Dynamic Data Structures: Orthogonal Range Queries and Update Efficiency Konstantinos Athanasiou Tsakalidis
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Arxiv:Submit/0396732
Dynamic 3-sided Planar Range Queries with Expected Doubly Logarithmic Time⋆ Gerth Stølting Brodal1, Alexis C. Kaporis2, Apostolos N. Papadopoulos4, Spyros Sioutas3, Konstantinos Tsakalidis1, Kostas Tsichlas4 1 MADALGO⋆⋆, Department of Computer Science, Aarhus University, Denmark gerth,tsakalid @madalgo.au.dk 2 { } Computer Engineering and Informatics Department, University of Patras, Greece [email protected] 3 Department of Informatics, Ionian University, Corfu, Greece [email protected] 4 Department of Informatics, Aristotle University of Thessaloniki, Greece [email protected] Abstract. This work studies the problem of 2-dimensional searching for the 3-sided range query of the form [a,b] ( , c] in both main × −∞ and external memory, by considering a variety of input distributions. We present three sets of solutions each of which examines the 3-sided problem in both RAM and I/O model respectively. The presented data structures are deterministic and the expectation is with respect to the input distribution: (1) Under continuous µ-random distributions of the x and y coordinates, we present a dynamic linear main memory solution, which answers 3- sided queries in O(log n + t) worst case time and scales with O(log log n) expected with high probability update time, where n is the current num- ber of stored points and t is the size of the query output. We external- ize this solution, gaining O(logB n + t/B) worst case and O(logB logn) amortized expected with high probability I/Os for query and update operations respectively, where B is the disk block size. (2)Then, we assume that the inserted points have their x-coordinates drawn from a class of smooth distributions, whereas the y-coordinates are arbitrarily distributed. -
Efficient Algorithms for Handling Molecular Weighted Sequences
EFFICIENT ALGORITHMS FOR HANDLING MOLECULAR WEIGHTED SEQUENCES Costas S. Iliopoulos‚1 Christos Makris‚2‚3 Yannis Panagis‚2‚3 Katerina Perdikuri‚2‚3 Evangelos Theodoridis‚2‚3 and Athanasios Tsakalidis‚2‚3 1 Department of Computer Science King’s College London‚ Strand‚ London WC2R2LS England [email protected] 2 Department of Computer Engineering and Informatics University of Patras‚ 26500 Patras Greece {makri‚ panagis‚ perdikur‚ theodori}@ceid.upatras.gr 3Research Academic Computer Technology Institute 61 Riga Feraiou Str.‚ 26221 Patras Greece [email protected] Abstract In this paper we introduce the Weighted Suffix Tree‚ an efficient data struc- ture for computing string regularities in weighted sequences of molecular data. Molecular Weighted Sequences can model important biological processes such as the DNA Assembly Process or the DNA-Protein Binding Process. Thus pattern matching or identification of repeated patterns‚ in biological weighted sequences is a very important procedure in the translation of gene expression and regulation. We present time and space efficient algorithms for constructing the weighted suffix tree and some applications of the proposed data structure to problems taken from the Molecular Biology area such as pattern matching‚ re- peats discovery‚ discovery of the longest common subsequence of two weighted sequences and computation of covers. Keywords: Molecular Weighted Sequences‚ Suffix Tree‚ Pattern Matching‚ Identifications of repetitions‚ Covers. 1 Introduction Molecular Weighted Sequences appear in various applications of Computational Molecular Biology. A molecular weighted sequence is a molecular sequence (either a sequence of nucleotides or aminoacids)‚ where each character in every position is as- 266 signed a certain weight. This weight could model either the probability of appearance of a character or the stability that the character contributes in a molecular complex. -
Exponential Structures for Efficient Cache-Oblivious Algorithms
Exponential Structures for Efficient Cache-Oblivious Algorithms Michael A. Bender1, Richard Cole2, and Rajeev Raman3 1 Computer Science Department, SUNY Stony Brook, Stony Brook, NY 11794, USA. [email protected]. 2 Computer Science Department, Courant Institute, New York University, New York, NY 10012, USA. [email protected]. 