User:Mrohatg2

E1575 - Share the data in Expertiza to a remote server via PRML format

Problem Statement

Educational peer-review systems like Expertiza contains a huge volume of data accumulated during the course of its use. Each peer-review system has a different database design. The relational model is so diverse that, it is often difficult to share the data across different peer-review systems.

In order to migrate a student’s data from one peer-review system to another, one will have to do the following:

1. Understand the database design/schema of the source peer-review system
2. Understand the database design/schema of the destination peer-review system
3. Find the equivalent tables and relations in the destination peer-review system for the source peer-review system
4. Retrieve the data from the source peer-review system
5. Modify it to match the schema of the destination peer-review system
6. Insert the data to the destination peer-review system database.

This is a very tedious job. Moreover, with such a diverse schema it is quite difficult to leverage the power of this large structured data and run any sort of analytics on it.

Solution

One solution to the above mentioned problem is to have a common schema which can capture the essence of all the reviews in a peer-review system to its entirety. Moreover, this schema should not capture any information specific to a particular peer-review system. Once such a schema standard is in place the different peer-review systems can export the data in their database to this new standard and the data can be easily shared across different peer-review systems.

PRML

Peer Review Markup Language is such a standard schema which can capture information about different reviews to its entirety.

Scope of the Project

The scope of this project is to export the data from the Expertiza database to PRML format.

Expertiza Design Document

Expertiza Database Diagram

Expertiza database schema to PRML database schema map

Table Name	Courses	Course
Attributes	id	CourseId
	null	CourseCIPCode
	name	CourseTitle
	info	CourseDescription
	null	CourseLevelID
	created_at	CourseCreated
	null	CourseStarted
	null	CourseEnded
Table Name	Assignments	Assignment
Attributes	id	AssignmentId
	null	AssignmentCIPCode
	name	AssignmentTitle
	spec_location	AssignmentDescription
	course_id	CourseID
TABLE NAME	due_dates, deadline_types	Task
ATTRIBUTES	due_dates.id	TaskID
	due_dates.deadline_type_id	TaskTypeID
	deadline_types.name	TaskTitle
	due_dates.description_url	TaskDescription
	NULL	TaskOpen
	due_dates.due_at	TaskDue
TABLE NAME	questions, questionnaires	criterion
ATTRIBUTES	questions.id	CriterionID
	questions.txt	CriterionTitle
	NULL	CriterionDescription
	questions.type	Type
	questionnaires.max_question_score	MaxLabel
	questionnaires.min_question_score	MinLabel
TABLE NAME	question_advices	Level
ATTRIBUTES	id	LevelID
	score	LevelLabel
	advice	LevelDescription
TABLE NAME	questions	Rubric
ATTRIBUTES		TaskID
	questions.id	CriterionID
TABLE NAME	teams	Actor
ATTRIBUTES	id	ActorID
	type
	parent_id
TABLE NAME	participants	Actor
	id	ActorID
	type
	parent_id
		ActorTask
ATTRIBUTES		ActorID
		TaskID
	teams_users	Participant
	user_id	ParticipantID
	team_id
		AppID
TABLE NAME	participants	Participant
ATTRIBUTES	user_id	ParticipantID
	id
		AppID
TABLE NAME		ActorParticipant
ATTRIBUTES		ActorID
		ParticipantID
		RoleID
TABLE NAME		Enrollment
ATTRIBUTES		ParticipantID
		AppID
		CourseID
TABLE NAME	Teams	Artifact
ATTRIBUTES		ActorID
		TaskID
	name	ArtifactContent
	NULL	ArtifactCharLength
	NULL	ArtifactOpen
	NULL	ArtifactEnd
TABLE NAME		Item
ATTRIBUTES		ItemContent
		ArtifactId
TABLE NAME		Review
ATTRIBUTES		AssessorActorID
		TaskID
		AssessedArtifactID
		CritiqueArtifactID
TABLE NAME		ReviewCriterionLevel
ATTRIBUTES		AssessorActorID
		TaskID
		AssessedArtifactID
		CriterionID
		CritiqueArtifactID
		EvalMode
		EvalLevelID
		ValueOrdinal
		ValueCardinal
		Comment

Implementation

We have used an Extract, Transform and Load ETL tool, Pentaho Kettle to perform the data migration from Expertiza to PRML database.

The Pentaho Kettle tool extracts data from the expertise schema, performs transformation on it and load the data in the PRML format. The tables are mapped as shown in the schema map above. The operations performed by the ETL tool:

Extraction The data is extracted from the expertise database and brought into the staging area by this operation of the ETL tool. It is the staging area in which all the transformation are done.

Transformation Many operations are performed on the data in the staging area so that the expertise schema can be converted to the PRML schema. This is called transformation of the data. An example of transformation is while mapping of participants table in the expertiza database to actors table in the PRML database. Each value of the id field is incremented by 1000 while inserting in the participants table.

Loading: The transformed data when moved from the staging area to the PRML database is called as loading. There are three types of loading implemented on the tables depending on the size of the table – initial load,partial loading and full loading. Partial Loading: Only the new or updated entries in the expertiza table are transformed and loaded in the corresponding PRML table. This is generally done for table with very large number of entries. Eg: Reviews

Partial loading is implemented with the help of timestamps and checksums.

Timestamp: One way to perform partial loading is to have a 'modified' time stamp in the source table. The destination table should mirror this 'modified' time stamp. Then while loading the destination table we extract only those records from the source table which has a 'modified' time stamp value greater than the latest 'modified' time stamp value in the destination table. The advantage is that we need not fetch all records from the source table. Only those records which needs to be updated in the destination table will be retrieved. (refer load_prml_ModifiedTimeStampTable transformation)

Checksum: Another way to perform partial loading is using checksum. A checksum is computed for each record, on selected field values, of the source table and added to the destination table while initial loading. Loading which follow would again compute the checksum for source table records and if there is no matching checksum in the destination table an update or an insert would be performed. (refer load_prml_checksumtable transformation)

Full Loading: All the entries from the expertiza table are transformed and loaded in the corresponding PRML table. This is generally done for tables with less number of entries. Eg: Courses

Expertiza tables which have been implemented

Assignments Table - Full Load

Course Table - Full Load

ModifiedTimeStamp Table - Partial Load

Task Table

Criteria Table

Due_Dates Table

Questionnaire Table

Teams Table

Jobs are being performed in a sequential order in the current project. If the tables do not depend on one another they can be performed in a parallel way to reduce time. Initially the entries in the table are buffered so that only around 8000 rows are processed at a time.

Testing

We have tested our conversion by using a set of sample expertiza data. We run the extract, transform and load methodology on it. Queries are run on the PRML database to check if it gives the same results as the expertiza database. Though the testing is not through, this is the only way the project can be tested.

Running the Project

Required Software:

• Pentaho’s Data Integration (Kettle).
• MySQL
• JAVA

Setup:

• Download and Install MySQL and JAVA
• Import to MySQL expertiza database dump file.
• Import to MySQL PRML database dump file.
• Download Pentaho's Data Integration(Kettle) Community Edition (its a copy deployment hence no installation is required).
• Download MySql Connector jar file and copy to <pentaho_root_directory\data-integration\lib>
• Start Spoon.bat if you are using a windows machine or Spoon.sh if you using Linux (pentaho_root_directory\data-integration)
• Download the transformations and jobs attached
• You can open a kettle transformation (.ktr file) using Spoon and run it (Make sure proper MySql DB connections are configured)
• You may also open the kettle job (.kjb file) using spoon and run it (Make sure the path for transformations in the job are correct)