Lecture 9 Lecture 6: Design Principles & System Architecture Course Outline W1: Intro W2: Modelling with UML W3-4: Design patterns W5-6: Design principles & system architecture W7-8: Testing W9: Continuous integration W10: Review Today’s Plan 10:05-11:55: Quiz 6 (assessed, correctness) Design principles System architectures […]
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Presentation_final Combinational Collaborative Filtering: An Approach For Personalised, Contextually Relevant Product Recommendation Baskets Research Project – Jai Chopra (338852) Dr Wei Wang (Supervisor) Dr Yifang Sun (Assessor) • Recommendation engines are now heavily used online • 35% of Amazon purchases are from algorithms • We would like to extend on current implementations and provide some
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Microsoft Word – pap0869-inglesant.doc The True Cost of Unusable Password Policies: Password Use in the Wild Philip Inglesant & M. Angela Sasse Department of Computer Science University College London Gower Street, London WC1E 6BT, UK {p.inglesant, a.sasse}@cs.ucl.ac.uk ABSTRACT HCI research published 10 years ago pointed out that many users cannot cope with the number and
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CS 361A (Advanced Algorithms) CS 361A (Advanced Data Structures and Algorithms) Lecture 20 (Dec 7, 2005) Data Mining: Association Rules Rajeev Motwani (partially based on notes by Jeff Ullman) Association Rules Overview Market Baskets & Association Rules Frequent item-sets A-priori algorithm Hash-based improvements One- or two-pass approximations High-correlation mining Association Rules Two Traditions DM is
Homework Task 4 Nonparametric Regression Advanced Analytics (QBUS3830) Homework Task 4: Additive Models Setting up the notebook for figures: In [11]: # Packages import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline # Settings sns.set_context(‘notebook’) sns.set_style(‘ticks’) colors = [‘#4E79A7′,’#F28E2C’,’#E15759′,’#76B7B2′,’#59A14F’, ‘#EDC949′,’#AF7AA1′,’#FF9DA7′,’#9C755F’,’#BAB0AB’] sns.set_palette(colors) plt.rcParams[‘figure.figsize’] = (9, 6) In [13]: import warnings warnings.filterwarnings(‘ignore’) In [7]: import pandas as pd import
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Schema Refinement and Normal Forms Normal Forms. BCNF and 3NF Decompositions CS430/630 Lecture 17 Slides based on “Database Management Systems” 3rd ed, Ramakrishnan and Gehrke Decomposition of a Relation Schema A decomposition of R replaces it by two or more relations Each new relation schema contains a subset of the attributes of R
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Overview In this assignment, you will first construct the basic building block of a table: Tuples. Next, you will implement a simple Catalog that keeps track of what tables are currently in the database and provides a way to access them. Finally, you will implement HeapFiles which are the physical representation of the data in
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Page 1 of 10 Table of Contents Part 1- Data Management: Spatial Queries …………………………………………………………………… 2 Exercise 1- Simple Spatial SQL ………………………………………………………………………………… 2 Exercise 2 – Combining Spatial and Non-Spatial Queries ……………………………………………. 3 Exercise 3 – Write Your Own Combined Queries ………………………………………………………… 4 Part 2- Working with 3D Data – DDL and DML ………………………………………………………………. 6 Part
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17spm_L16 Scaling Up Agile Processes SPM 2017 © Ron Poet Lecture 16 1 Multiple Team Projects � Agile approaches mainly describe how to run a single team on a project lasting a few months and with uncertain requirements. � What about larger projects with multiple teams and longer timescales? � Most larger agile processes are
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1 Spatial Data Management • Dr Claire Ellul • c.ellul@ucl.ac.uk Assignment Progress .. • By now you should have written your system specification and created the corresponding conceptual and logical ER Diagrams – (At least in draft format) ER Diagrams • An ERD is a model of the world, containing information of interest to the
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