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ONTOLOGY-BASED data summarization ENGINE: A DESIGN METHODOLOGY

JOURNAL OF COMPUTER INFORMATION SYSTEMS 2012年第1期53卷 48-56页

作者： Wang, Hai Wang, Shouhong St Marys Univ Halifax NS B3H 2W3 Canada Univ Massachusetts Dartmouth Dartmouth MA 02747 USA

data summarization has recently received considerable attention in the information technology community. This design research paper discusses the methodology and principles of the design of data summarization engine. Based on an initial analysis of requirement representations in data summarization, it proposes a generic organization of ontology for data summarization engine. Furthermore, this paper proposes a design framework for data summarization engine that uses the ontology as the fundamental structure of the interactive user-computer interface. A prototype project of ontology-based data summarization engine for summarizing the data incompleteness demonstrates the effectiveness of the design of ontology-based data summarization engine.

关键词： Design research ontology data summarization data summarization engine database systems analysis and design business intelligence data mining incomplete data

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Effective data summarization for hierarchical clustering in large datasets

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KNOWLEDGE AND INFORMATION SYSTEMS 2015年第1期42卷 1-20页

作者： Patra, Bidyut Kr. Nandi, Sukumar Natl Inst Technol Rourkela Rourkela 769008 Orissa India VTT Tech Res Ctr Finland Espoo 02044 Finland Indian Inst Technol Guwahati Gauhati 789039 Assam India

Cluster analysis in a large dataset is an interesting challenge in many fields of Science and Engineering. One important clustering approach is hierarchical clustering, which outputs hierarchical (nested) structures of a given dataset. The single-link is a distance-based hierarchical clustering method, which can find non-convex (arbitrary)-shaped clusters in a dataset. However, this method cannot be used for clustering large dataset as this method either keeps entire dataset in main memory or scans dataset multiple times from secondary memory of the machine. Both of them are potentially severe problems for cluster analysis in large datasets. One remedy for both problems is to create a summary of a given dataset efficiently, and the summary is subsequently used to speed up clustering methods in large datasets. In this paper, we propose a summarization scheme termed data sphere (ds) to speed up single-link clustering method in large datasets. The ds utilizes sequential leaders clustering method to collect important statistics of a given dataset. The single-link method is modified to work with ds. Modified clustering method is termed as summarized single-link (SSL). The SSL method is considerably faster than the single-link method applied directly to the dataset, and clustering results produced by SSL method are close to the clustering results produced by single-link method. The SSL method outperforms single-link using data bubble (summarization scheme) both in terms of clustering accuracy and computation time. To speed up proposed summarization scheme, a technique is introduced to reduce a large number of distance computations in leaders method. Experimental studies demonstrate effectiveness of the proposed summarization scheme for large datasets.

关键词： data summarization Single-link Large datasets Leaders method

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Approximation Algorithms for Submodular data summarization with a Knapsack Constraint

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PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS 2021年第1期5卷 1–31页

作者： Han, Kai Cui, Shuang Zhu, Tianshuai Zhang, Enpei Wu, Benwei Yin, Zhizhuo Xu, Tong Tang, Shaojie Huang, He Univ Sci & Technol China SuZhou Res Inst Sch Comp Sci & Technol Hefei Anhui Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Univ Texas Dallas Naveen Jindal Sch Management Dallas TX USA Soochow Univ Sch Comp Sci & Technol Suzhou Jiangsu Peoples R China

data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a randomized algorithm with approximation ratio 4, and show that both of them can be accelerated to achieve nearly linear running time at the cost of weakening the approximation ratio by an additive factor of is an element of. We then consider a more restrictive setting without full access to the whole dataset, and propose streaming algorithms with approximation ratios of 8 + is an element of and 6 + is an element of that make one pass and two passes over the data stream, respectively. As a by-product, we also propose a two-pass streaming algorithm with an approximation ratio of 2 + is an element of when the considered submodular function is monotone. To the best of our knowledge, our algorithms achieve the best performance bounds compared to the state-of-the-art approximation algorithms with efficient implementation for the same problem. Finally, we evaluate our algorithms in two concrete submodular data summarization applications for revenue maximization in social networks and image summarization, and the empirical results show that our algorithms outperform the existing ones in terms of both effectiveness and efficiency.

