2019 First International Conference on Graph Computing (GC)最新文献_第2页

Understanding SPARQL Endpoints through Targeted Exploration and Visualization 通过有针对性的探索和可视化理解SPARQL端点

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00012

Maria Krommyda, Verena Kantere

{"title":"Understanding SPARQL Endpoints through Targeted Exploration and Visualization","authors":"Maria Krommyda, Verena Kantere","doi":"10.1109/GC46384.2019.00012","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00012","url":null,"abstract":"The Resource Description Framework (RDF) has provided a unified way for everyone to publish their data. The SPARQL query language has been developed to facilitate the exploration of this information. The RDF format, however, is addressed mainly to machines and it is not easily comprehended by humans. Due to the value of the information available through the SPARQL endpoints many efforts have been dedicated to facilitate the access and exploration of this information from users with limited knowledge of the Semantic Web. The main challenge of such approaches is the diversity of the information contained the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on knowledge base containing raw data from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131465067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Graph-Based Recommender System for Food Products 基于图的食品产品推荐系统

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00020

Arpita Mathur, Sai Kumar Juguru, M. Eirinaki

引用次数: 3

An Object-Pose Estimation Acceleration Technique for Picking Robot Applications by Using Graph-Reusing k-NN Search 基于图重用的k-NN搜索的拾取机器人目标姿态估计加速技术

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00018

Atsutake Kosuge, T. Oshima

引用次数: 6

Streaming and Batch Algorithms for Truss Decomposition 桁架分解的流和批处理算法

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-08-28 DOI: 10.1109/GC46384.2019.00016

Venkata Rohit Jakkula, G. Karypis

{"title":"Streaming and Batch Algorithms for Truss Decomposition","authors":"Venkata Rohit Jakkula, G. Karypis","doi":"10.1109/GC46384.2019.00016","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00016","url":null,"abstract":"Truss decomposition is a method used to analyze large sparse graphs in order to identify successively better connected subgraphs. Since in many domains the underlying graph changes over time, its associated truss decomposition needs to be updated as well. This work focuses on the problem of incrementally updating an existing truss decomposition and makes the following three significant contributions. First, it presents a theory that identifies how the truss decomposition can change as new edges get added. Second, it develops an efficient incremental algorithm that incorporates various optimizations to update the truss decomposition after every edge addition. These optimizations are designed to reduce the number of edges that are explored by the algorithm. Third, it extends this algorithm to batch updates (i.e., where the truss decomposition needs to be updated after a set of edges are added), which reduces the overall computations that need to be performed. We evaluated the performance of our algorithms on real-world datasets. Our incremental algorithm achieves over 250000x average speedup for inserting an edge in a graph with 10 million edges relative to the non-incremental algorithm. Further, our experiments on batch updates show that our batch algorithm consistently performs better than the incremental algorithm.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116009344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3