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A Dependence Stability Bound based on the VC Dimension for Relational Classification

Xing Wang, Hui He, Bin-Xing Fang, Hong-Li Zhang
2015 International Journal of Database Theory and Application  
RC models do not have improved stability to smooth the perturbations generated by variations in the correlation between the relational data.  ...  Relational classification (RC) is concerned with the application of statistical learning to relational data.  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers.  ... 
doi:10.14257/ijdta.2015.8.3.11 fatcat:tjonthkl7vdrbgknavwng5hkv4

Refactor Business Process Models with Maximized Parallelism

Tao Jin, Jianmin Wang, Yun Yang, Lijie Wen, Keqin Li
2016 IEEE Transactions on Services Computing  
More specifically, we analyze the real causal relations between business tasks based on data operation dependency analysis, and refactor business process models with process mining technology.  ...  In this paper, we propose a novel approach on how to systematically refactor business process models with parallel structures for sequence structures for the first time.  ...  Task C writes data s, and task F reads data s. In the refactored model in Fig. 2 , there is a causal relation between C and F because tasks D and E are refactored into a parallel structure.  ... 
doi:10.1109/tsc.2014.2383391 fatcat:yrw3p2r2gzc47fdxgsqqwo32gq

Unsupervised learning of dependency structure for language modeling

Jianfeng Gao, Hisami Suzuki
2003 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03  
This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords  ...  First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency.  ...  w i , w j , R) is the number of times w i and w j have a dependency relation in a sentence in training data, and C(w i , w j ) is the number of times w i and w j are seen in the same sentence.  ... 
doi:10.3115/1075096.1075162 dblp:conf/acl/GaoS03 fatcat:aypmx3r62vgohdnlajyztk6exu

Designing Functional Dependencies for XML [chapter]

Mong Li Lee, Tok Wang Ling, Wai Lup Low
2002 Lecture Notes in Computer Science  
We develop a model based on F DXML to estimate the amount of data replication in XML data.  ...  With the increasing relevance of the data-centric aspects of XML, it is pertinent to study functional dependencies in the context of XML, which will form the basis for further studies into XML keys and  ...  Functional Dependencies in XML The well-known definition of functional dependencies for the relational data model is : Let r be a relation on scheme R, with X and Y being subsets of attributes in R.  ... 
doi:10.1007/3-540-45876-x_10 fatcat:gpqrznwifjg3dpjjfufgnubjye

Towards a topological view of databases

K.H. Baik
1989 Computers and Mathematics with Applications  
~act--The structure of data constraint which is a major issue to solve database design problems is formally defined as a binary relation over the topology in a finite topological space, we derived deduction  ...  With a formal definition for key related database design problems which are intractable, the Borel representation for keys classifies the category of database design problems.  ...  One of the major shortcomings in the relational data model is that the structure 27 is treated as a single level structure on S without exphcitly recognizing two levels of the structure.  ... 
doi:10.1016/0898-1221(89)90235-6 fatcat:t7tez3uwuzcp5h56fsvb3cbxa4

Database Support for Exploring Scientific Workflow Provenance Graphs [chapter]

Manish Kumar Anand, Shawn Bowers, Bertram Ludäscher
2012 Lecture Notes in Computer Science  
A unique feature of the model is that it can be implemented using standard relational database technology, which has a number of advantages in terms of supporting existing provenance frameworks and efficiency  ...  We present and formalize the operations within the model as a set of relational queries expressed against an underlying provenance schema.  ...  Given a trace relation R, we write R (D) to denote the filtered version of R with respect to the dependencies in D.  ... 
doi:10.1007/978-3-642-31235-9_23 fatcat:t2ktm4hmcvebzltwqdfyzam3c4

Reasoning about Independence in Probabilistic Models of Relational Data [article]

Marc Maier, Katerina Marazopoulou, David Jensen
2014 arXiv   pre-print
We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence.  ...  We extend the theory of d-separation to cases in which data instances are not independent and identically distributed.  ...  This effort is supported by the Intelligence Advanced Research Project Agency (IARPA) via Department of Interior National Business Center Contract number D11PC20152, Air Force Research Lab under agreement  ... 
arXiv:1302.4381v3 fatcat:3vxdbkykcfb3bopxvvqcnthw7i