关键词： machine learning data summarization submodular function maximization

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NNK-Means: data summarization using dictionary learning with non-negative kernel regression 30

NNK-Means: Data summarization using dictionary learning with...

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30th European Signal Processing Conference (EUSIPCO)

作者： Shekkizhar, Sarath Ortega, Antonio Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9789082797091

An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this is done without analyzing the dataset structure. As task complexity, data size, and parameters all increase to millions or even billions, data summarization is becoming a major challenge. In this work, we investigate data summarization via dictionary learning (DL), leveraging the properties of recently introduced non-negative kernel regression (NNK) graphs. Our proposed NNK-Means, unlike previous DL techniques, such as kSVD, learns geometric dictionaries with atoms that are representative of the input data space. Experiments show that summarization using NNK-Means can provide better class separation compared to linear and kernel versions of kMeans and kSVD. Moreover, NNK-Means is scalable, with runtime complexity similar to that of kMeans.

关键词： data summarization dataset analysis dictionary learning neighborhood methods kernel methods

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Dimensionality Reduction in data summarization Approach to Learning Relational data

Dimensionality Reduction in Data Summarization Approach to L...

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5th Asian Conference on Intelligent Information and database Systems (ACIIDS)

作者： Kheau, Chung Seng Alfred, Rayner Keng, Lau Hui Univ Malaysia Sabah Jalan UMS Sch Engn & Informat Technol Kota Kinabalu 88400 Sabah Malaysia

ISBN: (纸本)9783642365461

Due to the growing amount of digital data stored in relational databases, more new approaches are required to learn relational data. The DARA algorithm is designed to summarize data and it is one of the approaches introduced in relational data mining in order to handle data with one-to-many relations. The DARA algorithm transforms data stored in relational databases into a vector space representation by applying the information retrieval theory. Based on the experimental results, the DARA algorithm is proven to be very effective in learning relational data. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. This paper investigates the effects of discretizing the magnitude of terms computed and applying a feature selection process that reduces the cardinalities of attributes of the relational datasets on the predictive accuracy of the overall classification task. This involves the task of finding the best set of relevant features used to summarize the data, in which the feature selection processed is performed based on the magnitude of terms computed earlier. Based on the results obtained, it shows that the predictive accuracy of the classification task can be improved by improving the quality of the summarized data. The quality of the summarized data can be enhanced by appropriately discretizing the magnitude of terms computed earlier and also appropriately selecting only a certain percentage of the attributes.

关键词： Relational data Mining data summarization Clustering Dimensionality Reduction Discretization Numbers Feature Selection

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On data summarization for Machine Learning in Multi-Organization Federations 5

On Data Summarization for Machine Learning in Multi-Organiza...

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5th IEEE International Conference on Smart Computing (SMARTCOMP)

作者： Ko, Bong Jun Wang, Shiqiang He, Ting Conway-Jones, Dave IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Penn State Univ University Pk PA 16802 USA IBM United Kingdom Ltd Emerging Technol Hursley Pk Winchester Hants England

ISBN: (纸本)9781728116891

Machine learning is a promising technology for many modern applications. To train an effective machine learning model, a large amount of data is required. However, data may be created in different organizations and sharing data across organizational boundaries is difficult due to privacy concerns and communication bandwidth limitations. data summarization is a technique for reducing the amount of data that needs to be shared, while preserving characteristics in the data that are useful for training machine learning models. In this paper, we present an overview of data summarization techniques, which can be useful for machine learning across organizational boundaries. We also discuss some possible applications related to these data summarization techniques and challenges for future research.

关键词： data summarization federation machine learning

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data summarization via bilevel optimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3549-3641页

作者： Zalán Borsos Mojmír Mutný Marco Tagliasacchi Andreas Krause Department of Computer Science ETH Zurich Google Research

The increasing availability of massive data sets poses various challenges for machine learning. Prominent among these is learning models under hardware or human resource constraints. In such resource-constrained settings, a simple yet powerful approach is operating on small subsets of the data. Coresets are weighted subsets of the data that provide approximation guarantees for the optimization objective. However, existing coreset constructions are highly model-specific and are limited to simple models such as linear regression, logistic regression, and k-means. In this work, we propose a generic coreset construction framework that formulates the coreset selection as a cardinality-constrained bilevel optimization problem. In contrast to existing approaches, our framework does not require model-specific adaptations and applies to any twice differentiable model, including neural networks. We show the effectiveness of our framework for a wide range of models in various settings, including training non-convex models online and batch active learning.

