Professor Ismail Toroslu gave a lecture on "Web Usage Mining and Using Ontology for Capturing Web Usage Semantic" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
This document discusses compressive spectral image sensing and optimization. It introduces compressive spectral imaging (CASSI) which uses coded apertures to sense a datacube with only N^2 measurements rather than the traditional N x N x L measurements. Coded apertures can be optimized for sensing and reconstruction performance as well as spectral selectivity and image classification. New families of coded apertures include boolean, spectrally selective, super-resolution, and colored apertures.
Professor Maria Petrou gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
Ahmed K. Elmagarmid (IEEE Fellow and ACM Distinguished Scientist) gave a lecture on Data Quality: Not Your Typical Database Problem in the Distinguished Lecturer Series - Leon The Mathematician.
Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.
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Georgios Giannakis, Professor and ADC Chair in Wireless Telecommunications, University of Minnesota, Department of Electrical & Computer Engineering (IEEE/EURASIP Fellow, IEEE SPS DL), Sparsity Control for Robustness and Social Data Analysis
This document discusses compressive spectral image sensing and optimization. It introduces compressive spectral imaging (CASSI) which uses coded apertures to sense a datacube with only N^2 measurements rather than the traditional N x N x L measurements. Coded apertures can be optimized for sensing and reconstruction performance as well as spectral selectivity and image classification. New families of coded apertures include boolean, spectrally selective, super-resolution, and colored apertures.
Professor Maria Petrou gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
Ahmed K. Elmagarmid (IEEE Fellow and ACM Distinguished Scientist) gave a lecture on Data Quality: Not Your Typical Database Problem in the Distinguished Lecturer Series - Leon The Mathematician.
Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.
The document discusses the IEEE Signal Processing Society and the Greek signal processing community. It provides a brief history of signal processing and its influences from other fields. It notes the ubiquity of signals and signal processing. It then summarizes the current state and challenges facing the IEEE Signal Processing Society. It provides details on the local Greek SPS chapter, including its size, activities, and plans for coordinating with the broader Greek signal processing community. These plans include making the Greek SP Jam a regular event and establishing workshops, summer schools, lectures, decentralized events, and awards.
Georgios Giannakis, Professor and ADC Chair in Wireless Telecommunications, University of Minnesota, Department of Electrical & Computer Engineering (IEEE/EURASIP Fellow, IEEE SPS DL), Sparsity Control for Robustness and Social Data Analysis
This document summarizes a talk on influence propagation in large graphs. It discusses theorems and algorithms related to modeling the spread of information, viruses, and diseases over networks. The document begins by motivating the importance of understanding dynamical processes over networks through examples related to epidemiology, viral marketing, cybersecurity, and more. It then outlines threshold results for epidemic models on static graphs that depend on the largest eigenvalue of the graph's adjacency matrix and properties of the propagation model. The talk discusses proofs of these results and also covers extensions to dynamic graphs and competing viruses. Finally, it discusses algorithms for determining who to immunize to control outbreaks.
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
Professor Ivica Crnkovic gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Professor Xin Yao gave a lecture on "Co-evolution, games, and social behaviors" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/G7MdD
This document discusses using model checking techniques for safety critical systems at NASA. It begins by introducing model checking and how it can be used to verify that a program or model satisfies a given property. It then discusses challenges like the state explosion problem and presents compositional verification as a way to address this by breaking the verification task into checking smaller components. The document provides several examples of applying these techniques to real NASA systems like rovers and spacecraft software.
Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and Description
Professor Dr. Sudip Misra gave a lecture on "Jamming in Wireless Sensor Networks" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/sM0jy
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Aggelos Katsaggelos, Professor and AT&T Chair, Northwestern University, Department of Electrical Engineering & Computer Science (IEEE/ SPIE Fellow, IEEE SPS DL), Sparse and Redundant Representations: Theory and Applications
Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/U5nGq
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
Professor Gonzalo R. Arce gave a lecture on "Compressed sensing in spectral imaging" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/satkf
The document discusses web usage mining, which involves automatically discovering patterns in how users access and interact with web pages on a website by analyzing web server log files. It describes the three main stages of the web usage mining process: data collection and preprocessing, pattern discovery, and pattern analysis. In the preprocessing stage, user access data is cleaned and organized into user sessions. Statistical and machine learning algorithms are then used to find hidden patterns in user behavior. Discovered patterns can be used by applications like recommendation engines. The document provides details on gathering and preprocessing usage data, including identifying unique users and constructing user sessions from server logs. It also discusses applying sequential pattern mining algorithms to discover frequent traversal patterns between pages within user sessions.