3 Department of Mathematics and Computer Science, University of Leicester, Leicester LE1 7RH, UK. [email protected] Abstract. We present cache-oblivious data structures based upon exponential structures. These data structures perform well on a hierarchical memory but do not depend on any parameters of the hierarchy, including the block sizes and number of blocks at each level. The problems we consider are searching, partial persistence and planar point location. On a hierarchical memory where data is transferred in blocks of size B, some of the results we achieve are: – We give a linear-space data structure for dynamic searching that supports searches and updates in optimal O(logB N) worst-case I/Os, eliminating amortization from the result of Bender, Demaine, and Farach-Colton (FOCS ’00). We also consider finger searches and updates and batched searches. – We support partially-persistent operations on an ordered set, namely, we allow searches in any previous version of the set and updates to the latest version of the set (an update creates a new version of the set). All operations take an optimal O(logB (m+N)) amortized I/Os, where N is the size of the version being searched/updated, and m is the number of versions. – We solve the planar point location problem in linear space, taking optimal O(logB N) I/Os for point location queries, where N is the number of line segments specifying the partition of the plane. -
Lecture Notes in Computer Science 7451 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen
Lecture Notes in Computer Science 7451 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Christian Böhm Sami Khuri Lenka Lhotská M. Elena Renda (Eds.) InformationTechnology in Bio- and Medical Informatics Third International Conference, ITBAM 2012 Vienna, Austria, September 4-5, 2012 Proceedings 13 Volume Editors Christian Böhm Ludwig-Maximilians-University, Department of Computer Science Oettingenstraße 67, 80538 München, Germany E-mail: [email protected]fi.lmu.de Sami Khuri San José State University, Department of Computer Science One Washington Square, San José, CA 95192-0249, USA E-mail: [email protected] Lenka Lhotská Czech Technical University, Faculty of -
CCIS Springer Series
Communications in Computer and Information Science 384 Editorial Board Simone Diniz Junqueira Barbosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil Phoebe Chen La Trobe University, Melbourne, Australia Alfredo Cuzzocrea ICAR-CNR and University of Calabria, Italy Xiaoyong Du Renmin University of China, Beijing, China Joaquim Filipe Polytechnic Institute of Setúbal, Portugal Orhun Kara TÜBITAK˙ BILGEM˙ and Middle East Technical University, Turkey Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Russia Krishna M. Sivalingam Indian Institute of Technology Madras, India Dominik Sle´ ˛zak University of Warsaw and Infobright, Poland Takashi Washio Osaka University, Japan Xiaokang Yang Shanghai Jiao Tong University, China Lazaros Iliadis Harris Papadopoulos Chrisina Jayne (Eds.) Engineering Applications of Neural Networks 14th International Conference, EANN 2013 Halkidiki, Greece, September 13-16, 2013 Proceedings, Part II 13 Volume Editors Lazaros Iliadis Democritus University of Thrace, Orestiada, Greece E-mail: [email protected] Harris Papadopoulos Frederick University of Cyprus, Nicosia, Cyprus E-mail: [email protected] Chrisina Jayne Coventry University, UK E-mail: [email protected] ISSN 1865-0929 e-ISSN 1865-0937 ISBN 978-3-642-41015-4 e-ISBN 978-3-642-41016-1 DOI 10.1007/978-3-642-41016-1 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: Applied for CR Subject Classification (1998): I.2.6, I.5.1, H.2.8, J.2, J.1, J.3, F.1.1, I.5, I.2, C.2 © Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. -
Editorial Alexander B. Sideridis Athanasios Tsakalidis Constantina
Int. J. Electronic Democracy, Vol. 1, No. 2, 2009 119 Editorial Alexander B. Sideridis Informatics Laboratory, Agricultural University of Athens, 75 Iera Odos, 118 55 Athens, Greece E-mail: [email protected] Athanasios Tsakalidis Department of Computer Engineering and Informatics, University of Patras, 26500 Patras, Greece E-mail: [email protected] Constantina Costopoulou Informatics Laboratory, Agricultural University of Athens, 75 Iera Odos, 118 55 Athens, Greece E-mail: [email protected] Andreja Pucihar Faculty of Organizational Sciences, University of Maribor, Kidriceva cesta 55a, 4000 Kranj, Slovenia E-mail: [email protected] Vasilis Zorkadis Hellenic Data Protection Authority, Kifissias 1-3, 115 23 Athens, Greece E-mail: [email protected] Biographical notes: Alexander B. Sideridis is Professor and Head of the Informatics Laboratory of the Agricultural University of Athens. He earned his first degree at the University of Athens and his MSc and PhD from Brunel University. He has been the project leader of more than 30 successful national and international projects and has published more than 180 scientific papers in management and decision support systems, computer networking related to local and central administration activities, advanced computational numerical modelling, informatics and impact of computers in society and agricultural informatics. Copyright © 2009 Inderscience Enterprises Ltd. 120 A.B. Sideridis et al. Athanasios Tsakalidis is a Professor in the Department of Computer Engineering and Informatics of the University of Patras since 1993. He completed his Diploma in Mathematics in 1973 (University of Thessaloniki), his studies and his PhD in Informatics in 1980 and 1983 respectively (University of Saarland, Germany). From 1983 to 1989, he worked as a researcher at the University of Saarland. -
