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Modeling Relational Data as Graphs for Mining
2009
International Conference on Management of Data
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The focus of this paper is to develop algorithms and a framework for modeling transactional data stored in relational database into graphs for mining. ...
Real-world data has been used for generating graphs and mining them for various patterns. ...
Once the data is modeled as graphs, they can be used as the input for graph-based data mining (Subdue [1, 2, 3] in our case). ...
dblp:conf/comad/PradhanCT09
fatcat:uder6dlg7faqhnaptfysi6rah4
Graph BI & Analytics: Current State and Future Challenges
[chapter]
2018
Lecture Notes in Computer Science
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They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. ...
We survey the topics of graph modeling, management, processing and analysis in graph warehouses. ...
Graph Mining Data mining refers to the process of discovering patterns or models for data. ...
doi:10.1007/978-3-319-98539-8_1
fatcat:56fgfclobveifcbkwvbyejt5pi
Graph-based managing and mining of processes and data in the domain of intellectual property
2021
Information Systems
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We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving ...
In this paper, we propose a bottomup approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as ...
The work was also supported by the Federal Ministry for Digital and Economic Affairs (BMDW), the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), and ...
doi:10.1016/j.is.2021.101844
fatcat:qu5guju44ra5viqh4aah4nutbq
Link mining
2005
SIGKDD Explorations
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Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. ...
While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among ...
Acknowledgments Thanks to the students in the LINQs group at UMD, especially Indrajit Bhattacharya, Mustafa Bilgic, and Prithviraj Sen for their input. ...
doi:10.1145/1117454.1117456
fatcat:z33bv3nf3rac5o3t43poebum7i
Property Oriented Relational-To-Graph Database Conversion
2016
Automatika
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Given the increasing awareness about the benefits of data analysis as well as current research interest in graph mining techniques, we aim to enable the usage of those techniques on relational data. ...
In that regard, we propose a universal relational-to-graph data conversion algorithm which can be used in preparation of data to perform a graph mining analysis. ...
She served as Vice Dean for students and Education at the University of Zagreb, Faculty of Electrical Engineering and Computing. ...
doi:10.7305/automatika.2017.02.1581
fatcat:e5yd4wf67zas5jqrn5cqephc4u
Re-Mining Association Mining Results Through Visualization, Data Envelopment Analysis, and Decision Trees
[chapter]
2012
Atlantis Computational Intelligence Systems
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Re-mining is a general framework which suggests the execution of additional data mining steps based on the results of an original data mining process. ...
The methodology suggests re-mining using data visualization, data envelopment analysis, and decision trees. ...
The authors also thank Ilhan Karabulut for her work that inspired the visual re-mining approach on association graphs.
Bibliography ...
doi:10.2991/978-94-91216-77-0_28
fatcat:ofiaebjbzbcwrcugijfvczu74y
Semantic web for integrated network analysis in biomedicine
2009
Briefings in Bioinformatics
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We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. ...
Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb^drug interactions ...
RDF provides a simple graph data model for encoding networked data on the Web using node and binary relations. ...
doi:10.1093/bib/bbp002
pmid:19304873
fatcat:4q6skldwufbtxfdymxl6uraleq
Graph Mining
[chapter]
2013
Encyclopedia of Systems Biology
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Network analysis, Learning from graph structured data. Definition Graph mining is the study of how to perform data mining and machine learning on data represented with graphs. ...
One can distinguish between on the one hand transactional graph mining, where a database of separate, independent graphs is considered (such as databases of molecules and databases of images), and on the ...
The domains focussing on data mining using these representations are called relational data mining and inductive logic programming, respectively. Representing data with graphs has several advantages. ...
doi:10.1007/978-1-4419-9863-7_615
fatcat:2oah4qdrqvhahfdviu5wpomy5i
Using a knowledge graph and query click logs for unsupervised learning of relation detection
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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As a first step towards this direction, we present unsupervised methods for training relation detection models exploiting the semantic knowledge graphs of the semantic web. ...