The document discusses web usage mining, which involves automatically discovering patterns in how users access and interact with web pages on a website by analyzing web server log files. It describes the three main stages of the web usage mining process: data collection and preprocessing, pattern discovery, and pattern analysis. In the preprocessing stage, user access data is cleaned and organized into user sessions. Statistical and machine learning algorithms are then used to find hidden patterns in user behavior. Discovered patterns can be used by applications like recommendation engines. The document provides details on gathering and preprocessing usage data, including identifying unique users and constructing user sessions from server logs. It also discusses applying sequential pattern mining algorithms to discover frequent traversal patterns between pages within user sessions.
This document presents a method for detecting hotspots (services responsible for suboptimal performance) in a service-oriented architecture. The method reconstructs service call graphs from logged metrics to identify the top-k services whose optimization would most reduce latency or cost. It proposes quantifying each service's impact and using a greedy algorithm to select services in descending order of impact. Evaluation on a large-scale production dataset found the approach more effective than a baseline and produced consistent results over time, helping engineering teams optimize performance.
The magic behind your Lyft ride prices: A case study on machine learning and ...Karthik Murugesan
Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalability—allowing the pricing system to be more adaptable to real-world changes.
This document presents the πRT-calculus, a calculus for modeling mobile real-time processes. It extends the π-calculus with a timeout operator to model real-time aspects. The document covers the syntax and semantics of the π-calculus and πRT-calculus. It also discusses design choices like having a global clock and discrete time. An example of a mobile video streaming system is used to illustrate the πRT-calculus. The document concludes by discussing future work, like developing timed bisimulation and extending to continuous time.
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Acutesoft is a best online center in Hyderabad. We are providing very best online training on Informatica.
Get(100$ off) on INFORMATICA Online Training at AcuteSoft Solutions For more information please contact us.
This document outlines the course content for an Informatica training course. The course covers data warehousing concepts, installing and using Informatica PowerCenter, transformations, mappings, workflows, monitoring, debugging, error handling, performance tuning, and best practices. The course includes over 30 hands-on labs covering topics like basic and advanced transformations, mappings, workflows, debugging, heterogeneous targets, reusable objects, workflow tasks, and a case study project.
This document summarizes a talk on influence propagation in large graphs. It discusses theorems and algorithms related to modeling the spread of information, viruses, and diseases over networks. The document begins by motivating the importance of understanding dynamical processes over networks through examples related to epidemiology, viral marketing, cybersecurity, and more. It then outlines threshold results for epidemic models on static graphs that depend on the largest eigenvalue of the graph's adjacency matrix and properties of the propagation model. The talk discusses proofs of these results and also covers extensions to dynamic graphs and competing viruses. Finally, it discusses algorithms for determining who to immunize to control outbreaks.
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
Professor Ivica Crnkovic gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Professor Xin Yao gave a lecture on "Co-evolution, games, and social behaviors" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/G7MdD
This document discusses using model checking techniques for safety critical systems at NASA. It begins by introducing model checking and how it can be used to verify that a program or model satisfies a given property. It then discusses challenges like the state explosion problem and presents compositional verification as a way to address this by breaking the verification task into checking smaller components. The document provides several examples of applying these techniques to real NASA systems like rovers and spacecraft software.
Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and Description
Professor Dr. Sudip Misra gave a lecture on "Jamming in Wireless Sensor Networks" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/sM0jy
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Aggelos Katsaggelos, Professor and AT&T Chair, Northwestern University, Department of Electrical Engineering & Computer Science (IEEE/ SPIE Fellow, IEEE SPS DL), Sparse and Redundant Representations: Theory and Applications
Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/U5nGq
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
Professor Gonzalo R. Arce gave a lecture on "Compressed sensing in spectral imaging" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/satkf
The document discusses web usage mining, which involves automatically discovering patterns in how users access and interact with web pages on a website by analyzing web server log files. It describes the three main stages of the web usage mining process: data collection and preprocessing, pattern discovery, and pattern analysis. In the preprocessing stage, user access data is cleaned and organized into user sessions. Statistical and machine learning algorithms are then used to find hidden patterns in user behavior. Discovered patterns can be used by applications like recommendation engines. The document provides details on gathering and preprocessing usage data, including identifying unique users and constructing user sessions from server logs. It also discusses applying sequential pattern mining algorithms to discover frequent traversal patterns between pages within user sessions.
