Interesting WWW 2010 papers

• EXPLORING WEB SCALE LANGUAGE MODELS FOR SEARCH QUERY PROCESSING
  Jian Huang, Jiangbo Miao, Xiaolong Li, Jianfeng Gao and Kuansan Wang
• CROSS-DOMAIN SENTIMENT CLASSIFICATION VIA SPECTRAL FEATURE ALIGNMENT
  Sinno Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang and Zheng Chen
• BUILDING TAXONOMY OF WEB SEARCH INTENTS FOR NAME ENTITY QUERIES
  Xiaoxin Yin and Sarthak Shah
• MULTI-MODALITY IN ONE-CLASS CLASSIFICATION
  Boris Chidlovskii and Matthijs Hovelynck
• A SCALABLE MACHINE LEARNING APPROACH FOR SEMI-STRUCTURED NAMED ENTITY RECOGNITION
  Utku Irmak and Reiner Kraft
• FACETED EXPLORATION OF IMAGE SEARCH RESULTS
  Roelof van Zwol and Börkur Sigurbjörnsson
• TOWARDS NATURAL QUESTION GUIDED SEARCH
  Alexander Kotov and ChengXiang Zhai
• A LARGE SCALE ACTIVE LEARNING SYSTEM FOR TOPICAL CATEGORIZATION ON THE WEB
  Suju Rajan, Dragomir Yankov, Scott Gaffney and Adwait Ratnaparkhi
• DIVERSIFYING WEB SEARCH RESULTS
  Davood Rafiei, Krishna Bharat and Anand Shukla
• A GENERAL FRAMEWORK FOR EXPLORING CATEGORY INFORMATION FOR QUESTION RETRIEVAL IN COMMUNITY QUESTION ANSWER ARCHIVES
  Xin Cao, Gao Cong, Bin Cui and Christian Jensen
• RANKING SPECIALIZATION FOR WEB SEARCH: A DIVIDE-AND-CONQUER APPROACH BY USING TOPICAL RANKSVM
  Jiang Bian, Xin Li, Fan Li, Zhaohui Zheng and Hongyuan Zha
• GENERALIZED DISTANCES BETWEEN RANKINGS
  Ravi Kumar and Sergei Vassilvitskii
• THE ANATOMY OF A LARGE-SCALE SOCIAL SEARCH ENGINE
  Damon Horowitz and Sepandar Kamvar
• USE TWITTER DATA FOR RECENCY RANKING IMPROVEMENT IN WEB SEARCH
  Anlei Dong, Ruiqiang Zhang, Pranam Kolari, Bai Jing, Yi Chang, Fernando Diaz, Zhaohui Zheng and Hongyuan Zha
• A CHARACTERIZATION OF ONLINE SEARCH BEHAVIOR
  Ravi Kumar and Andrew Tomkins
• WHAT IS TWITTER, A SOCIAL NETWORK OR A NEWS MEDIA?
  Haewoon Kwak, Changhyun Lee, Hosung Park and Sue Moon

sqlite in Python

Sqlite is a very useful server-less database system that can be accessed from Python as well.

By the way you can combine multiple insert statements in a single transaction to improve performace
as described here.

import sqlite3

def createDB():
    db = sqlite3.connect('people.db')
    db.execute('create table boys(name, age)')
    db.commit()
    db.close()
    pass

def writeDB():
    db = sqlite3.connect('people.db')
    db.execute('insert into boys(name, age) values ("danushka",30)')
    db.commit()
    db.close()
    pass

def readDB():
    db = sqlite3.connect('people.db')
    L = db.execute('select name, age from boys').fetchall()
    print L
    db.close()
    pass

if __name__ == "__main__":
    #createDB()
    #writeDB()
    readDB()

Snow Leopard NumPy, SciPy, matplotlib

Installing the above mentioned packages in Snow Leopard can be quite difficult.

An easy solution is to use the script mentioned here
There is a very good description here if you want to do it step by step.