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1 Introduction to Project
The goal of this project is to build a web portal which will be used to manage telephone call management and tracking information of various office.
Expected Delivery
We analyze given itemized telephone bill (CSV) data models or consolidated telephone bill (PDF) data models that can be imported/extracted data and estimated accurately from trained samples.
01.2 Purpose of the Project
There is zero cost to the customer until this system research, identify, calculate and present all the potential savings.
01.2.1 Itemized Per Minute Rate
Analyze any per minute rate program based on the electronic information supplied to
(TeT).
Review volume of calls from time spent on the telephone.
01.2.2 Local And Zone Calls
Review calling plans and rate charged for Local and Zone calls.
01.2.3 Monthly Charges & Features
Analyze the monthly charges based on the features and monthly charges.
01.2.4 Slamming And Cramming Charges
Review all Slamming and Cramming charges and send letter to the companies that is charging your telephone bills assist in recovering past dollars spent on unauthorized charges.
01.2.5 Wireless Charges
Determine which employees are routinely exceeding the allotted monthly charges.
01.2.6 Taxes Charged to Tax Exempt
Determine if taxes are charged to a tax exempt businesses and or municipalities.
Finally, an electronic method of analyzing your telephone bills, while improving the accuracy and reviewing the costs. There is “NO CHARGE” to our clients unless we save you money!! Our fees are based on a percentage of what we identify.
This platform compares your telephone bill with a number of categories to determine any overbilling, incorrect billing or unnecessary monthly features.
01.2.7 How Does It Works?
● Client sends us their past 12 months worth of telephone bills, electronically
● Copies of their telephone bill may also be sent by their service provider
● Our team downloads the telephone bills
● We synchronize the client’s telephone bills with our web portal format of information in the computer
● Our software program processes the information in the telephone bills and generates results by dashboard categories
● Our team of professional analysts review the results and identify saving opportunities
● Our team of professional analysts will provide a preliminary report and identify the possible savings by category
● Our professional consultant will review the report and along with the client determine the potential savings
● Once the client and consultant determine the amount saved, eBill 4 the future will send an invoice the future savings
● Based on the telephone bill audit and review if recovery dollars are found, we will work to recovery those dollars for a percentage
Yesterday’s methods of reviewing your monthly telephone bills are inefficient and outdated Based on the size of your company and number of bills your accounting department receives it take hours and hours to properly review all of your paper bills. Unfortunately, in some cases your telephone bills are hard to review and understand. Based on the large numbers of pages several accounting errors are missed and mistakes are made.
Today’s methods are using the latest technology. Several corporations are requesting and receiving their telephone bills electronically. This platform will match up your telephone bills electronically in six (6) categories looking for mistakes, overbilling and incorrect billing amounts.
Once this project review your telephone bill information they can make a determination if your telephone bills are correct and match up with your telephone service agreements. This process could save your corporation thousands of dollars every year.
This platform will also assist your company in receiving dollars back based on the overcharges and incorrect pricing. We will provide our services at no cost!! We will only get paid a percentage on the amount of money we save you in a 12 month period. This project works as consultants will also assist your corporation in “getting money back” for a percentage recovered based on our findings.
02. System Analysis
02.1 Introduction
We analyze given itemized telephone bill (CSV) data models or consolidated telephone bill (PDF) data models that can be imported/extracted data and estimated accurately from trained samples.
02.2 Existing System
Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data/pattern analysis.
● Exploration
● Using Business Intelligence tools Pattern identification
● Deployment
Lacking :
● Business Rules
● Analytics
● Data Visualization
● Unstructured Data Analytics
● Predictive Analytics
● RealTime Analytics
● EvidenceBased Planning
02.3 Proposed System
Nonuniform memory access is an architecture of the main memory subsystem where the latency of a memory operation depends on the relative location of the processor that is performing memory operations. Broadly, each processor in a NUMA system has a local memory that can be accessed with minimal latency, but can also access at least one remote memory with longer latency.
Advantages
● Generic Business Rules Settings
● Data Analytics
● Data Visualization
● Can Handle Unstructured Data Analytics
● Predictive Analytics is Possible
● RealTime Analytics
● EvidenceBased Planning
02.4 Input & Output
02.4.1 Input
The system can handle following form of data inputs, CSV, Excel, PDF & Online API's. From the data source the data’s will be extracted and inserted into the database table.
02.4.2 Output
Once the data’s been inserted, a process query will be executed and the results will be show in list view and graph chart view. Based on sufficient data, recommendation engine with be executed for suggesting best plans.
02.5 Requirement Specification
02.5.1 Hardware Requirement
● Pentium Dual Core Processor
● 1 GB RAM
● 200 MB Hard Disk
● Monitor/Mouse/Keyboard
● Internet Connection
02.5.2 Software Requirement
● Ubuntu 14.04
● Python 2.7.6
● Django 1.7.1
● MySQL 5.6/Postgresql 9
● HTML5 & CSS3
● Javascript & JQuery
● PDF to HTML
● Python Image Library
03. System Development Environment
03.1 Introduction to Python
Python is an interpreted, objectoriented, highlevel programming language with dynamic semantics. Its highlevel built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edittestdebug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edittestdebug cycle makes this simple approach very effective.
Comparing Python to Other Languages
Python is often compared to other interpreted languages such as Java, JavaScript, Perl, Tcl, or Smalltalk. Comparisons to C++, Common Lisp and Scheme can also be enlightening. In this section I will briefly compare Python to each of these languages. These comparisons concentrate on language issues only. In practice, the choice of a programming language is often dictated by other realworld constraints such as cost, availability, training, and prior investment, or even emotional attachment. Since these aspects are highly variable, it seems a waste of time to consider them much for this comparison.
Java
Python programs are generally expected to run slower than Java programs, but they also take much less time to develop. Python programs are typically 35 times shorter than equivalent Java programs. This difference can be attributed to Python's builtin highlevel data types and its dynamic typing. For example, a Python programmer wastes no time declaring the types of arguments or variables, and Python's powerful polymorphic list and dictionary types, for which rich syntactic support is built straight into the language, find a use in almost every Python program. Because of the runtime typing, Python's run time must work harder than Java's. For example, when evaluating the expression a+b, it must first inspect the objects a and b to find out their type, which is not known at compile time. It then invokes the appropriate addition operation, which may be an overloaded userdefined method. Java, on the other hand, can perform an efficient integer or floating point addition, but requires variable declarations for a and b, and does not allow overloading of the + operator for instances of userdefined classes.
For these reasons, Python is much better suited as a "glue" language, while Java is better characterized as a lowlevel implementation language. In fact, the two together make an excellent combination. Components can be developed in Java and combined to form applications in Python; Python can also be used to prototype
components until their design can be "hardened" in a Java implementation. To support this type of development, a Python implementation written in Java is under development, which allows calling Python code from Java and vice versa. In this implementation, Python source code is translated to Java bytecode (with help from a runtime library to support Python's dynamic semantics).
Javascript
Python's "objectbased" subset is roughly equivalent to JavaScript. Like JavaScript (and unlike Java), Python supports a programming style that uses simple functions and variables without engaging in class definitions. However, for JavaScript, that's all there is. Python, on the other hand, supports writing much larger programs and better code reuse through a true objectoriented programming style, where classes and inheritance play an important role.
Perl
Python and Perl come from a similar background (Unix scripting, which both have long outgrown), and sport many similar features, but have a different philosophy. Perl emphasizes support for common applicationoriented tasks, e.g. by having builtin regular expressions, file scanning and report generating features. Python emphasizes support for common programming methodologies such as data structure design and objectoriented programming, and encourages programmers to write readable (and thus maintainable) code by providing an elegant but not overly cryptic notation. As a consequence, Python comes close to Perl but rarely beats it in its original application domain; however Python has an applicability well beyond Perl's niche.
Tcl
Like Python, Tcl is usable as an application extension language, as well as a standalone programming language. However, Tcl, which traditionally stores all data as strings, is weak on data structures, and executes typical code much slower than Python. Tcl also lacks features needed for writing large programs, such as modular namespaces. Thus, while a "typical" large application using Tcl usually contains Tcl extensions written in C or C++ that are specific to that application, an equivalent Python application can often be written in "pure Python". Of course, pure Python development is much quicker than having to write and debug a C or C++ component. It has been said that Tcl's one redeeming quality is the Tk toolkit. Python has adopted an interface to Tk as its standard GUI component library.
Tcl 8.0 addresses the speed issuse by providing a bytecode compiler with limited data type support, and adds namespaces. However, it is still a much more cumbersome programming language.
Smalltalk
Perhaps the biggest difference between Python and Smalltalk is Python's more "mainstream" syntax, which gives it a leg up on programmer training. Like Smalltalk, Python has dynamic typing and binding, and everything in Python is an object. However, Python distinguishes builtin object types from userdefined classes, and currently doesn't allow inheritance from builtin types. Smalltalk's standard library of collection data types is more refined, while Python's library has more facilities for dealing with Internet and WWW realities such as email, HTML and FTP.
Python has a different philosophy regarding the development environment and distribution of code. Where Smalltalk traditionally has a monolithic "system image" which comprises both the environment and the user's program, Python stores both standard modules and user modules in individual files which can easily be rearranged or distributed outside the system. One consequence is that there is more than one option for attaching a Graphical User Interface (GUI) to a Python program, since the GUI is not built into the system.
C++
Almost everything said for Java also applies for C++, just more so: where Python code is typically 35 times shorter than equivalent Java code, it is often 510 times shorter than equivalent C++ code! Anecdotal evidence suggests that one Python programmer can finish in two months what two C++ programmers can't complete in a year. Python shines as a glue language, used to combine components written in C++.
Common Lisp and Scheme
These languages are close to Python in their dynamic semantics, but so different in their approach to syntax that a comparison becomes almost a religious argument: is Lisp's lack of syntax an advantage or a disadvantage? It should be noted that Python has introspective capabilities similar to those of Lisp, and Python programs can construct and execute program fragments on the fly. Usually, realworld properties are decisive: Common Lisp is big (in every sense), and the Scheme world is fragmented between many incompatible versions, where Python has a single, free, compact implementation.
03.1.1 Verifying
To verify that Python is installed, type python from your shell. Then at the Python prompt, try to execute print "Hello, World!":
03.2 The Django Platform
Django is a highlevel Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much
of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.
Ridiculously Fast
Django was designed to help developers take applications from concept to completion as quickly as possible.
Fully Loaded
Django includes dozens of extras you can use to handle common Web development tasks. Django takes care of user authentication, content administration, site maps, RSS feeds, and many more tasks — right out of the box.
Reassuringly Secure
Django takes security seriously and helps developers avoid many common security mistakes, such as SQL injection, crosssite scripting, crosssite request forgery and clickjacking. Its user authentication system provides a secure way to manage user accounts and passwords.
Exceedingly Scalable
Some of the busiest sites on the planet use Django’s ability to quickly and flexibly scale to meet the heaviest traffic demands.
Incredibly Versatile
Companies, organizations and governments have used Django to build all sorts of things — from content management systems to social networks to scientific computing platforms.
Install Python
Being a Python Web framework, Django requires Python. See What Python version can I use with Django? for details. Python includes a lightweight database called SQLite so you won’t need to set up a database just yet.
Get the latest version of Python at https://www.pythondownload/ or with your operating system’s package manager.
You can verify that Python is installed by typing python from your shell; you should see something like:
Set up a database
This step is only necessary if you’d like to work with a “large” database engine like
PostgreSQL, MySQL, or Oracle. To install such a database, follow the below steps.
Install Django
Select "terminal" launchpad and enter "pip/easy_install" and execute any of these following command
Install by pip
pip install Django==1.7.1
Install by easy_install easy_install Django==1.7.1
Verifying
To verify that Django can be seen by Python, type python from your shell. Then at the Python prompt, try to import Django:
>>> import django
>>> print(django.get_version())
03.3 Nginx Web Server
Powering half of the world’s busiest sites, NGINX is the heart of the modern web. We help you deploy and deliver your sites and apps with performance, reliability, security, and scale through our complete application delivery platform.
Load Balancing: Scalable Traffic Management with NGINX Plus
Load balancing with NGINX Plus gives you the control you need to manage and scale your web and mobile applications. We offer a complete, softwarebased application delivery platform that load balances HTTP and TCP applications at a fraction of the cost of hardware solutions. Maximize the availability and reliability of your site and applications and minimize disappointed customers and lost revenue.
High Availability & Health Checks
Applicationaware health checks and robust monitoring keep you informed of issues. NGINX Plus detects and works around these issues to significantly improve the availability of your applications.
Deploy Anywhere
NGINX Plus is a softwarebased load balancing solution that you can easily deploy in almost any environment. You have one easy solution, whether load balancing on your own hardware or in the cloud.
Session Persistence & Routing
NGINX Plus manages session persistence in several ways, including cookie insertion and sticky routes. Easily manage traffic for optimal performance without disrupting the user experience.
Massive Performance Improvements
NGINX is the recognized leader in highperformance application delivery. NGINX Plus enables you to deliver applications to 10x as many users, reduce infrastructure costs, and increase site traffic, customer satisfaction, conversions, and revenue.
No Performance Limits; No Proprietary Hardware
NGINX Plus is a complete software solution available at a fraction of the cost of legacy hardware load balancers and application delivery appliances. Leverage your commodity hardware or cloud infrastructure with no limitations on throughput or connections.
Simple to Deploy and Manage
NGINX Plus’ configuration language is flexible, logical, and easily scalable. It’s also easy to automate deployment and configuration with tools like Puppet and Chef, so you can avoid timeconsuming maintenance work.
Content Caching with NGINX Plus
NGINX Plus is used as a content cache, both to accelerate local origin servers and to create edge servers for content delivery networks. Caching can reduce the load on your origin servers by a huge factor, depending on the cacheability of your content and the profile of user traffic.
NGINX Plus can cache content retrieved from upstream HTTP servers and responses returned by FastCGI, SCGI, and uwsgi services.
NGINX Plus is the web server, reinvented
NGINX Plus is the HTTP operating system for the modern web application. Whether you’re delivering content, streaming video or audio, or deploying complex web services, NGINX Plus is the optimal platform to connect your users to your applications.
The highperformance, efficient HTTP processing engine in NGINX Plus handles desktop, mobile, and API traffic equally well before switching and routing each request to the correct service. Companies deploy NGINX Plus to manage the complexities and pitfalls associated with HTTP and to make their web applications more responsive, scalable, fast, and secure.
Defend Your Applications with NGINX Plus
Securing a web application is not just about protecting your data, but also means keeping your website running in the face of malicious traffic. NGINX Plus combines both into one software package to provide comprehensive protection for your sites and apps.
03.4 MySQL Server
MySQL is an open source relational database management system (RDBMS) based on Structured Query Language (SQL).
MySQL runs on virtually all platforms, including Linux, UNIX, and Windows. Although it can be used in a wide range of applications, MySQL is most often associated with webbased applications and online publishing and is an important component of an open source enterprise stack called LAMP. LAMP is a Web development platform that uses Linux as the operating system, Apache as the Web server, MySQL as the relational database management system and PHP as the objectoriented scripting language. (Sometimes Perl or Python is used instead of PHP.)
MySQL, which was originally conceived by the Swedish company MySQL AB, was acquired by Oracle in 2008. Developers can still use MySQL under the GNU General Public License (GPL), but enterprises must obtain a commercial license from Oracle.
Offshoots of MySQL are called forks. They include:
Drizzle – a lightweight open source database management system in development based on MySQL 6.0.
MariaDB – a popular communitydeveloped "dropin" replacement for MySQL that uses MySQL APIs and commands.
Percona Server with XtraDB– an enhanced version of MySQL known for horizontal scalability.
03.5 Big Data
On Theory
Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
On Expert’s View
Big data is highvolume, highvelocity and highvariety information assets that demand costeffective, innovative forms of information processing for enhanced insight and decision making.
On Real Time
Big data supports both structured and unstructured data – that inundates a business on a daytoday basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Widely used in,
● Health Care Applications
● Cloud Applications
● Internet of Things (IoT) Applications
● Social Networking Applications
● many more...
Technique/Methods
● Unstructured Data Analytics
● Predictive Analytics
● RealTime Analytics
● EvidenceBased Planning
04. Literature Survey
04.1 Modeling the Probability of a Strikeout for a Batter/Pitcher Matchup
We analyze models for predicting the probability of a strikeout for a batter/pitcher matchup in baseball using player descriptors that can be estimated accurately from small samples. We start with the log5 model which has been used extensively for describing matchups in sports. Log5 is a special case of a logit model and we use constrained logistic regression over nearly one million matchup observations to assess the use of the log5 explanatory variables for this application. We also show that a batter/pitcher ground ball rate interaction variable is significant for the prediction of strikeout probability and we provide physical justification for the inclusion of this variable in the model. We quantify the differences among the models and show that batters control the majority of the variance in predicted strikeout rate. Authored by,
Glenn Healey, Fellow, IEEE VOL. 27, NO. 9, SEPT 2015
04.2 InMemory Big Data Management and Processing: A Survey
Growing main memory capacity has fueled the development of inmemory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, inmemory systems are much more sensitive to other sources of overhead that do not matter in traditional I/Obounded diskbased systems. Some issues such as faulttolerance and consistency are also more challenging to handle in inmemory environment. We are witnessing a
revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of inmemory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient inmemory data management and processing.
Authored by,
Hao Zhang, Gang Chen, Member, IEEE, Beng Chin Ooi, Fellow, IEEE, KianLee Tan, Member, IEEE, and Meihui Zhang, Member, IEEE
VOL. 27, NO. 7, JULY 2015
05. System Design
This is an overall view of the relationship between telephone ebill call management system and other components. The main call management system’s application is deployed on the Django web server. It uses the builtin Sqlite3 database that comes with Django. This can easily be migrated to other Django supported databases such as Mysql/PostgreSql. There are two types of commands: create and destroy a specific resource such as virtual host, and start and stop a specific call management, e.g., by creating user accounts on physical host. The testbed application also invokes web API on the call management system to get more information about the resource or telephone call management tracking as needed.
The administrator can login to the ebill telephone call management system to create new company and manage user settings also assign a company. Once a user get activated and assigned to a company. They are now allowed to login into system and access all the information about their company’s telephone bills and it’s featured data. Also they are allowed to upload ebills and be tracking the usage.