The document discusses web usage mining, which involves automatically discovering patterns in how users access and interact with web pages on a website by analyzing web server log files. It describes the three main stages of the web usage mining process: data collection and preprocessing, pattern discovery, and pattern analysis. In the preprocessing stage, user access data is cleaned and organized into user sessions. Statistical and machine learning algorithms are then used to find hidden patterns in user behavior. Discovered patterns can be used by applications like recommendation engines. The document provides details on gathering and preprocessing usage data, including identifying unique users and constructing user sessions from server logs. It also discusses applying sequential pattern mining algorithms to discover frequent traversal patterns between pages within user sessions.
This document presents a method for detecting hotspots (services responsible for suboptimal performance) in a service-oriented architecture. The method reconstructs service call graphs from logged metrics to identify the top-k services whose optimization would most reduce latency or cost. It proposes quantifying each service's impact and using a greedy algorithm to select services in descending order of impact. Evaluation on a large-scale production dataset found the approach more effective than a baseline and produced consistent results over time, helping engineering teams optimize performance.
The magic behind your Lyft ride prices: A case study on machine learning and ...Karthik Murugesan
Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalability—allowing the pricing system to be more adaptable to real-world changes.
This document presents the πRT-calculus, a calculus for modeling mobile real-time processes. It extends the π-calculus with a timeout operator to model real-time aspects. The document covers the syntax and semantics of the π-calculus and πRT-calculus. It also discusses design choices like having a global clock and discrete time. An example of a mobile video streaming system is used to illustrate the πRT-calculus. The document concludes by discussing future work, like developing timed bisimulation and extending to continuous time.
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Acutesoft is a best online center in Hyderabad. We are providing very best online training on Informatica.
Get(100$ off) on INFORMATICA Online Training at AcuteSoft Solutions For more information please contact us.
This document outlines the course content for an Informatica training course. The course covers data warehousing concepts, installing and using Informatica PowerCenter, transformations, mappings, workflows, monitoring, debugging, error handling, performance tuning, and best practices. The course includes over 30 hands-on labs covering topics like basic and advanced transformations, mappings, workflows, debugging, heterogeneous targets, reusable objects, workflow tasks, and a case study project.
Web Application Performance from User PerspectiveŁódQA
This document discusses web application performance from a user perspective. It describes how performance testing is typically done with load generators, which can simulate many users but do not behave exactly like browsers. Factors like page size, complexity, and browser hardware impact user experience. Guidelines are provided for high performance sites, and tools like YSlow and PageSpeed are recommended for checking adherence. Both synthetic and real user measurements are important, with each having advantages and disadvantages.
MuleSoft Meetup Roma - Processi di Automazione su CloudHubAlfonso Martino
The document summarizes an event held by the Rome MuleSoft Meetup Group to discuss automation of processes on CloudHub using MuleSoft's Anypoint Platform. The agenda included presentations on using infrastructure as code to automate CloudHub setup, managing API proxies, and a Q&A session. A tool called the CloudHub Automation Tool was demonstrated, which uses Terraform and other open source tools to automate CloudHub configuration and setup of environments, users, and other resources through code. The document also provided information on migrating APIs from a legacy system to the Anypoint Platform at scale.
The document discusses client side performance testing. It defines client side performance as how fast a page loads for a single user on a browser or mobile device. Good client side performance is important for user experience and business metrics like sales. It recommends rules for faster loading websites, and introduces the WebPageTest tool for measuring client side performance metrics from multiple locations. WebPageTest provides waterfall views, filmstrip views, packet captures and reports to analyze page load times and identify optimization opportunities.
How to monitor your Java micro-service with Prometheus? How to design metrics, what is USE and RED? Metrics for a REST service with Prometheus, AlertManager, and Grafana.
Slides and live-coding demo from Warsaw Java User Group Meetup in Warsaw #238.
