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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7250卷 285-286页

作者： Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany

ISBN: (纸本)9783642318290

In the previous chapters of this book quite different approaches to create networks based on existing data collections (Part II) have been discussed and diverse methods for network analysis have been proposed (Part III). All these methods provide powerful means in order to obtain different insights into the properties of huge information networks or graphs. However, one disadavantage of these individual approaches is that each approach provides only specific facets of information to the end user. Integrated exploration tools that enable the interactive exploration of huge graphs and data collections using data viusalization, aggregation and mining methods could be much more beneficial for the (interactive) task of finding bisociations. Therefore, we present and discuss in this part of the book methods for interactive exploration that bring these fields together. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： data acquisition

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Topology-based data compression in wireless sensor networks

Topology-based data compression in wireless sensor networks

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13th International Conference on Web-Age Information Management, WAIM 2012

作者： Mo, Shangfeng Chen, Hong Li, Yinglong Key Laboratory of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Hunan University of Science and Technology Xiangtan 411201 China

ISBN: (纸本)9783642322808

In this paper, we address the problem of data Compression which is critical in wireless sensor networks. We proposed a novel Topology-based data Compression (TDC) algorithm for wireless sensor networks. We utilize the topological structure of routing tree to reduce the transmission of message packets. We analyzed the differences and relations between our algorithm and other compression algorithms. Extensive experiments are conducted to evaluate the performance of the proposed TDC approach by using two kinds of data sets: real data set and synthetic data set. The results show that the TDC algorithm substantially outperforms Non-compression algorithm in terms of packets transmitted. © 2012 Springer-Verlag.

关键词： Topology

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Towards a modelization of the elusive concept of wisdom using fuzzy techniques

Towards a modelization of the elusive concept of wisdom usin...

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2012 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2012

作者： Hoppe, Anett Tabacchi, Marco Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany Dipartimento di Matematica Ed Informatica Sezione di Informatica Università degli Studi di Palermo Italy

ISBN: (纸本)9781467323376

After having been a term of reflection in philosophy as well as psychology for ages, the fascination with human wisdom finally reaches the realms of computer science. In comparison to the first philosophical definition attempts, that date back to Aristotle (384BC - 322BC), the efforts in information science during the late 1980s seem quite recent. Nevertheless have there been astonishing new insights provided by cognitive science since those first formal modelizations were designed - findings that strongly suggest a reconsideration of our current formalization of wisdom in the computer science domain. We suggest to establish a new, integrated view on the concept of wisdom, using the insights of cognitive sciences. Furthermore, we discuss possible measures that are able to cover at least fundamental properties of wisdom including its vague aspects. © 2012 IEEE.

关键词： Philosophical aspects

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Efficient approximation of the maximal preference scores by lightweight cubic views

Efficient approximation of the maximal preference scores by ...

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15th International Conference on Extending database Technology, EDBT 2012

作者： Chen, Yueguo Cui, Bin Du, Xiaoyong Tung, Anthony K. H. Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China Department of Computer Science Peking University Beijing China School of Information Renmin University of China Beijing China School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781450307901

Given a multi-features data set, a best preference query (BPQ) computes the maximal preference score (MPS) that the tuples in the data set can achieve with respect to a preference function. BPQs are very useful in applications where users want to efficiently check whether many individual data sets contain tuples that are of interest to them. Although a BPQ can be naïvely answered by issuing a top-1 query and computing the score from the returned tuple, doing so might require to load a larger number of tuples externally. In this paper, we address the problem of efficient processing BPQs by using lightweight cubic (3-dimensional) views. With these in-memory views, the MPSs of BPQs can be efficiently estimated with an error bound guaranteed, by paying only a small number of I/Os. Extensive experimental results over real-life data sets show that our approximate solution can achieve the efficiency of up to three orders of magnitude compared to exact solutions, with certain accuracy guaranteed. © 2012 ACM.

关键词： best preference score materialized views preference query top-k query

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Bisociative music discovery and recommendation

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7250卷 472-483页

作者： Stober, Sebastian Haun, Stefan Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Magdeburg D-39106 Magdeburg Germany

ISBN: (纸本)9783642318290

Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems. Motivated by the concept of bisociations, we propose ways to create an environment where such serendipitous recommendations become more likely. As application domain we focus on music recommendation using MusicGalaxy, an adaptive user-interface for exploring music collections. It leverages a non-linear multi-focus distortion technique that adaptively highlights related music tracks in a projection-based collection visualization depending on the current region of interest. While originally developed to alleviate the impact of inevitable projection errors, it can also adapt according to user-preferences. We discuss how using this technique beyond its original purpose can create distortions of the visualization that facilitate bisociative music discovery. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Visualization

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Correspondence pattern attribute selection for consumption of federated data sources

Correspondence pattern attribute selection for consumption o...

