Amal Beldi, Salma Sassi, Richard Chbeir, Abderrazek Jemai
{"title":"DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs","authors":"Amal Beldi, Salma Sassi, Richard Chbeir, Abderrazek Jemai","doi":"10.2298/csis230331062b","DOIUrl":null,"url":null,"abstract":"Advances in computing resources have enabled the processing of vast amounts\n of data. However, identifying trends in such data remains challenging for\n humans, especially in fields like medicine and social networks. These\n challenges make it difficult to process, analyze, and visualize the data. In\n this context, graph summarization has emerged as an effective framework\n aiming to facilitate the identification of structure and meaning in data.\n The problem of graph summarization has been studied in the literature and\n many approaches for static contexts are proposed to summarize the graph.\n These approaches provide a compressed version of the graph that removes many\n details while retaining its essential structure. However, they are\n computationally prohibitive and do not scale to large graphs in terms of\n both structure and content. Additionally, there is no framework providing\n summarization of mixed sources with the goal of creating a dynamic,\n syntactic, and semantic data summary. In this paper, our key contribution is\n focused on modeling data graphs, summarizing data from multiple sources\n using a schema-driven approach, and visualizing the graph summary version\n according to the needs of each user. We demonstrate this approach through a\n case study on the use of the E-health domain.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"55 1","pages":"0"},"PeriodicalIF":1.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science and Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2298/csis230331062b","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Advances in computing resources have enabled the processing of vast amounts
of data. However, identifying trends in such data remains challenging for
humans, especially in fields like medicine and social networks. These
challenges make it difficult to process, analyze, and visualize the data. In
this context, graph summarization has emerged as an effective framework
aiming to facilitate the identification of structure and meaning in data.
The problem of graph summarization has been studied in the literature and
many approaches for static contexts are proposed to summarize the graph.
These approaches provide a compressed version of the graph that removes many
details while retaining its essential structure. However, they are
computationally prohibitive and do not scale to large graphs in terms of
both structure and content. Additionally, there is no framework providing
summarization of mixed sources with the goal of creating a dynamic,
syntactic, and semantic data summary. In this paper, our key contribution is
focused on modeling data graphs, summarizing data from multiple sources
using a schema-driven approach, and visualizing the graph summary version
according to the needs of each user. We demonstrate this approach through a
case study on the use of the E-health domain.
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Computer Science and Information Systems (ComSIS) is an international refereed journal, published in Serbia. The objective of ComSIS is to communicate important research and development results in the areas of computer science, software engineering, and information systems.