The document discusses PTV's use of business process management (BPM) in spatial data processing. It describes PTV's motivation to introduce BPM to streamline workflows and enable parallel processing. The system architecture uses a BPM engine to control processes executed by services running on multiple workers. It demonstrates a map data processing workflow. Technical and data production BPM processes are discussed. Experiences show benefits of using BPM for transparency while pitfalls include concurrency issues. Future plans include improved modeling, statistics, and parallel processing support.
Building a web application with ontinuation monadsSeitaro Yuuki
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Web Usage Miningand Using Ontology for Capturing Web Usage Semantic
1. İsmail Hakkı Toroslu Middle East Technical University Department of Computer Engineering Ankara, Turkey Web Usage Mining and Using Ontology for Capturing Web Usage Semantic
2. 08/28/11 PART I A New Approach for Reactive Web Usage Data Processing
3.
4.
5. Web Usage Mining (WUM) Application of data mining techniques to web log data in order to discover user access patterns. Example User Web Access Log Web Mining 4130 200 HTTP/1.0 C.html GET [25/Apr/2005:03:04:48–05] 144.123.121.23 2050 200 HTTP/1.0 B.html GET [25/Apr/2005:03:04:43–05] 144.123.121.23 3290 200 HTTP/1.0 A.html GET [25/Apr/2005:03:04:41–05] 144.123.121.23 Number of Bytes Transmitted Success of Return Code Protocol URL Method Request Time IP Address
6. Phases of Web Usage Mining Web Mining Pre-Processing Pattern Analysis Raw Server log User session File Rules and Patterns Interesting Knowledge Applications Session Reconstruction Heuristics Pattern Discovery Apriori, GSP, SPADE
7.
8.
9. Example Web Topology Graph Example Web Page Request Sequence Previous Session Reconstruction Heuristics 47 32 29 15 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page
10.
11.
12.
13. Navigation-Oriented Heuristic Previous Session Reconstruction Heuristics [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 , P 23 ] P 23 Link[P 34 , P 23 ] =1 [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 ] P 34 Link[P 49 , P 34 ] = 0 Link[P 13 , P 34 ] = 1 [P 1 , P 20 , P 1 , P 13 , P 49 ] P 49 Link[P 13 , P 49 ] = 1 [P 1 , P 20 , P 1 , P 13 ] P 13 Link[P 20 , P 13 ] = 0 Link[P 1 , P 13 ] = 1 [P 1 , P 20 ] P 20 Link[P 1 , P 20 ] = 1 [P 1 ] P 1 [ ] New Page Condition Curent Session
14.
15.
16. Example Candidate Session Example Web Topology Smart-SRA 15 14 12 9 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page
17. Smart-SRA [P 1 , P 13 , P 34 , P 23 ] , [P 1 , P 13 , P 49 , P 23 ] [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ], [P 1 , P 13 , P 49 ] [P 1 , P 20 ] New Session Set (after) [P 1 , P 13 , P 34 , P 23 ] [P 1 , P 13 , P 49 , P 23 ], [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] Temp Session Set {P 23 } {P 49 , P 34 } Temp Page Set [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] [P 1 , P 20 ] [P 1 ,P 20 ] [P 1 ,P 13 ] New Session Set (before) [P 23 ] [P 49 , P 34 , P 23 ] Candidate Session 4 3 Iteration [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] New Session Set (after) [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] Temp Session Set {P 20 , P 13 } {P 1 } Temp Page Set [P 1 ] New Session Set (before) [P 20 , P 13 , P 49 , P 34 , P 23 ] [P 1 , P 20 , P 13 , P 49 , P 34 , P 23 ] Candidate Session 2 1 Iteration
18.
19. Web user can start a new session with any one of the possible entry pages of the web site Agent Simulator User-Behavior I
20. Web user can select a new page having a link from the most recently accessed page P 13 P 1 P 49 P 20 P 23 P 34 2 1 Agent Simulator User-Behavior II
21. Web user can select as the next page having a link from any one of the previously browsed pages Agent Simulator User-Behavior III P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5
22. Web user can terminate the session Agent Simulator User-Behavior IV P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5 6
23.
24.
25.
26. Parameters for generating user sessions and web topology Experimental Results 30% 0%-90% NIP : Fixed & Range 30% 0%-90% LPP : Fixed & Range 5% 1%-20% STP : Fixed & Range 10000 Number of agents 0,5 min Deviation for page stay time 2,2 min Average number of page stay time 15 Average number of outdegree 300 Number of web pages (nodes) in topology