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IEEE Symposium on Network Operations and Management

作者： Brian Walshe Rob Brennan Declan O'Sullivan FAME and Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked data. The classification problem is compounded by the less formal semantics of Linked data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.

关键词： Ontologies Standards Gold Training Semantics Resource description framework Vectors

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Injecting Discrimination and Privacy Awareness Into Pattern Discovery

Injecting Discrimination and Privacy Awareness Into Pattern ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Sara Hajian Anna Monreale Dino Pedreschi Josep Domingo-Ferrer Fosca Giannotti Department of Computer Engineering and Math. Universitat Rovira i Virgili Department of Computer Science Knowledge Discovery and Data Mining Lab ISTI-CNR Knowledge Discovery and Data Mining Lab Pisa

data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.

关键词： data privacy Privacy Additives Context Itemsets data models

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On the integration of graph exploration and data analysis: The creative exploration toolkit

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7250卷 301-312页

作者： Haun, Stefan Gossen, Tatiana Nürnberger, Andreas Kötter, Tobias Thiel, Kilian Berthold, Michael R. Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany Department for Bioinformatics and Information Mining University of Konstanz Germany

ISBN: (纸本)9783642318290

To enable discovery in large, heterogenious information networks a tool is needed that allows exploration in changing graph structures and integrates advanced graph mining methods in an interactive visualization framework. We present the Creative Exploration Toolkit (CET), which consists of a state-of-the-art user interface for graph visualization designed towards explorative tasks and support tools for integration and communication with external data sources and mining tools, especially the data-mining platform KNIME. All parts of the interface can be customized to fit the requirements of special tasks, including the use of node type dependent icons, highlighting of nodes and clusters. Through an evaluation we have shown the applicability of CET for structure-based analysis tasks. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： User interfaces

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Interval valued fuzzy rough classifier and its application on privacy protection

Interval valued fuzzy rough classifier and its application o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Su-Yun Zhao Si Lin Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China College of Science Beijing Forestry University Beijing China

Currently, most works on interval valued problems mainly focus on attribute reduction (i.e., feature selection) by using rough set technologies. However, less research work on classifier building on interval-valued problems has been conducted. It is promising to propose an approach to build classifier for interval-valued problems. In this paper, we propose a classification approach based on interval valued fuzzy rough sets. First, the concept of interval valued fuzzy granules are proposed, which is the crucial notion to build the reduction framework for the interval-valued databases. Second, the idea to keep the critical value invariant before and after reduction is selected. Third, the structure of reduction rule is completely studied by using the discernibility vector approach. After the description of rule inference system, a set of rules covering all the objects can be obtained, which is used as a rule based classifier for future classification. Finally, numerical examples are presented to illustrate feasibility and affectivity of the proposed method in the application of privacy protection.

关键词： Abstracts Diabetes Sonar Iris Accuracy

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Evaluating decisions: Characteristics, evaluation of outcome and serious games

Evaluating decisions: Characteristics, evaluation of outcome...

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14th International Conference on Information Processing and Management of Uncertainty in knowledge-Based Systems, IPMU 2012

作者： Inthorn, Julia Haun, Stefan Hoppe, Anett Nürnberger, Andreas Dick, Michael Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany Department for Ethics and History of Medicine University Medical Center Goettingen Germany Institute for Research and Development of Collaborative Processes School of Applied Psychology University of Applied Sciences and Arts Northwestern Switzerland Switzerland

ISBN: (纸本)9783642317149

The characteristics of decisions and the evaluation of their outcome are highly complex. In this paper, we first give a short analysis of different types of decisions such as long-term and short-term decisions or dilemmas by discussing different scenarios and examples. We identify characteristics of decisions and give a short account of the usually quite complex decision scenarios involved and the high uncertainty of long-term predictions. We introduce serious games as a tool for evaluation of decisions through simulation. By using serious games long-term effects of decisions can be evaluated and known measurement criteria for short-term decision can be transformed to support long-term decisions. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Information management

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