The document describes a project called The Asset Consultancy which aims to analyze and predict current and future prices of real estate properties. It will fetch data from various sources and use Hadoop and its components like Hive and Sqoop to analyze the data and produce predictions using algorithms. This will help customers determine if investing in a particular property is advantageous or not. The document outlines the technology used, system architecture, current scenario, new proposed flow, features, implementation of big data using Hadoop, and future scope of the project.

1. The Asset Consultancy

2. Project Details:
Project Title : The Asset Consultancy
Project ID: 46414
Group Size: 2
Name Of Developers : Janki Kansara(120170107024)
Rushin Naik (120170107046)
Project Type: IDP
Company Name: Sculptsoft

3. 1) Abstract
2) Technology Used
3) System Architecture
4) Current Scenario
5) New Flow
6) Project Need
7) Core Features and Benefits
8) What’s New
9) Diagrams
10)Implementation of Big Data
11) Future Scope
12) Conclusion
13) Bibliography/References
Index:

4. Abstract:
The Asset Consultancy involves the analysis/prediction of current
and future price of real-estate commercials and buildings. The goal is to
conduct that depending on analysis/prediction made for the prices of
property, whether the customer should invest his money on that property or
not. Upon examination of the results, it can be inferred if investing money on
the estate would bring out a positive outcome or a negative one, and hence
makes the customer aware about the advantages/flaws of investing at the
particular place. Data will be fetched from different pre-requisites and
compiled at one place producing analysis by an algorithm using Hadoop
application and its different internal structures, which would be implemented
in Java language.

5. Technology Used:
Software Used
 Operating System: Windows 7 32 bit Professional, Linux Ubuntu
 Web Server: Apache Tomcat Server 8.0.15.0
 Server side Application Software: NetBeans IDE 7.0.1 or 8.0.2
 Languages: Java Script, HTML/CSS, Java, JSP
 Data Base: SQL Server Management Studio
 Client Browsers: Google Chrome Or Firefox
 Java Software : JDK 1.8.0_66
 Hadoop 2.7.1
 Hive 1.2.1
 Sqoop 1.4.6
 Netbeans IDE 8.0.2
 MYSQL Workbench 6.2
 Linux OS/Linux OS environment on windows

6. Hardware Used
3 Ghz Quad core processor
8 GB RAM
Ethernetcard

7. System Architecture:
Java Application Architecture:

8. Hadoop Architecture:

9. Current Scenario:
Decision
to
Invest
Confusion
Lack of
Authenticity
Of Agents
Lack of
Information
Giving
Money to
Agents

10. New Flow:
The Asset
Consultancy
Property and
Agent Ratings
Analysis &
Prediction
Group
Interaction
Agent, Customer
& Property

11. Project Need:
• To get authentic data about the upcoming trend from the past years
pricing of real estate.
• To improve the agent-customer connectivity.
• To get an idea about the scenario at personal level rather than just
following what the agent says.
• To save money making decisions by yourself.
• To make investment more precisely and smartly.
• Improve awareness about the market trend.
• To get approximate value of one’s own property after few years.

12. Core Feature and Benefits:
• To provide the latest data of the price fluctuations taking place
in real estates by referring to the past years data.
• It also provides facility of creating groups to interact with other
customers and agents.
• The data for the next few years can be analyzed and
summarized, which will give a better idea to the customer. The
property can be recognized more deeply with better prospects.
• Authenticated data- All at One place.

13. What’s new?
• Yearly prediction of real estate pricing from the past and current scenario.
This new application provides the facility for prediction of the prices of the
assets and give an idea about the trend of the value of properties.
• Users can create group and discuss about different properties with agents.
The agent can give notifications to multiple customers and also other agents
through the group feature. This facility promotes customer-agent interaction.
• There other features worth mentioning like the user can get alerts about the
latest updates of the particular property, easy and user friendly search of
multiple properties and simple interface.
• The system not only allows to view the graphical form of the trend, but also
allows the user to save the report of the analysis and prediction to the disc.

