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Searching for authors named Christos Faloutsos – sorted by Relevance.

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  • Spatial Access Methods- problem  
  • by Christos Faloutsos, C. Faloutsos, C. Faloutsos, C. Faloutsos, C. Faloutsos
  • …z-ordering- Detailed outline • What is the problem / S.A.M. • z-ordering – main idea- 3 methods – use w / B-trees; algorithms (range, knn queries – non-point (eg., region) data – analysis; variations…
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  • Indexing and mining streams  
  • by Christos Faloutsos — 2004 — In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data
  • …How can we find patterns in a sequence of sensor measurements (eg., a sequence of temperatures, or water-pollutant measurements)? How can we compress it? What are the major tools for forecasting and outlier detection? The objective of this tutorial is to provide a concise and intuitive overview of t…
  • Cited by 1 (0 self) – Add To MetaCart
  • Indexing Multimedia Databases  
  • by Christos Faloutsos
  • …INTRODUCTION 1.1 Problem definition Multimedia System: A system that can store and retrieve objects/documents with text, voice, images, animation, slides show etc. voice Query by content: ffl Efficient and ffl `complete' (no false dismissals) 2 1.2 Applications ffl education, entertainment …
  • Cited by 2 (0 self) – Add To MetaCart
  • Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks  
  • by Christos Faloutsos
  • …Abstract: The problem of deploying sensors in a large water distribution network is considered, in order to detect the malicious introduction of contaminants. It is shown that a large class of realistic objective functions—such as reduction of detection time and the population protected from consumi…
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  • Scalable modeling of real graphs using Kronecker multiplication  
  • by Christos Faloutsos — 2007 — In 24th ICML
  • …Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum, etc? We propose to use “Kronecker graphs”, which naturally obey all of the above properties, and we present KronFit, a f…
  • Cited by 9 (2 self) – Add To MetaCart
  • and Data Mining Information recovery  
  • by C. Faloutsos, C C. Faloutsos, Christos Faloutsos
  • …■ Qopt- selectivities ■ data warehousing ■ transaction recording systems (details: in tertiary store) ■ statistical/scientific db ■ data integration (partial info from many sources) 2. SURVEY ■ Qopt- selectivity estimation – uniformity assumption [Selinger et al, 79] –... shown pessimistic [Christod…
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  • On Power-Law Relationships of the Internet Topology  
  • by Michalis Faloutsos, Petros Faloutsos, Christos Faloutsos — 1999 — In SIGCOMM
  • …Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period. We show that our power-l…
  • Cited by 798 (35 self) – Add To MetaCart
  • Power-Laws and the AS-level Internet Topology  
  • by Georgos Siganos, Michalis Faloutsos, Petros Faloutsos, Christos Faloutsos — 2003 — IEEE/ACM Transactions on Networking
  • …In this paper, we study and characterize the topology of the Internet at the Autonomous System level. First, we show that the topology can be described efficiently with power-laws. The elegance and simplicity of the powerlaws provide a novel perspective into the seemingly uncontrolled Internet struc…
  • Cited by 54 (8 self) – Add To MetaCart
  • Association Rules  
  • by Christos Faloutsos, Pods Christos Faloutsos, Ibrahim Kamel, C. Faloutsos, C. Faloutsos, C. Faloutsos
  • …Montgomery county: •Q1: how many d.a. for an R-tree? •Q2: distribution? •not uniform…
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  • The Evolution of the Internet:Topology and Routing  
  • by Georgos Siganos, Michalis Faloutsos, Christos Faloutsos
  • …Abstract—In this paper, we study the evolution of the Internet topology over the last three years. We study the evolution at three different levels: a) each node individually, b) the network as a whole, c) the path level. First, we find that the degrees of the nodes increase in a “rich get richer” f…
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