关键词： data summarization coresets bilevel optimization continual learning streaming batch active learning

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Efficient and Self-Balanced ROLLUP Aggregates for Large-Scale data summarization 4

Efficient and Self-Balanced ROLLUP Aggregates for Large-Scal...

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IEEE International Congress on Big data

作者： Duy-Hung Phan Quang-Nhat Hoang-Xuan Dell'Amico, Matteo Michiardi, Pietro EURECOM Sophia Antipolis Alpes Maritimes France

ISBN: (纸本)9781467372787

data summarization queries that compute aggregates by grouping datasets across several dimensions are essential to help users make sense of very large datasets. In this work, we focus on ROLLUP, an important operator that has been recently added to the Hadoop MapReduce ecosystem. However, its current implementation suffers from very large communication costs, leading to inefficient executions. We thus proceed with the design of a new ROLLUP operator for high-level languages. Our operator is self-optimizing, which means that it automatically performs load-balancing and determines a suitable operating point to achieve the highest performance. We have implemented our ROLLUP operator for Apache Pig, a popular high-level language in the Hadoop ecosystem. Our experimental results, obtained on both synthetic and real datasets, indicate that our new operator outperforms the current ROLLUP implementation in Pig by at least 50%.

关键词： MapReduce ROLLUP data summarization optimization high level language(hll) self-balancing Operators Workstations language level Load balancing dataset Pigs Set Theory aggregates Aggregates Aggregates

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Clustering based Semantic data summarization Technique: A New Approach 9

Clustering based Semantic Data Summarization Technique: A Ne...

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9th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Ahmed, Mohiuddin Mahmood, Abdun Naser Univ New S Wales Sch Informat Technol & Engn Canberra ACT Australia

ISBN: (纸本)9781479943159

to advancement of computing and proliferation of data repositories, efficient data mining techniques are required to extract meaningful information. summarization is such an important data analysis technique which can be broadly classified into two categories as semantic and syntactic methods. Syntactic methods consider a dataset as a sequence of bytes whereas semantic methods convert large dataset into a much smaller one yet maintaining low information loss. Clustering algorithms are widely used for semantic summarization such as basic k-means. Existing clustering based summarization techniques assume that a summary is represented using the cluster centroids. However, the centroids might not represent the actual data points in summary. In addition, many clustering algorithms, such as the most popular k-means algorithm requires the number of clusters as an input, which is not available for unsupervised summarization of unlabeled data. To address these issues, we propose a clustering based semantic sumtnarization using a combination of x-means and k-nzedoid clustering algorithms. Our experimental analysis shows that, the proposed algorithm outperforms k-means based summarization techniques.

关键词： data summarization Clustering

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Walk-Based Diversification for data summarization

Walk-Based Diversification for Data Summarization

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International Conference on Information Technology and Systems (ICITS)

作者： Oliva, Samuel Zanferdini Felipe, Joaquim Cezar Univ Sao Paulo Bioengn Interunit Grad Program Sao Carlos Brazil Univ Sao Paulo Dept Comp & Math Ribeirao Preto Brazil

ISBN: (纸本)9783030406905;9783030406899

Due to the large amount of data stored in current information systems, new strategies are required in order to extract useful information from databases. Hereupon, data summarization is an interesting process that allows reducing a large database maintaining just the relevant parts of the whole collection. In this study, we propose a new approach for data summarization based on a recently proposed tourist walk diversification method. This approach allows setting two ways of selecting elements considering density and hyper volume of each class. In order to evaluate the proposed approach, we compared it with two known methods of the literature considering one real world dataset and one artificial dataset. The artificial dataset was created considering different data distribution aspects. The conducted experiments outcomes demonstrate that our proposed data summarization approach is a promising alternative for addressing the problem of selecting elements from large databases considering different aspects of distribution.

关键词： data summarization Tourist walk Query diversification

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