Section Handout
CS106B Handout 37 Autumn 2012 November 26th, 2012 Section Handout Discussion Problem 1: Quadtrees A quadtree is a rooted tree structure where each internal node has precisely four children. Every node in the tree represents a square, and if a node has children, each encodes one of that square’s four quadrants. Quadtrees have many applications in computer graphics, because they can be used as in- memory models of images. That they can be used as in-memory versions of black and white images is easily demonstrated via the following (borrowed from Wikipedia.org): The 8 by 8 pixel image on the left is modeled by the quadtree on the right. Note that all leaf nodes are either black or white, and all internal nodes are shaded gray. The internal nodes are gray to reflect the fact that they contain both black and white pixels. When the pixels covered by a particular node are all the same color, the color is stored in the form of a Boolean and all four children are set to NULL. Otherwise, the node’s sub-region is recursively subdivided into four sub-quadrants, each represented by one of four children. Given a Grid<bool> representation of a black and white image, implement the gridToQuadtree function, which reads the image data, constructs the corresponding quadtree, and returns its root. Frame your implementation around the following data structure: struct quadtree { int lowx, highx; // smallest and largest x value covered by node int lowy, highy; // smallest and largest y value covered by node bool isBlack; // entirely black? true. -
Graph Community Discovery Algorithms in Neo4j with a Regularization-Based Evaluation Metric
Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric Andreas Kanavos, Georgios Drakopoulos and Athanasios Tsakalidis Computer Engineering and Informatics Department, University of Patras, Achaia 26504, Greece Keywords: CNM Algorithm, Community Discovery, Graph Databases, Graph Mining, Graph Signal Processing, Louvain Algorithm, Newman-Girvan Algorithm, Neo4j, Regularization, Walktrap Algorithm. Abstract: Community discovery is central to social network analysis as it provides a natural way for decomposing a social graph to smaller ones based on the interactions among individuals. Communities do not need to be disjoint and often exhibit recursive structure. The latter has been established as a distinctive characteristic of large social graphs, indicating a modularity in the way humans build societies. This paper presents the im- plementation of four established community discovery algorithms in the form of Neo4j higher order analytics with the Twitter4j Java API and their application to two real Twitter graphs with diverse structural properties. In order to evaluate the results obtained from each algorithm a regularization-like metric, balancing the global and local graph self-similarity akin to the way it is done in signal processing, is proposed. 1 INTRODUCTION los et al., 2015b) (Drakopoulos et al., 2016). To this end, a coherence metric balancing global and local Twitter is currently the most popular microblogging self-similarity properties with a rationale similar to platform and the stage for ongoing political, finan- the signal processing regularization criterion 2 2 cial, and cultural conversations with a vast amount K = x As + µ0 Bs , µ0 > 0 (1) of tweets being posted on a daily basis. Decom- k − k k k posing a Twitter social graph to communities yields which given a data vector x, possibly with noise and a deeper insight to these seemingly chaotic interac- outliers, computes a smoother version s thereof by tions. -
Detecting Perturbed Subpathways Towards Mouse Lung Regeneration Following H1N1 Influenza Infection
computation Article Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection Aristidis G. Vrahatis 1,*, Konstantina Dimitrakopoulou 2, Andreas Kanavos 1, Spyros Sioutas 3 and Athanasios Tsakalidis 1 1 Department of Computer Engineering and Informatics, University of Patras, Patras 26500, Greece; [email protected] (A.K.); [email protected] (A.T.) 2 Centre for Cancer Biomarkers CCBIO and Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5020, Norway; [email protected] 3 Department of Informatics, Ionian University Corfu, Corfu 49100, Greece; [email protected] * Correspondence: [email protected]; Tel.: +30-694-767-7069 Academic Editor: Demos T. Tsahalis Received: 31 December 2016; Accepted: 29 March 2017; Published: 3 April 2017 Abstract: It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this orientation, early pathway-based approaches coupled expression data