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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Xie, Xike Lu, Hua Chen, Jinchuan Shang, Shuo Department of Computer Science Aalborg University Denmark Key Labs of Data Engineering and Knowledge Engineering MOE Renmin University of China China

ISBN: (纸本)9783642374869

In many real-life applications, spatial objects are associated with multiple non-spatial attributes. For example, a hotel may have price and rating in addition to its geographic location. In traditional spatial databases, spatial objects are often ranked solely based on their distance to a given query location, e.g., in a nearest neighbor search. In another line of research, domination based skyline queries are used to return best objects according to multi-criteria on non-spatial attributes. In this paper, we study how to rank spatial objects with respect to their non-spatial attributes within their spatial neighborhoods. To enable a general ranking, we design a ranking function that inherits the advantages of dominance relationship and integrates them with spatial proximity. Further, we propose an effective index structure, and a branch and bound solution that executes the ranking efficiently via the index. We conduct extensive empirical studies on real and synthetic datasets. The results demonstrate the high efficiency of our proposal compared to straightforward alternatives. © Springer-Verlag 2013.

关键词： Nearest neighbor search

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Effective evaluation measures for subspace clustering of data streams

Effective evaluation measures for subspace clustering of dat...

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17th Pacific-Asia Conference on knowledge Discovery and data Mining, PAKDD 2013

作者： Hassani, Marwan Kim, Yunsu Choi, Seungjin Seidl, Thomas Data Management and Data Exploration Group RWTH Aachen University Germany Department of Computer Science and Engineering Pohang University of Science and Technology Korea Republic of

ISBN: (纸本)9783642403187

Nowadays, most streaming data sources are becoming high-dimensional. Accordingly, subspace stream clustering, which aims at finding evolving clusters within subgroups of dimensions, has gained a significant importance. However, existing subspace clustering evaluation measures are mainly designed for static data, and cannot reflect the quality of the evolving nature of data streams. On the other hand, available stream clustering evaluation measures care only about the errors of the full-space clustering but not the quality of subspace clustering. In this paper we propose, to the first of our knowledge, the first subspace clustering measure that is designed for streaming data, called Sub-CMM : Subspace Cluster Mapping Measure. SubCMM is an effective evaluation measure for stream subspace clustering that is able to handle errors caused by emerging, moving, or splitting subspace clusters. Additionally, we propose a novel method for using available offline subspace clustering measures for data streams within the Subspace MOA framework. © Springer-Verlag 2013.

关键词： Quality control

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Learning-based interactive retrieval in large-scale multimedia collections

Learning-based interactive retrieval in large-scale multimed...

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9th International Workshop on Adaptive Multimedia Retrieval, AMR 2011

作者： Mohamed, Hisham Von Wyl, Marc Bruno, Eric Marchand-Maillet, Stéphane Viper Group Department of Computer Science University of Geneva Switzerland Data Mining and Knowledge Discovery - Corporate R and D Division Firmenich SA Switzerland

ISBN: (纸本)9783642374241

Indexing web-scale multimedia is only possible by distributing storage and computing efforts. Existing large-scale content-based indexing services mostly do not offer interactive relevance feedback. Here, we detail the construction of our Cross-Modal Search Engine (CMSE) implementing a query-by-example search strategy with relevance feedback and distributed over a cluster of 20 Dual core machines using MPI. We present the performance gain in terms of interactivity (search time) using a part of the Image-Net collection containing more than one million images as base example. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Search engines

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Fast SimRank computation over disk-resident graphs

Fast SimRank computation over disk-resident graphs

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Sheng, Likun Key Lab of Data Engineering and Knowledge Engineering of Ministry of Education And Department of Computer Science Renmin University of China China JiangXi Agricultural University China

ISBN: (纸本)9783642374494

There are many real-world applications based on similarity between objects, such as clustering, similarity query processing, information retrieval and recommendation systems. SimRank is a promising measure of similarity based on random surfers model. However, the computational complexity of SimRank is high and several optimization techniques have been proposed. In the paper optimization issue of Sim-Rank computation in disk-resident graphs is our primary focus. First we suggest a new approach to compute *** we propose optimization techniques that improve the time cost of the new approach from O (kN2 D2) to O (kNL), where k is the number of iteration, N is the number of nodes, L is the number of edges, and D is the average degree of nodes. Meanwhile, a threshold sieving method is presented to reduce storage and computational cost. On this basis, an external algorithm computing SimRank in disk-resident graphs is introduced. In the experiments, our algorithm outperforms its opponent whose computation complexity also is O (kNL). © Springer-Verlag 2013.

关键词： Search engines

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A fast and simple graph kernel for RDF

A fast and simple graph kernel for RDF

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International Workshop on data Mining on Linked data, with Linked data Mining Challenge, DMoLD 2013 - Collocated with the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECMLPKDD 2013

作者： De Vries, Gerben Klaas Dirk De Rooij, Steven System and Network Engineering Group Informatics Institute University of Amsterdam Netherlands Knowledge Representation and Reasoning Group Department of Computer Science VU University Amsterdam Netherlands

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-of-words feature vectors, which increases performance in two of the four experiments.

关键词： Forestry

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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Comparing ensemble learning approaches in genetic programming for classification with unbalanced data

Comparing ensemble learning approaches in genetic programmin...

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Bhowan, Urvesh Johnston, Mark Zhang, Mengjie Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Statistics and Operations Research School of Mathematics Victoria University of Wellington New Zealand School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9781450319645

This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble.

关键词： Multiobjective optimization

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Semantic interpretation of mobile phone records exploiting background knowledge 12

Semantic interpretation of mobile phone records exploiting b...

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12th International Semantic Web Conference, ISWC 2013

作者： Dashdorj, Zolzaya Serafini, Luciano Semantic and Knowledge Innovation Lab. Telecom Italia Italy Department of Information Engineering and Computer Science University of Trento Italy Data and Knowledge Management Unit Fondazione Bruno Kessler Italy

The increasing availability of massive mobile phone call data records (CDR) has opened new opportunities for analyzing and understanding real-life social phenomena and human dynamics. In order to better interpret this enormous amount of data it is useful to contextualize them with information about the circumstances under which they has been generated. Nowadays, linked open data initiative provide access to a huge amount of geo-time referenced knowledge about territory and events that happen in the territory. These informations can be used to characterize the aforementioned context. The aim of this Ph.D is to investigate on the intercorrelations between CDR, contexts, and human behaviors. The ultimate goal is to build a stochastic model, that can be used to predict semantic (qualitative) behavioral patterns on the basis of CDR traffic and context and identify and explain anomalous situations on the basis of deviations from standard CDR patterns.

关键词： knowledge management

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Mining complex event patterns in computer networks

Mining complex event patterns in computer networks

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1st International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012

作者： Seipel, Dietmar Neubeck, Philipp Köhler, Stefan Atzmueller, Martin University of Würzburg Department of Computer Science Germany Google Germany GmbH Munich Germany Infosim GmbH and Co. KG Würzburg Germany University of Kassel Knowledge and Data Engineering Group Germany

ISBN: (纸本)9783642373817

More and more ubiquitous and mobile computer networks are becoming available, which leads to a massive growth in the amount of traffic and according log messages. Therefore, sophisticated approaches for network management and analysis are necessary for handling and managing networks efficiently. In this paper, we show how to use temporal data mining in a declarative framework for analysing log files for computer networks. From a sequence of network management protocol messages, we derive temporal association rules, which state frequent dependencies between the occuring events. We also present methods for extendable and modular parsing of text messages and their analysis in log files based on Xml. © 2013 Springer-Verlag.

关键词： data mining

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