Write short note on Sachivalaya Vahini or, E-Governance in Secretariat

-

Sachivalaya Vahini or, E-Governance in Secretariat
  •  Secretariat is the highest policy-making body and the apex seat of the State government. It is a repository of a wealth of governmental knowledge, which needs to be managed efficiently for achieving the desired objectives.
  •  In its capacity as the apex policy-making and decision-making authority at the State government level, the Secretariat has to deal with a large number of departments, organizations, and individuals.
  •  This process involves an enormous amount of communication, besides holding, maintaining, and processing large volumes of data files or other formats. Conventionally, all the files were created and maintained manually resulting in a very slow and inefficient process.
  • Project initiated by Government of Karnataka, Bangalore through the help of Directorate of Information Technology (DIT) to promote e-governance & to introduce the concept of information and knowledge management. 
  •  Connects more than 1000 computers across 40 secretariat departments fulfilling the needs of 6000 secretariat staff. 

                            Or, we can say,

  •   Secretariat LAN (Local Area Network), the fiber optic Intranet set up by the Directorate of Information Technology (DIT). The government of Karnataka, Bangalore, connects more than 100 computers spread across 40 Secretariat departments catering to the needs of 6000 secretariat staff. To promote e-governance by facilitating the policy and decision-making process and to introduce the concept of inflammation and Knowledge Management making use of this large network, national Information's Centre (NIC). Karnataka State Unit, Banglore, designed developed, and implemented a suite of software packages in all the 40 departments of the secretariat, under the title Sachivalaya Vahini.


OBJECTIVE

The objective of this project is to automate all major Office procedures followed in all the departments of the Karnataka Government Secretariat And to maintain & process large volumes of data files.


Services 

1. Patra – letter monitoring system 

2. Kadatha – file monitoring system 

3. Mokaddame – court case monitoring system 

4. Aayayaya – budget monitoring system 

5. Sibbandi – personal information system 

6. Customer support system

Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Explain Parallel Efficiency of MapReduce.

-
Image
 PARALLEL EFFICIENCY OF MAP-REDUCE It is worth trying to figure out what is the parallel efficiency of MapReduce. Now let us assume that the data produced after the map phase is σ times the original data size D (σD), further, we assume that there are P processors which in turn perform map and reduce operations depending on which phase they are used in, so we do not have any wastage of processors. Also, the algorithm itself is assumed to do wD useful work, w is not necessarily a constant it could be D^2 or something like that, but the point is that there is some amount of useful work being done even if you have a single processor and that's wD. Now let us look at the overheads of doing the computation wD using MapReduce. After the map operation, instead of D data items, we have σD data items in P pieces, so each mapper writes data to their local disk. So, P there is some overhead associated with writing this data. Next, this data has to be read by each reducer before it can begin ...
Read more

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more