with whole pathway interaction topologies but it was the recent approaches that zoomed into subpathways (local areas of the entire biological pathway) that provided more targeted and context-specific candidate disease biomarkers. Here, we explore the application potential of PerSubs, a graph-based algorithm which identifies differentially activated disease-specific subpathways. PerSubs is applicable both for microarray and RNA-Seq data and utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as reference for biological pathways. PerSubs operates in two stages: first, identifies differentially expressed genes (or uses any list of disease-related genes) and in second stage, treating each gene of the list as start point, it scans the pathway topology around to build meaningful subpathway topologies. -
Suggested Final Project Topics
CS166 Handout 10 Spring 2019 April 25, 2019 Suggested Final Project Topics Here is a list of data structures and families of data structures we think you might find interesting topics for a final project. You're by no means limited to what's contained here; if you have another data structure you'd like to explore, feel free to do so! If You Liked Range Minimum Queries, Check Out… Range Semigroup Queries In the range minimum query problem, we wanted to preprocess an array so that we could quickly find the minimum element in that range. Imagine that instead of computing the minimum of the value in the range, we instead want to compute A[i] ★ A[i+1] ★ … ★ A[j] for some associative op- eration ★. If we know nothing about ★ other than the fact that it's associative, how would we go about solving this problem efficiently? Turns out there are some very clever solutions whose run- times involve the magical Ackermann inverse function. Why they're worth studying: If you really enjoyed the RMQ coverage from earlier in the quarter, this might be a great way to look back at those topics from a different perspective. You'll get a much more nuanced understanding of why our solutions work so quickly and how to adapt those tech- niques into novel settings. Lowest Common Ancestor and Level Ancestor Queries Range minimum queries can be used to solve the lowest common ancestors problem: given a tree, preprocess the tree so that queries of the form “what node in the tree is as deep as possible and has nodes u and v as descendants?” LCA queries have a ton of applications in suffix trees and other al- gorithmic domains, and there’s a beautiful connection between LCA and RMQ that gave rise to the first ⟨O(n), O(1)⟩ solution to both LCA and RMQ. -
Storage Efficient Trajectory Clustering and K-NN for Robust Privacy
algorithms Article Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases Elias Dritsas *, Andreas Kanavos, Maria Trigka, Spyros Sioutas and Athanasios Tsakalidis Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece; [email protected] (A.K.); [email protected] (M.T.); [email protected] (S.S.); [email protected] (A.T.) * Correspondence: [email protected]; Tel.: +30-2610-996959 Received: 30 October 2019; Accepted: 7 December 2019; Published: 11 December 2019 Abstract: The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1. To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. -
Agli Tk Tm Hlh a Glimpse at Kurt Mehlhorn
Honorary Doctorate Ceremony 8 November 2017, University of Patras, Greece A Glimpse at KKturt MhlhMehlhorn by Christos Zaroliagis Education & Career Date/Period Education & employment 29.08 .1949 Born in Ingolstadt (Bavaria, Germany) 1968 – 1971 Studies in Computer Science & Math at TU Munich 1971 – 1974 PhD studies in Computer Science, Cornell University, USA PhD Thesis: “Polynomial and Abstract Subrecursive Classes” Advisor Prof. Bob Constable 1971 – 1974 Studienstiftung des Deutschen Volkes 1973 – 1974 Cornell University Fellowship 1974 – 1975 Research Associate at the Department of Computer Science, Universität des Saarlandes, Germany 1975 –today Professor of Computer Science, Universität des Saarlandes, Germany 1990 – today Director of the Max‐Planck‐Institute for Informatics, Saarbruecken, D 1995 –today Co‐founder of Algorithmic Solutions GmbH Patras 08.11.2017 2 Numbers Publications 400 Citations ≥ 18.870 Books 10 h‐index 69 Chapters in Books/Volumes 4 g‐index 228 Volume Editor 12 Journals ≥ 153 PhDs awarded 86 Conferences ≥ 187 Archived repositories ≥ 30 Top 12 world‐nurtures in CS research (2004) Awards/Distinctions 23 Editor in Journals 13 Patras 08.11.2017 3 Major Awards & Distinctions 1986: Leibniz‐Award, Deutsche Forschungsgemeinschaft 1989: Humboldt‐Award 1994: Karl‐Heinz‐Beckurts‐Award 1995: Konrad‐Zuse‐Award 1999: ACM Fellow 2004: Member of Leopoldina, Nationale Akademie der Wissenschaften 2010: EATCS Award 2016: EATCS Fellow 2011: ACM Paris Kanellakis Theory and Practice Award 2014: Erasmus Medal, Academia Europaea 2014: