International Journal of Research In Science & Engineering
Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280
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An ERP solution “Realties” for Real Estate & Construction Business to resolve all the burning issues related to industry whether it is Multi Location Inventory, Man Power Deployment and Material Billing, Multiple Project
Monitoring, Sub Contracting, Financial Management etc. “Realties” is being developed to allow Project
Managers, Accountants, a Management tool that provides integrated visibility. The result is Organizational competence and improved viability.
Cloud, Inventory, Purchase, Vendor, Sales, Billing, etc.
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Real Estate Business demands to manage many different things at a time. Managers need to use many different software for managing different areas such as inventory management, tally and etc. Realties is a proposed integrated software 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.
The objective is to make the entire operations of the company system driven, rather than personnel driven. With the rapid growth of infrastructure sector in India, the real-estate companies are facing enormous pressure in retaining specialized manpower and streamlining the operations. “Realties” ensures uninterrupted working of the operations, even in such high mobility environment.
This specification defines a lossless compressed data format that pads data using a combination of the
LZ77 algorithm and Huffman coding, with efficiency comparable to the best currently available general purpose compression methods. The data can be produced or consumed, even for an arbitrarily long sequentially presented input data stream, using only an apriori bounded amount of intermediate storage. The format can be implemented readily in a manner not covered by patents. The purpose of this specification is to define lossless compressed data format that:
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Is independent of CPU type, operating system, file system, and character set, and hence can be used for interchange;
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Can be produced or consumed, even for an arbitrarily long sequentially presented input data stream, using only an a priori bounded amount of intermediate storage, and hence can be used in data communications or similar structure such as Unix filters;
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Compresses data with efficiency comparable to the best currently available general-purpose compression methods, and in particular considerably better than the “compress” program;
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Can be implemented readily in a manner not covered by patents, and hence can be practiced freely;
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
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Is compatible with the file format produced by the current widely used gzip utility, in that conforming decompressors will be able to read data produced by the existing gzip compressor.
The data format defined by this specification does not attempt to:
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Allow random access to compressed data;
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Compress specialized data (e.g., raster graphics) as well as the best currently available specialized algorithms.
The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are:
insertion: cot → coat
deletion: coat → cot
substitution: coat → cost
These three operations may be generalized as forms of substitution by adding a NULL character (here symbolized by *) wherever a character has been deleted or inserted:
insertion: co*t → coat
deletion: coat → co*t
substitution: coat → cost
Some approximate matchers also treat transposition, in which the positions of two letters in the string are swapped, to be a primitive operation. Changing cost to cots is an example of a transposition.[2]
Different approximate matchers impose different constraints. Some matchers use a single global unweighted cost, that is, the total number of primitive operations necessary to convert the match to the pattern. For example, if the pattern is coil, foil differs by one substitution, coils by one insertion, oil by one deletion, and foal by two substitutions. If all operations count as a single unit of cost and the limit is set to one, foil, coils, and oil will count as matches while foal will not.
Other matchers specify the number of operations of each type separately, while still others set a total cost but allow different weights to be assigned to different operations. Some matchers permit separate assignments of limits and weights to individual groups in the pattern.
One possible definition of the approximate string matching problem is the following: Given a pattern string P = p_1p_2...p_m and a text string T = t_1t_2\dots t_n, find a substring T_{j',j} = t_{j'}\dots t_j in T, which, of all substrings of T, has the smallest edit distance to the pattern P.
A brute-force approach would be to compute the edit distance to P for all substrings of T, and then choose the substring with the minimum distance. However, this algorithm would have the running time O(n3 m).
A better solution, which was proposed by Sellers [3], relies on dynamic programming. It uses an alternative formulation of the problem: for each position j in the text T and each position i in the pattern P, compute the minimum edit distance between the i first characters of the pattern, P_i, and any substring T_{j',j} of T that ends at position j.
For each position j in the text T, and each position i in the pattern P, go through all substrings of T ending at position j, and determine which one of them has the minimal edit distance to the i first characters of the pattern P. Write this
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 minimal distance as E(i, j). After computing E(i, j) for all i and j, we can easily find a solution to the original problem: it is the substring for which E(m, j) is minimal (m being the length of the pattern P.)
Computing E(m, j) is very similar to computing the edit distance between two strings. In fact, we can use the
Levenshtein distance computing algorithm for E(m, j), the only difference being that we must initialize the first row with zeros, and save the path of computation, that is, whether we used E(i − 1,j), E(i,j − 1) or E(i − 1,j − 1) in computing E(i, j).
In the array containing the E(x, y) values, we then choose the minimal value in the last row, let it be E(x2, y2), and follow the path of computation backwards, back to the row number 0. If the field we arrived at was E(0, y1), then
T[y1 + 1] ... T[y2] is a substring of T with the minimal edit distance to the pattern P.
Computing the E(x, y) array takes O(mn) time with the dynamic programming algorithm, while the backwardsworking phase takes O(n + m) time.
A hash function is any function that can be used to map data of arbitrary size to data of fixed size. The values returned by a hash function are called hash values, hash codes, hash sums, or simply hashes. One use is a data structure called a hash table, widely used in computer software for rapid data lookup. Hash functions accelerate table or database lookup by detecting duplicated records in a large file.
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Centralized data storage which will be easier to integrate and use for any user.
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Organizational efficiency and improved profitability.
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Resolves all the burning issues related to industry whether it is Multi Location Inventory, Man Power
Deployment and Material Billing, Multiple Project Monitoring, Sub Contracting, Financial Management etc.
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For Constructors
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Subcontractors
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Inventory Management
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Financial Management
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Project Management
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299 p-ISSN: 2394-8280
Fig 1: Architecture Diagram
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Fig 2: State Machine Diagram

International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299 p-ISSN: 2394-8280
Fig 3: Data Flow Diagram
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Fig 4: Class Diagram

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 different software's for individual task. It is being developed to allow Project Executives, Bookkeepers, a
Management tool that provides integrated discernibility. The result is organizational efficiency and improved productivity.
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.
[1] Eric Chan (2012). “Management Perspective for TQM Solution” . Science and Engineering Publishing
Company.
[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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