Some official statistics on social media from the owners themselves
1) Facebook-
http://www.facebook.com/press/info.php?statistics
Date -17 Nov 2011
Statistics
People on Facebook
- More than 800 million active users Continue reading “Statistics on Social Media”
So I decided to create Pages on Google Plus for my favorite programming languages.
Add to circles
Python
https://plus.google.com/107930407101060924456/posts
These are in accordance with Google’s Policies http://www.google.com/intl/en/+/policy/pagesterm.html Continue reading “Creating Pages on Google Plus for some languages”
Just an announcement from The Document Foundation (which has notable supporters including Google etc at http://www.documentfoundation.org/supporters/)
With both Google Docs and Libre Office – it seems like a flank attack on Office productivity software (from the cloud and from the PC/tablet ground)- however Microsoft’s Sharepoint is much better in collobration compared to the Google Docs and it has huge number of templates (more than the 38 extensions and 13 templates right now at the links below (just like WordPress has huge number of themes compared to Blogger)
Anyways, check out- it is an interesting start
http://extensions.libreoffice.org/
Extension Releases
….
and http://templates.libreoffice.org/
But after all that- I was quite happy to see Google Fusion Tables within Google Docs. Databases as a service ? Not quite but still quite good, and lets see how it goes.
https://www.google.com/fusiontables/DataSource?dsrcid=implicit&hl=en_US&pli=1
http://googlesystem.blogspot.com/2011/09/fusion-tables-new-google-docs-app.html
But what interests me more is
http://code.google.com/apis/fusiontables/docs/developers_guide.html
The Google Fusion Tables API is a set of statements that you can use to search for and retrieve Google Fusion Tables data, insert new data, update existing data, and delete data. The API statements are sent to the Google Fusion Tables server using HTTP GET requests (for queries) and POST requests (for inserts, updates, and deletes) from a Web client application. The API is language agnostic: you can write your program in any language you prefer, as long as it provides some way to embed the API calls in HTTP requests.
The Google Fusion Tables API does not provide the mechanism for submitting the GET and POST requests. Typically, you will use an existing code library that provides such functionality; for example, the code libraries that have been developed for the Google GData API. You can also write your own code to implement GET and POST requests.
Also see http://code.google.com/apis/fusiontables/docs/sample_code.html
| Language | Library | Public repository | Samples |
|---|---|---|---|
| Python | Fusion Tables Python Client Library | fusion-tables-client-python/ | Samples |
| PHP | Fusion Tables PHP Client Library | fusion-tables-client-php/ | Samples |
An easy way to learn how to use an API can be to look at sample code. The table above provides links to some basic samples for each of the languages shown. This section highlights particularly interesting samples for the Fusion Tables API.
| Language | Featured samples | API version |
|---|---|---|
| cURL |
|
SQL API |
| Google Apps Script |
|
SQL API |
| Java |
|
SQL API |
| Python |
|
Docs List API |
| Android (Java) |
|
SQL API |
| JavaScript – FusionTablesLayer | Using the FusionTablesLayer, you can display data on a Google Map
Also check out FusionTablesLayer Builder, which generates all the code necessary to include a Google Map with a Fusion Table Layer on your own website. |
FusionTablesLayer, Google Maps API |
| JavaScript – Google Chart Tools | Using the Google Chart Tools, you can request data from Fusion Tables to use in visualizations or to display directly in an HTML page. Note: responses are limited to 500 rows of data. | Google Chart Tools |
Google Fusion Tables is dedicated to providing code examples that illustrate typical uses, best practices, and really cool tricks. If you do something with the Google Fusion Tables API that you think would be interesting to others, please contact us at googletables-feedback@google.com about adding your code to our Examples page.