UML Specification and Relational Database

Liwu Li, Xin Zhao
2003 Journal of Object Technology  
We also present techniques for converting structures of relational dependencies to UML constructs.  ...  The Unified Modeling Language (UML) is a standard language for modeling software and database systems. We discuss how to extend the UML metamodel with elements for modeling relational dependencies.  ...  data model.  ... 
doi:10.5381/jot.2003.2.5.a1 fatcat:rpl2itsv45gwjadtyiahibm2li

Syntactically-Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction

Wenya Wang, Sinno Jialin Pan
2019 Computational Linguistics  
Specifically, the auxiliary task builds structural correspondences across domains by predicting the dependency relation for each path of the dependency tree in the recursive neural network.  ...  In this paper, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning.  ...  For the cross-domain setting, to train the joint model with only labeled data D S = {(x S i , y S i )} n S i=1 in the source domain, we generate another two sets of training data, including D R = {(r j  ... 
doi:10.1162/coli_a_00362 fatcat:topw3vnee5ao7aevhc6sd7axdq

Neural Ranking Models for Temporal Dependency Structure Parsing

Yuchen Zhang, Nianwen Xue
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this  ...  It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure.  ...  Unlike syntactic dependency parsing where each word in a sentence is a node in the dependency structure, in a temporal dependency structure only some of the words in a text are nodes in the structure.  ... 
doi:10.18653/v1/d18-1371 dblp:conf/emnlp/ZhangX18 fatcat:fddmsyfwyjazzhugurddhrua4a

Neural Ranking Models for Temporal Dependency Structure Parsing [article]

Yuchen Zhang, Nianwen Xue
2018 arXiv   pre-print
Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this  ...  It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure.  ...  Unlike syntactic dependency parsing where each word in a sentence is a node in the dependency structure, in a temporal dependency structure only some of the words in a text are nodes in the structure.  ... 
arXiv:1809.00370v1 fatcat:maiqys2reve3lfxn4zxktbhrn4

A Self-Adaptive Process Mining Algorithm Based on Information Entropy to Deal with Uncertain Data

Weimin Li, Yuting Fan, Wei Liu, Minjun Xin, Hao Wang, Qun Jin
2019 IEEE Access  
The recognition of parallel structures contributes to eliminating imbalances when calculating the threshold to deal with the uncertain data.  ...  Experimental results show that the algorithm proposed in this study has a higher degree of behavioral and structural appropriateness, and fitness, for the uncertain log data compared to traditional algorithms  ...  RELATED WORKS The concept of process mining was proposed in 1995 by Professor Cook and Wolf [7] , in which the goal of discovering process models based on the given log data automatically.  ... 
doi:10.1109/access.2019.2939565 fatcat:fqjqojlas5b35izdo43sq6oz6u

Topology and semantic based topic dependency structure discovery

Anping Zhao, Suresh Manandhar, Lei Yu
2018 Filomat  
As an important enabler in achieving the maximum potential of text data analysis, topic relationship dependency structure discovery is employed to effectively support the advanced text data analysis intelligent  ...  The approach is to identify topics of the text data based on the LDA and to discover the graphical semantic structure of the intrinsic association dependency between topics.  ...  In this paper, We focus on jointly modeling the topics from text data and their dependency relationship in the unified model.  ... 
doi:10.2298/fil1805843z fatcat:pxeqwsun5rbzvepeg5m7ksvimi

Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks [article]

Junhyung Kim, Byungyoon Park, Charmgil Hong
2021 arXiv   pre-print
By training a model with multiple kernel sizes, the method is able to learn the dependency relations among labels at multiple scales, while it uses a drastically smaller number of parameters.  ...  Modern multi-label classifiers have been adopting recurrent neural networks (RNNs) as a memory structure to capture and exploit label dependency relations.  ...  More specifically, as the memory structure of recurrent neural networks (RNN) is considered to be useful in capturing and exploiting the dependence relations among labels, several RNNbased models [7,  ... 
arXiv:2107.05941v1 fatcat:mabcqxdnyvfgjcgg3qebx6qtya

A Survey on Statistical Relational Learning [chapter]

Hassan Khosravi, Bahareh Bina
2010 Lecture Notes in Computer Science  
real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither identical nor independent.  ...  The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most  ...  This article is the result of a part of our research under the supervision of Dr Oliver Schulte. We thanks Dr Schulte for his guidance and interesting discussions.  ... 
doi:10.1007/978-3-642-13059-5_25 fatcat:wazustf5cbaovljnfacy67hfma
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