The Asset Consultancy_PPT _final
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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.
The Asset Consultancy_PPT _final
- 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:
- 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:
- 18. Level-2:
- 19. Level-2:
- 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:
- 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
- 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