International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
REALTIES – CLOUD-BASED REAL ESTATE INVENTORY
MANAGEMENT SYSTEM
Shubham S. Kale1, Riya Patankar2
1
Student, Computer Department, DYPIEMR, shubham.kale27@gmail.com
Student, Computer Department, DYPIEMR, riya.patankar24@gmail.com
2
ABSTRACT
“Realties” is an ERP solution for Construction Business & Real Estate for resolving all the sweltering issues
related to industry whether it is Material Billing, Man Power Deployment, Multi Location Inventory, Sub
Contracting, Financial Management, Multiple Project Monitoring etc. “Realties” is developed for people in
different arenas such Accountants, Project Superiors, to provide them with a Management tool that offers
integrated visibility. The result of this tool will be Organizational Effectiveness and Improved Productivity.
Keywords: Cloud, Inventory, Purchase, Vendor, Sales, Billing, etc.
----------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION
With the trending Real Estate business, the demand to manage many different things at a time have increased
drastically. The Managers need to use various software for managing different areas under the project, such as
inventory management, tally and etc. Realties is a proposed integrated system which provides functionalities of all
the different software’s in one. This makes business easier and a personnel driven approach can be converted to
system driven approach by streamlining the business procedure.
2. OBJECTIVES
To provide a system driven approach to that companies which have been depending on personnel for getting the
work done. With the increasing attention to the infrastructure sector in India, real-estate companies are fronting
massive burden in retaining expert manpower and streamlining the operations. This system ensures continual
working of the real estate operations, even in such high mobility environment.
2.1 ALGORITHMS:
A. DEFLATE ALGORITHM - File Compression
This defines a lossless compacted data format that compresses the data using a combination of LZ77
algorithm and the Huffman’s coding algorithm, with the efficiency comparable to the currently best available
general purpose compression methods. The data can be consumed or produced, even for an indiscriminately long
successively presented input data stream, using only an apriori bounded amount of intermediate storage. The format
can be easily implemented in a manner not covered by the patents. The purpose is to define lossless compressed data
format that:
 Is not at all bounded by the CPU type, the operating system used, file system, and character set, and hence
can be used for interchange;
 Can be consumed or produced, even for an arbitrarily long sequentially presented input data stream, and
hence can be used in data communications or similar structure such as Unix filters;
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
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e-ISSN: 2394-8299
p-ISSN: 2394-8280
Compresses the data with efficiency which is comparable to the currently best available compression
methods, and in particular considerably better than the “compress” program;
Can be easily implemented in a manner which is not covered by patents, and hence can be practiced freely;
Its provides compatibility with the file format produced by the widely used gzip utility, in that conforming
decompressors will be able to read data produced by the existing gzip compressor.
The data format defined, does not attempt to:
 Facilitates random access to the compressed data;
 Compress the best currently available specialized algorithms along with the specialized data (e.g., raster
graphics).
B. NATURAL ALGORITHM - String Comparison
The confidence of a match is measured in terms of the number of the primitive operations required to convert the
string into an exact match. This number gives us the edit distance between the string and the pattern. The usual basic
operations are:
insertion: cot → coat
deletion: coat → cot
substitution: coat → cost
These operations may be generalized as forms of substitution by addition of a NULL character (here denoted by *)
wherever a character has been inserted or deleted:
insertion: co*t → coat
deletion: coat → co*t
substitution: coat → cost
Some estimated matchers also treat transposition, in which the positions of two letters in the string are exchanged, to
be a basic operation. An example of a transposition can be given as Change in cost.
Different constraints are imposed by different approximate matchers. Few matchers use a single global unweighted
cost, which is, the total number of basic actions that are needed to convert the match to the pattern. For example,
suppose the pattern is COIL, FOAL by two substitutions, FOIL differs by one substitution, OIL by one deletion, and
COILS by one insertion. If all actions amount as a single unit of cost and the limit is set to one, FOIL, OIL, and
COILS will amount as matches while FOAL will not be amounted as a match.
Supplementary matchers state the number of actions of each type distinctly, while still others set a total cost but
allow unlike weights to be allocated to unlike actions. Particular matchers allow distinct assignments of limits and
weights to discrete collections in the pattern.
A possible definition of the approximate string matching problem is the following:
Given a pattern string,
A = a_1,a_2...a_m and a text string B = b_1,b_2\dots b_n, find a substring B_{j',j} = b_{j'} \dots b_j in B,
which, of all substrings of B, has the smallest edit distance to the pattern A.
A brute-force tactic would be to calculate the edit distance to A for all substrings of B, and then choose the substring
with the least distance. However, this algorithm would have the running time O(n3 m).
A better answer, which was suggested by Sellers, trusts on dynamic programming. It uses an alternative construction
of the problem: for each position j in the text B and each position i in the pattern A, compute the least edit distance
amongst the i first characters of the pattern, A_i, and any substring B_{j',j} of B that ends at position j.
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
For each position j in the text B, and each position i in the pattern A, go through all substrings of B ending at
position j, and determine which one of them has the least edit distance to the i first characters of the pattern A. Write
this minimal distance as D(i, j). After computing D(i, j) for all i and j, we can easily find a solution to the original
problem: it is the substring for which D(m, j) is minimal (m being the length of the pattern A)
Computing D(m, j) is very similar to computing the edit distance amongst two strings. In fact, we can use the
Levenshtein distance computing algorithm for D(m, j), the only difference being that we must initialize the first row
with zeros, and store the path of computation, that is, whether we used D(i − 1,j), D(i,j − 1) or D(i − 1,j − 1) in
computing D(i, j).
In the array containing the D(x, y) values, we then choose the minimal value in the last row, let it be D(x2, y2), and
follow the path of calculation backwards, back to the row number 0. If the field we arrived at was
D(0, y1), then
B[y1 + 1] ... B[y2] is a substring of B with the minimal edit distance to the pattern A.
With the dynamic programming algorithm calculating the D(x, y) array takes O(mn) time, while the backwardsworking phase takes O(n + m) time.
C. HASHING ALGORITHM- Security
Any function that can be used to map data of random size to data of fixed size can be called as a Hash
Function. The hash codes, hash sums, or simply hashes are the values returned by a hash function. Hashing
Algorithm uses a data structure called a hash table, which is widely used in computer softwares for rapid
data lookup. Hash functions accelerates the search process by detecting replicated records in a large file.
2.2 ADVANTAGES:
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Centralized data storage which will be easier to integrate and use for any user.
Organizational efficiency and improved profitability.
Resolves all the issues related to real estate industry which can be Multi Location Inventory, Deployment
Man-Power and Billing of Materials, Multiple Site Monitoring, Sub Contracting, Financial Management
etc.
2.3 APPLICATIONS:
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For Constructors
Sub Contractors
Inventory Management
Financial Management
Project Management
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
2.4 ARCHITECTURE DIAGRAM:
Fig 1: Architecture Diagram
2.5 STATE MACHINE DIAGRAM:
Fig 2: State Machine Diagram
3. CONCLUSION
IJRISE| www.ijrise.org|editor@ijrise.org
e-ISSN: 2394-8299
p-ISSN: 2394-8280
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Real Estate & Construction Services will become efficient and improve profitability with the use of
Realties. It is an Integrated Product which gives all the functionalities at an instance for which earlier we had to
use a different software for every distinct task. It is being developed to allow Project Managers, Accountants, a
Management tool that provides integrated visibility. The result is organizational efficiency and improved
profitability.
ACKNOWLEDGEMENT
We might want to thank the analysts and also distributers for making their assets accessible. We additionally
appreciative to commentator for their significant recommendations furthermore thank the school powers for giving
the obliged base and backing.
REFERENCES
[1] Eric Chan (2012). “Management Perspective for TQM Solution”. Science and Engineering
Company.
Publishing
[2] Eric Chan (2013). “Australian Quantity Surveyors Utilize ICT and ERP System to Improve Efficiency”. Science
and Engineering Publishing Company.
[3] Yongjean John, Ki-Heung Yim (2001). ”A Study on an Environment of ERP”. Institute of Electrical and
Electronics Engineers (IEEE)
[4] Fabio Mulazzani, Barbara Russo, Giancarlo Succi (2009). “ERP Systems Development: Enhancing
Organization’s Strategic Control Through Monitoring Agents”.IEEE/ACIS International Conference on Computer
and Information Science.
[5] P. Deutsch, Aladdin Enterprises (1996), “DEFLATE Compressed Data Format Specification version 1.3”
[6] Bernard Chazelle, “Natural Algorithms* ”
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