Hi Friends,
                                              Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do.
                                                  There is just no time to look back, no time to wonder,"Will anyone read these pages?"
                                       With regards,
                                       Hemen Parekh
                                       27 June 2013

Saturday, 29 November 1997

ARDIS _ ARGIS

INPUTS
Yogesh/ cyril
·     Texts
    (A) Printed / Typed Documents

    (B) Electronic Documents

    Examples of Texts

(A)  Printed Documents
·     Bio - datas (Typed)
·     Job - Advts
·     Magazine / Newspaper Articles
·     Directories (Kothari /IRIS etc)
·     Directories (CA/ CS Membership)
·     Bullletins (Bapuji Impex/ Domex)
    (B) Electronic Documents
·     Databases
·     Bio - datas received over Internet / Extranet
·     E : mails
·     Files on Floppies
·     Files on CDs
·     Files on Hard - Disks
·     Files on Tapes
·     Voice / Speech Converted to text (thru speed - recognition software)

 PROCESS
·     Scanning of document (in a "scanner", if Printed/ typed document)
·     Electronic Scanning (if in electronic - file format)
·     OCR (for printed / typed document)
·     Spell - check / Automatic Spelling Correction
·     Search / Identify/ Pick - up "KEYWORDS" from each document / file
·     Give each document a unique "number" (this will be PEN for resume's / Advertisement Nos. etc)
·     Link key - words with document
·     Assign "meaning" to each keyword (based on context)
·     Store each keyword in relevant "Meaning - lines / Fields" to create a database”
·     For each keyword, create directory of "Synonyms & Autonyms"
·     Create a continuously up - date "tables" re : frequency - of - usage of each keyword
·     Create and continuously up - date "tables" re : frequency - of - usage of all words (not only keywords) used "before  & after" each keyword to create "CONTEXT PROBABILITY for each keyword.
·     Repeat above process for all the "Phrases" and "Sentences" in which a given KEYWORD has been used to establish "CONTEXT PROBABILITY" of phrase/ Sentences.
·     If a Keyword has been used in phrase / sentence having a very low "context Probability" then replaces that phrase/ sentence by a phrase / sentence of the highest "Context Probability".
 OUTPUTS :-
·     Databases of
·     Keywords
·     Synonyms
·     Antonyms
·     Bins/ Fields
·     Phrases / Sentences
·     "Frequency of Usage" tables
  PRINTED OUTPUTS:
·     Converted Bio - datas (Full)
·     Brief bio - datas (brief outline)
·     One - line tabulations
·     Standard Letters/ Responses
·     Short - lists of suitable executives.

h.c.parekh

Saturday, 8 November 1997

INFOSEEK

What salaries executives get?
(Taken from Annual Reports)
Once we build up a large database (say 100,000 executives/ 1000 companies), several interesting
ANAYLYSIS / PRESENTATIONS are possible
(1) Scattergram
Each as "salary" data is being added daily/ continuously, it should be possible to plot Salary Vs. Age  On the fly (On Internet or Extranet). Thereafter a facility can be provided (to a surfer) to go along x axis and click on.
any desired "Age" value.
(of course the scattergram and the line of best fit are already visible to him on the screen).
As soon as he clicks on "Age = 35" the scattergram disappears.
 over a period of few years, with accumulation of salary - data of thousands of executives, year after year, it would be possible to carry - out (and display on screen) many types of
TREND ANALYSIS
Such "trend - analysis" could be repeated for
    . Any given "Age"
    . Any given "Edu. Qualification"
    . Any given "Function"
    . Any given "Industry"
    . Any given "Designation - Level"
    . Any given "Company"
Also, we have  to show
    . Salary Vs. Edu. Qualification
    . Salary Vs. Function
    . Salary Vs. Industry
    . Salary Vs. Designation Leave  etc. etc.
(2) SPECIFIC COMPANY SALARY ANALYSIS
Any number of executives (especially personnel Manager) would want to analyse,  "Salaries of Executives of a Given Company"  Such a "given Company" could be a competitor     
or
The Industry - Leader (so that you can bench - mark)
e.g. Videocon International (Ltd. Might wish to Analyse salary details/ distribution of
       . BPL
       . Godreg GE
       . Amtrex
       . Voltas
       . Philips  etc
Personnel Manager of Videocon Might like to shoot a query on our database as follows :
Pl. show me. salary details of Companies.
XYZ
ABC
LMN
PQR
A  Minimum of 3 companies must be mentioned.
a.  All executive
b.  Executives belonging to function
c.  Executives with “designation- Level” of CMD/ MD/V.P etc.
d.  Executives in the age-group of 25-29/ 30-34/ 35-39/40-44 etc.
The result be displayed as a TABULATION or A GRAPH ( without revealing the actual names of the executives)

h.c.parekh