14. Context Level Diagram
Data Flow Diagram:

15. Level-1:

16. Level-1:

17. Level-2:
Level-2:

18. Level-2:

19. Level-2:

20. Use Case Diagram:

21. Register
Login
System Interface
Search Property
Create Group
Select Property
View Description
Stalk Property Compare Property View Analysis & Prediction
Show Result
Logged-in Successfully
New User
Registered Successfully
Go to Groups
Give Property Specification
Get Property Notifications
Comparing mulitple properties
Select Chart Type
Invalid Entry
Invalid Entry
Logout
Invite Member
Read Post
Discuss Property
Group Verified and Created
Wait for notification
View FeedBack
Go to specific Property or Broker
No such property found
Select Group
State Transition
Diagram:

22. BrokerAdministratorCustomer
Registration
Login
Invalid Entry
View Property Details
Invalid Entry
Registration
Make Group
Group Created
Manage Group Activities
Give/View Feedback
View Analysis/Prediction
Logout
Login
Broker Profile Verification
Manage Group Request
Group Verified
Broker Verified
Logout
Login
Make Group
Manage Group Activities
Group Created
Logout
Update Property DetailsVerify Property Update
View Feedback
View Property Details
View Feedback
Invalid Entry
Invalid Entry
Invalid Entry
Activity Diagram:

23. +show_property()
-name
-address
-area
-cost
-type
-associated_broker
-description
Property
+search_property()
+search_broker()
+view_info()
+manage_group()
+registration()
-customer_id
-address
-G_id
Customer
+update_profile()
+update_property()
+manage_group()
+registration()
-broker_id
-address
-G_id
Broker
+login()
+logout()
-login_id
-password
-name
-phone number
-email
User
+invite_member()
+show_notification()
+delete_member()
-group_name
-group_admin
-date_of_creation
-no_of_member
-G_id
Group
+show_analysis()
+show_prediction()
+show_comparison()
+stalk_property()
-feature_name
Property Features
+manage_request()
Administrator
+show_feedback()
-user_id
-feedback_id
-feedback_type
Feedback
*
*
1
*
-Authentication1
*
1
1..*
*
*
*
*
1
*
*
-Authentication 1
Class Diagram:

24. Object
Diagram:
User
Customer Agent Admin
Group
Feedback
Property Property Features
*
*
*
*
*
*
*
*
*
*
1
*
1
*
-1
1
-*
*

25. E-R Diagram: Agent Detail
PK a_id
a_fname
a_lname
a_email
a_state
a_city
a_area
a_status
a_mypropid
a_description
a_workex
a_img
a_companyname
FK1 u_id
Agent Feedback
PK af_id
comment
time
FK1 a_id
Prop Detail
PK p_id
p_name
p_price
p_description
p_address
p_state
p_city
p_area
p_img
p_type
p_zipcode
FK1 u_id
Asset Records
PK id
FK1 p_id
Year
Price
Property Feedback
PK pf_id
comment
time
FK1 p_id
Group Detail
PK g_id
g_name
FK1 u_id
u_name
Group Member
PK gmid
FK2 u_id
FK1 g_id
Login
PK u_id
u_name
pwd
u_role
Customer Detail
PK c_id
c_name
c_email
c_city
c_num
c_state
c_area
c_img
FK1 u_id
Chat Messages
PK cmid
FK1 g_id
FK2 u_id
msg
date_time

26. Broker
B_id
Log_id
B_name
B_contactno
B_address
B_email
G_id
Customer
C_id
Log_id
C_name
C_gender
C_contactno
C_email
G_id
Feedback
F_id
C_id
F_type
Subject_id
Group_detail
G_id
G_name
G_admin
No_of_members
G_date
Log_id
Login
Log_id
Username
Password
User_type
Property
P_id
P_name
P_location
P_size
P_type
Associated_broker
Description
Report
R_id
P_id
User_id
R_date
R_path
Relationship
Database Diagram:

27. Data Dictionary:
Field Data Type Constraints Description
P_id Int Primary key Unique identity of property.
P_name Varchar(50) Not null Name of property.
P_location Varchar(200) Not null Address of the property.
P_size Int Not null Area of property in sq. meter.
P_type Varchar(50) Not null Features of property.
Associated_agent Varchar(50) Allow null Agent handling the property.
Description Varchar(500) Not null Summary about the property.
Property:

28. Field Data Type Constraints Description
C_id Int Primary key Unique identity of customer.
Log_id Int Foreign_id Login id.
C_name Varchar(50) Not null Name of customer.
C_gender Varchar(10) Not null Gender of customer.
C_contactno Varchar(50) Not null Contact number of the customer.
C_email Varchar(50) Not null Email id of the customer.
G_id int Allow null The id of group the customer has joined
Field Data Type Constraints Description
A_id Int Primary key Unique identity of agent.
Log_id Int Foreign key Login id.
A_name Varchar(50) Not null Name of agent.
A_contactno Varchar(50) Not null Contact number of agent.
A_address Varchar(50) Not null Address of agent.
A_email Varchar(50) Not null Email id of agent.
G_id int Allow null Id of the group the agent has joined.
Customer:
Agent:

29. Field Data Type Constraints Description
G_id Int Primary key Unique identity of the group.
G_name Varchar(50) Not null Name of group.
G_admin Varchar(50) Not null Administrator of the group.
No_of_members Int Not null Number of members in the group.
Log_id int Not null The login id of the members in the group.
G_date Date Not null Date on which the group was created.
Field Data Type Constraints Description
F_id Int Primary Key Unique identity of feedback.
C_id Int Foreign Key Customer who gave the feedback.
F_type Varchar(50) Not null Property feedback or agent feedback.
Subject_id int Not null Agent id or Property id about which the feedback
is posted.
Group:
Feedback:

30. Field Data Type Constraints Description
R_id Int Primary Key Unique identity of analysis and prediction
report.
P_id Int Foreign Key Property about which the report is generated.
User_id Int Foreign Key The user who issued the report.
R_date Date Not null Date on which the report was generated.
R_path Varchar(100) Not null Path for the reports.
Field Data Type Constraints Description
Log_id int Primary key Unique identity used for login.
Username Varchar(50) Not null Username of the user.
Password Varchar(20) Not null Password for login
User_type Varchar(10) Not null Type of user: Customer, Agent, Admin
Report:
Login:

31. Implementation of Big Data
 Big Data is used to reduce the large amount of data
existing in the database.
 This data(mostly existing in the row format) is then
processed inside hive and sqoop to synchronize the
data.
 Then after, the whole data fetched from the database is
reduced using MapReduce Technique of Hadoop.
 Thousands of rows are shorten up to 50-100 rows and
hence we get a reduced format of the infinite rows.
 Hence these rows will produce the data required by the
program and will present them in chart format.

33.  The chart format usually is shown in the following
manner:

34. Sqoop Functionality
 Sqoop is used in our project to transfer data between
Hadoop and relational databases. The data is imported
from MySQL into the Hadoop Distributed File System
(HDFS), transformed in Hadoop MapReduce, and
then exported back into an RDBMS.
 Sqoop automates most of this process, relying on the
database to describe the schema for the data to be
imported. Sqoop uses MapReduce to import and
export the data, which provides parallel operation as
well as fault tolerance.

35. Sqoop Code:

36. Hive Functionality
 Hive defines a simple SQL-like query language, called QL,
that enables users familiar with SQL to query the data. At
the same time, this language also allows programmers who
are familiar with the MapReduce framework to be able to
plug in their custom mappers and reducers to perform
more sophisticated analysis that may not be supported by
the built-in capabilities of the language. QL can also be
extended with custom scalar functions (UDF's),
aggregations (UDAF's), and table functions (UDTF's).
 Hive functionality is basically used in our project to process
the fetched tables from the database and create new tables
in the hadoop database.

37. Hive Code

38. Criteria Fetch
 We have set a particular criteria for the function to
fetch the data from the database table just as below:
 This query is used to fetch the particular data from the
database of MYSQL and hence the processing of the
function takes place according to this criteria that has
been set.
 The query is written and run externally by hadoop and
after it has been completely processed, the script is
called from the database of the tables.

39. Criteria Fetch

40. Script Call
 As soon as the criteria is fetched, the relevant script is
called from the database table which is used to
implement in the servlet and java script pages.
 The script that is fetched, contains all the information
which is required by the database to implement sqoop
and hive functions.
 Script is basically the Query that is fetched to
implement in the Hadoop.

41. Script Call

42. Script Execution

43. Future Scope
 The new features related to personal advice from the adviser can be added to the
system. Customer can personally ask questions to the advisers and get the doubts
cleared.
 More precise evaluation of parameters can be done to provide accurate analysis. Better
algorithms can be designed to get better results about the prediction of prices.
 A feature of audio searching with voice recognition can also be implemented to make
system better and make it easy to go for the users.
 Furthermore, property area ranking can also be shown twice in every month. Such
feature will create a better idea in mind of the customer about which area is to be
selected for investment.
 Features that include Stalk Property and Comparison of two or more properties can be
added to make the customers aware about which property is best out of both and to
keep track of the properties they like.

44. Conclusion
 In conclusion, it can be foreseen that if the project is implemented, it is
going to be of much help the people who would like to analyze the market
of real estate by oneself. The features would avail the user to know the
trend and thus come to a decision about the property statistics. Though
many other systems would be competing this system, but once the
limitations are eliminated or the future enhancements are implemented it
would be one of the best in the segment of asset market.

45. Bibliography/References
Books:
 Object oriented Analysis and Design
using UML by
 Michel R. Blaha and James R.
Rambaugh
 The Complete Reference, Java 2
(Fourth Edition), Herbert Schild,
TMH.
Websites:
• www.magicbricks.com
• www.99acres.com
• www.propchill.com
• www.google.com
• www.tutorialspoint.com