows that our algorithms outperform the baselines with respect to indexing time by several orders of magnitude while achieving similar or better query  ...  Q2: How fast are queries of our algorithms compared to the baseline and to the heuristics in [6] ? Do our index solutions improve the query times?  ... 
arXiv:2009.06778v2 fatcat:hhx5h4spqjhnxgok576n4twn3a

An analysis of the graph processing landscape

Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
2021 Journal of Big Data  
and different definitions related to the potential for a graph to be updated.  ...  For these systems focused on processing the bulk of graph elements, common use-cases consist in executing for example algorithms for vertex ranking or community detection, which produce insights on graph  ...  Acknowledgements Not applicable.  ... 
doi:10.1186/s40537-021-00443-9 pmid:33850687 pmcid:PMC8033100 fatcat:vnwcn2pwszhv3detnwcv6jrthu

The City Brain: Practice of Large-Scale Artificial Intelligence in the Real World

Xiansheng Hua, xu shen, Jianfeng Zhang, jianqiang huang, Jingyuan Chen, Qin Zhou, Zhihang Fu, Yiru Zhao
2019 IET Smart Cities  
fast-growing computing capacity.  ...  Then they focus on the system overview and key technical details of each component of the City Brain system, from cognition to intervention.  ...  Graph Indexing and Graph query: graph indexing is a very important pre-processing step in graph query. Indexing guarantees the uniqueness of each row of data in the database table.  ... 
doi:10.1049/iet-smc.2019.0034 fatcat:45qm7t5qgve7hgyvfzjl7huocq

Graph Constraints in Urban Computing: Dealing with Conditions in Processing Urban Data

Laurent DOrazio, Mirian Halfeld-Ferrari, Carmem Satie Hara, Nadia P. Kozievitch, Martin A. Musicante
2017 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)  
This position paper presents our views towards developing techniques for querying and evolving graph-modeled datasets based on userdefined constraints.  ...  , vehicles, buildings, and humans, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion.  ...  In order to provide fast responses to the plethora of applications and users accessing available urban information, the data management system should be able to execute queries on large volumes of data  ... 
doi:10.1109/ithings-greencom-cpscom-smartdata.2017.171 dblp:conf/ithings/DOrazioAHKM17 fatcat:o6tgizddejarjn4y53nvshaygq

Combinatorial optimization and reasoning with graph neural networks [article]

Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković
2021 arXiv   pre-print
However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by  ...  This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.  ...  Large inference cost In some machine learning applications for CO, the inference might be repeated thousands of times to minimize the wall-clock time being a core objective.  ... 
arXiv:2102.09544v2 fatcat:eweej3mq2bbohaifazeghswcpi
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