We use the snippets that the search engine returns to create natural language examples that can be used as the training data for each relation. ...
Acknowledgments: We thank Ashley Fidler for her help with creating the development and test data sets, and Umut Ozertem for discussions. ...
doi:10.1109/icassp.2013.6639289
dblp:conf/icassp/Hakkani-TurHT13
fatcat:vfrglwgmfna7rbpbug25blq47a
Big Graph Mining: Frameworks and Techniques
2016
Big Data Research
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2016 pre-print arXiv:1602.03072v1 fatcat:avuumkds5zgn7fdq6yv6bpef7q
Then it presents a survey of current researches in the field of data mining / pattern mining in big graphs and discusses the main research issues related to this field. ...
This task consists on using data mining algorithms to discover interesting, unexpected and useful patterns in large amounts of graph data. It aims also to provide deeper understanding of graph data. ...
In GraphX, graphs are defined as a pair of two specialized RDD. The first one contains data related to vertices and the second one contains data related to edges of the graph. ...
doi:10.1016/j.bdr.2016.07.002
fatcat:sctq3qlbmndd3islrbugcxxzv4
Large Graph Mining: Recent Developments, Challenges and Potential Solutions
[chapter]
2013
Lecture Notes in Business Information Processing
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In this paper, we will review the new paradigms of large graph processing and their applications to graph mining domain using the distributed and shared nothing approach used for large data by internet ...
Finally, we will expose a set of open research questions linked with serveral new business requirements as the graph data warehouse. ...
and targets for mining using data mining as building blocs in a multi-step mining process using data cube computation for speeding-up repeated models construction The graph model should be another way ...
doi:10.1007/978-3-642-36318-4_5
fatcat:2lvpyxs7obbs3i5lfv344dv5om
A model for fast web mining prototyping
2009
Proceedings of the Second ACM International Conference on Web Search and Data Mining - WSDM '09
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The objective of this paper is to present a model for fast Web mining prototyping, referred to as WIM -Web Information Mining. ...
The underlying conceptual model of WIM provides its users with a level of abstraction appropriate for prototyping and experimentation throughout the Web data mining task. ...
It is based on a model, referred to as WIM -Web Information Mining -, designed for processing data mining tasks for Web mining applications. ...
doi:10.1145/1498759.1498816
dblp:conf/wsdm/PereiraBZB09
fatcat:ncyv2k4bzzdrth4gvqnvwrtfiq
Current Situation and Application of Graph Data Mining Technology
2017
International Journal of Database Theory and Application
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As an important data structure, graph can be used to describe the complex relationship among stuffs. ...
Traditional data mining technology has been applied to the field of graph data mining constantly. Consequently the development of the graph data mining technology has been accelerated. ...
model, the graph model and the relational model. ...
doi:10.14257/ijdta.2017.10.3.01
fatcat:voyvocal3vf6vhvnb7yykl2vzm
Mining Heterogeneous Information Graph for Health Status Classification
2018
2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)
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This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. ...
However, how to mine these data effectively and efficiently still remains a critical challenge. ...
By mining knowledge from a heterogeneous graph, the model has contributed to a significant improvement for classification problem in healthcare data. ...
doi:10.1109/besc.2018.8697292
dblp:conf/besc/PhamTZYZC18
fatcat:le6qotnxh5bcrdev6kkvpcxnuu
Concurrency In Web Access Patterns Mining
2009
Zenodo
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From experiments conducted on large-scale synthetic sequence data as well as real web access data, it is demonstrated that CAP mining provides a powerful method for structural knowledge discovery, which ...
WAP-Graph also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more complex web page requests. ...
PSPM does not mine structural relation patterns directly from the data, as it first takes advantage of existing sequential patterns mining methods. ...
doi:10.5281/zenodo.1074754
fatcat:holownkbbffzzlha34vqncrtrm
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