Updated-You can use Google Fusion Tables from within R from http://andrei.lopatenko.com/rstat/fusion-tables.R
ft.connect <- function(username, password) {
url = "https://www.google.com/accounts/ClientLogin";
params = list(Email = username, Passwd = password, accountType="GOOGLE", service= "fusiontables", source = "R_client_API")
connection = postForm(uri = url, .params = params)
if (length(grep("error", connection, ignore.case = TRUE))) {
stop("The wrong username or password")
return ("")
}
authn = strsplit(connection, "\nAuth=")[[c(1,2)]]
auth = strsplit(authn, "\n")[[c(1,1)]]
return (auth)
}
ft.disconnect <- function(connection) {
}
ft.executestatement <- function(auth, statement) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = statement)
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = postForm(uri = url, .params = params, .opts = opts)
if (length(grep("<HTML>\n<HEAD>\n<TITLE>Parse error", result, ignore.case = TRUE))) {
stop(paste("incorrect sql statement:", statement))
}
return (result)
}
ft.showtables <- function(auth) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = "SHOW TABLES")
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = getForm(uri = url, .params = params, .opts = opts)
tables = strsplit(result, "\n")
tableid = c()
tablename = c()
for (i in 2:length(tables[[1]])) {
str = tables[[c(1,i)]]
tnames = strsplit(str,",")
tableid[i-1] = tnames[[c(1,1)]]
tablename[i-1] = tnames[[c(1,2)]]
}
tables = data.frame( ids = tableid, names = tablename)
return (tables)
}
ft.describetablebyid <- function(auth, tid) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = paste("DESCRIBE", tid))
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = getForm(uri = url, .params = params, .opts = opts)
columns = strsplit(result,"\n")
colid = c()
colname = c()
coltype = c()
for (i in 2:length(columns[[1]])) {
str = columns[[c(1,i)]]
cnames = strsplit(str,",")
colid[i-1] = cnames[[c(1,1)]]
colname[i-1] = cnames[[c(1,2)]]
coltype[i-1] = cnames[[c(1,3)]]
}
cols = data.frame(ids = colid, names = colname, types = coltype)
return (cols)
}
ft.describetable <- function (auth, table_name) {
table_id = ft.idfromtablename(auth, table_name)
result = ft.describetablebyid(auth, table_id)
return (result)
}
ft.idfromtablename <- function(auth, table_name) {
tables = ft.showtables(auth)
tableid = tables$ids[tables$names == table_name]
return (tableid)
}
ft.importdata <- function(auth, table_name) {
tableid = ft.idfromtablename(auth, table_name)
columns = ft.describetablebyid(auth, tableid)
column_spec = ""
for (i in 1:length(columns)) {
column_spec = paste(column_spec, columns[i, 2])
if (i < length(columns)) {
column_spec = paste(column_spec, ",", sep="")
}
}
mdata = matrix(columns$names,
nrow = 1, ncol = length(columns),
dimnames(list(c("dummy"), columns$names)), byrow=TRUE)
select = paste("SELECT", column_spec)
select = paste(select, "FROM")
select = paste(select, tableid)
result = ft.executestatement(auth, select)
numcols = length(columns)
rows = strsplit(result, "\n")
for (i in 3:length(rows[[1]])) {
row = strsplit(rows[[c(1,i)]], ",")
mdata = rbind(mdata, row[[1]])
}
output.frame = data.frame(mdata[2:length(mdata[,1]), 1])
for (i in 2:ncol(mdata)) {
output.frame = cbind(output.frame, mdata[2:length(mdata[,i]),i])
}
colnames(output.frame) = columns$names
return (output.frame)
}
quote_value <- function(value, to_quote = FALSE, quote = "'") {
ret_value = ""
if (to_quote) {
ret_value = paste(quote, paste(value, quote, sep=""), sep="")
} else {
ret_value = value
}
return (ret_value)
}
converttostring <- function(arr, separator = ", ", column_types) {
con_string = ""
for (i in 1:(length(arr) - 1)) {
value = quote_value(arr[i], column_types[i] != "number")
con_string = paste(con_string, value)
con_string = paste(con_string, separator, sep="")
}
if (length(arr) >= 1) {
value = quote_value(arr[length(arr)], column_types[length(arr)] != "NUMBER")
con_string = paste(con_string, value)
}
}
ft.exportdata <- function(auth, input_frame, table_name, create_table) {
if (create_table) {
create.table = "CREATE TABLE "
create.table = paste(create.table, table_name)
create.table = paste(create.table, "(")
cnames = colnames(input_frame)
for (columnname in cnames) {
create.table = paste(create.table, columnname)
create.table = paste(create.table, ":string", sep="")
if (columnname != cnames[length(cnames)]){
create.table = paste(create.table, ",", sep="")
}
}
create.table = paste(create.table, ")")
result = ft.executestatement(auth, create.table)
}
if (length(input_frame[,1]) > 0) {
tableid = ft.idfromtablename(auth, table_name)
columns = ft.describetablebyid(auth, tableid)
column_spec = ""
for (i in 1:length(columns$names)) {
column_spec = paste(column_spec, columns[i, 2])
if (i < length(columns$names)) {
column_spec = paste(column_spec, ",", sep="")
}
}
insert_prefix = "INSERT INTO "
insert_prefix = paste(insert_prefix, tableid)
insert_prefix = paste(insert_prefix, "(")
insert_prefix = paste(insert_prefix, column_spec)
insert_prefix = paste(insert_prefix, ") values (")
insert_suffix = ");"
insert_sql_big = ""
for (i in 1:length(input_frame[,1])) {
data = unlist(input_frame[i,])
values = converttostring(data, column_types = columns$types)
insert_sql = paste(insert_prefix, values)
insert_sql = paste(insert_sql, insert_suffix) ;
insert_sql_big = paste(insert_sql_big, insert_sql)
if (i %% 500 == 0) {
ft.executestatement(auth, insert_sql_big)
insert_sql_big = ""
}
}
ft.executestatement(auth, insert_sql_big)
}
}
As per official statistics, not many people download data from it .
Why dont they just donate the data and save taxpayers some money
| Agency/Sub-Agency/Organization | Raw Datasets (high-value) |
Tools (high-value) |
Geodata | Total | Latest Entry | # of times downloaded within the last week* |
|---|---|---|---|---|---|---|
|
TOTAL
|
3,486 (2,163) | 1,071 (393) | 386,429 | 390,986 | 08/24/2011 | 0 |
* These numbers represent the number of times a user has clicked on the “XML” or “CSV” (for example) links in the Raw Data Catalogs to download datasets and user downloads of tools in the Tool Catalog available in these categories.
But apparently lots of people like it still
http://www.data.gov/metric/visitorstats/monthlyredirecttrend
More list of public data repositories-
Google http://www.google.com/publicdata/directory
DataMob http://datamob.org/datasets
Amazon http://aws.amazon.com/publicdatasets/
DataMarket http://datamarket.com/
Infochimps http://www.infochimps.com/
From SEC, the Edgar http://www.sec.gov/edgar/searchedgar/companysearch.html
More lists of lists
http://www.kdnuggets.com/2011/02/free-public-datasets.html
But who gets more downloads last week than Data.gov !
Here is more funny stuff on my Google Plus stream- you can now add me to a circle using the icon in the right margin.
0) Funny Cats are here to stay
1) So what?
New Contest at http://www.ecmlpkdd2011.org/dcOverview.php
Organization | Overview | Task and Datasets | Timeline
VideoLectures.net is a free and open access multimedia repository of video lectures, mainly of research and educational character. The lectures are given by distinguished scholars and scientists at the most important and prominent events like conferences, summer schools, workshops and science promotional events from many fields of Science. The portal is aimed at promoting science, exchanging ideas and fostering knowledge sharing by providing high quality didactic contents not only to the scientific community but also to the general public. All lectures, accompanying documents, information and links are systematically selected and classified through the editorial process taking into account also users’ comments.
The ECML-PKDD 2011 Discovery Challenge is organized in order to improve the website’s current recommender system. The challenge consists of two main tasks and a “side-by” contest. The provided data is for both of the tasks, and it is up to the contestants how it will be used for learning (building up) a recommender.
Due to the nature of the problem, each of the tasks has its own merit: task 1 simulates new-user and new- item recommendation (cold-start mode), task 2 simulates clickstream based recommendation (normal mode). Continue reading “Machine Learning Contest”
DECISION STATS 