codingneo

Location Data Mining and Its Applications

Trajectory Data Mining

• Sources of location trajectory data
• Trajectory preprocessing
• Trajectory indexing and retrieval
• Trajectory mining
  • Trajectory uncertainty
  • Trajectory pattern mining
  • Trajectory classification
• Trajectory outlier detection

codingneo

Step 0:

Get Homebrew installed on your mac if you don't already have it

Step 1:

Install highlight. "brew install highlight". (This brings down Lua and Boost as well)

Step 2:

codingneo

###Articles ####Apache Spark源码走读

1. Apache Spark源码走读之1 -- Spark论文阅读笔记
2. Apache Spark源码走读之2 -- Job的提交与运行
3. Apache Spark源码走读之3 -- Task运行期之函数调用关系分析
4. Apache Spark源码走读之4 -- DStream实时流数据处理
5. Apache Spark源码走读之5 -- DStream处理的容错性分析
6. Apache Spark源码走读之6 -- 存储子系统分析
7. Apache Spark源码走读之7 -- Standalone部署方式分析
8. Apache Spark源码走读之8 -- Spark on Yarn

codingneo

Download JDK 7
Open the DMG
Run pkgutil --expand "/Volumes/JDK 7 Update 60/JDK 7 Update 60.pkg" "/tmp/JDK 7 Update 60.unpkg"
Go to /tmp/JDK 7 Update 60.unpkg
Open the Distribution file in your favorite editor (I recommend (Sublime Text)[http://www.sublimetext.com])
Change the function pm_install_check() to always return true
Run pkgutil --flatten "/tmp/JDK 7 Update 60.unpkg" "/tmp/JDK 7 Update 60.pkg"
Run open "/tmp/JDK 7 Update 60.pkg"

codingneo

#!/usr/bin/env bash

# This file contains environment variables required to run Spark. Copy it as
# spark-env.sh and edit that to configure Spark for your site.
#
# The following variables can be set in this file:
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
# - SPARK_JAVA_OPTS, to set node-specific JVM options for Spark. Note that
#   we recommend setting app-wide options in the application's driver program.

codingneo

import java.util.Random;
import java.util.Scanner;
import java.io.*;
class Player
{
	private int score;
	private String name;
	Player()
	{
		name="Unknown";

codingneo

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep