lorinc

/*
problem: return the sum of call durations for those operators
         who initiated at least one call per day for a given
         timeslot. data: calls(mtid, date, duration)
*/

SELECT sum(duration)
FROM calls
JOIN
  (SELECT MTID, count(MTID)

lorinc

#!/bin/bash

# todo:
# - one script to control all 3 servers
# - script to know expected screens per server
# - pull functionalities into seperate functions e.g. is_server_up()

logfolder="/home/mc/servers/logs/"
this_box=`hostname | awk 'BEGIN {FS="."} {print toupper($1)}'`
backup_folder='/home/mc/servers/backup/'

lorinc

#### reading libraries ####
library(data.table)
library(ggplot2)
library(gridExtra)

#### reading sources ####

setwd("~/Desktop/consolidated")

# "SFaccountID","SFopportunityID","SFaccountName","SFcreateDate","SFendDate","SFindustry","SFproductName","SFtotalPriceCurrency","SFtotalPrice"

lorinc

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lorinc

-- iterator function to traverse nested tables
function iterate(tbl, parent)
  parent = parent or {}
  if (type(tbl)=="table") then
    for key, value in pairs(tbl) do
      iterate(value, table.extend(parent, key))
    end
  end
  coroutine.yield(parent, tbl)
end

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lorinc

/*
  Pairwise distance between every single members of the parliament
  active in that specific term, for every single voting that happened.
  
  Generates a 61GB dataset for the 14,452 voting events.
  
  Next step is reducing the amount of data, using roling window aggregation
  on the time dimension, and clustering on the people dimension.
*/