NetView 360 Product Description

NetView 360 Product Description

Heterogeneous network (HetNet) planning is a specialized process that should not be thought of as

adaptation of the traditional macro cell planning process. The new approach must accurately identify

issues in existing macro cell networks and seamlessly predict how problem areas can be resolved by

deploying new components such as indoor small cells, outdoor pico cells and Wi-Fi access

points. Vedicsoft in partnership with ClearSky Technologies supports and markets ClearSky’s NetView

360 solution. NetView360 is a new generation, proprietary software tool and planning service that

combines big data sources for advanced traffic analytics, high-resolution propagation modeling and

business case analysis to deliver a HetNet design with unprecedented detail and accuracy. NetView 360

is a unique, scalable service that can be used to cost-effectively plan entire cities at ultra-high resolution

within weeks.

Industry experience has revealed that small cells are critical to solving complex network capacity and

coverage issues, yet the challenge lies in designing an optimal plan for peak performance. Today’s

HetNets are a combination of macro cells, indoor small cells, outdoor small cells, and potentially, Wi-Fi

access points. A complete solution may require hundreds, if not thousands, of small cells, and the

successful design must be completed quickly and cost-effectively while addressing issues related to

precision, granularity, and resolution. Plus, operators must be able to model and design all solutions on a

single platform that can accurately assess the impact on the entire HetNet.

Many operators today still rely on RF design technology that uses low-resolution inputs and assumptions.

While this approach has worked well for macro cell network design, it is not applicable to small cell

planning. Incorporating small cells into a macro network requires careful attention to a variety of

challenges, including building size and construction, interference, backhaul options, and unprecedented

mapping precision. Achieving the objectives is highly unlikely if these factors are not considered.

So how does NetView 360 work, and what differentiates it from traditional design tools? First, in addition

to using operator network data to identify optimal locations for small cells, NetView 360 incorporates big

data sources and advanced analytical engines, plus actual smart phone use, to generate indoor and

outdoor traffic maps with 4-meter resolution. The NetView 360 platform accomplishes this without

placing any actual equipment within an operator’s network. The result includes not only data demand, but

also other crucial information such as user locations, user density, and even the type of device used.

 

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After examining traffic in the network, NetView 360 analyzes existing network parameters such as

coverage, capacity, and quality of service. After characterizing the baseline network, NetView 360

analyzes all available options and recommends the best solution, whether it is indoor small cells, outdoor

small cells, Wi-Fi, or a combination thereof.

NetView 360 then evaluates all possible backhaul and real estate attachment locations. Using a powerful

proprietary combinatorial analysis, NetView 360 determines which backhaul and attachment points (or

street furniture) will provide the best time-to-market, performance, and total cost of ownership. In

addition, the platform can be programmed to include custom attributes such as cost/time to install fiber,

zoning, permitting, installation and deployment. The output is a comprehensive technical and business

analysis for each attachment point that outlines the tradeoffs between the different options.

Traditional macro planning includes a significant leeway for error that is typically resolved after the fact

through drive-testing and optimization. With small cells, there is really no opportunity for optimization—

either the small cells meet the coverage or capacity objective, or they don’t. Therefore, the reliance on

radio planning for small cells is much more critical than with traditional macro planning.

Once the backhaul solution is complete, NetView 360 performs detailed RF design and planning, and

analyzes the impact of the proposed deployments. In order to perform very precise indoor and outdoor

modeling, NetView 360 uses a proprietary GIS database and propagation model engineered to address

specific issues that are unique to HetNet design. Typically, high-resolution datasets such as 3D building

models and 5-meter RF modeling clutter/land use sets are purchased from third-party GIS vendors.

NetView 360 does away with the high cost and delay of acquiring these sets by internally generating a full

1-meter 3D building model and a sub meter (0.3m) clutter dataset. The creation of these datasets is

accomplished in coordination with the proprietary NetView 360 indoor/outdoor 1-meter propagation

model. This critical step ensures that the resulting RF propagation analysis is extremely accurate and

capable of modeling not only macro cells, but also indoor and outdoor small cells, on the same platform.

The NetView 360 design process does not require additional external tools such as specific “indoor only”

or “outdoor only” components. Not only does NetView 360 create the propagation analysis, but it also

performs detailed capacity offload/onload simulations to quantify the small cell impact. In addition, the

platform creates a detailed indoor assessment and analysis to determine the radio conditions and traffic

demand in every building within the design footprint.

Finally, NetView 360 ties everything together with a business and tradeoff analysis. The tradeoff analysis

quantifies any attribute of a potential solution (e.g., coverage, capacity, time to market, capital

expenditure, or operational costs), and shows advantages and disadvantages of one potential solution

compared with another. This arms operators with the information they need to make informed decisions

about which option will best fulfill their objectives.

In conclusion, NetView 360 is the first platform developed exclusively to design and plan a true

heterogeneous network. Its revolutionary design is characterized by a unique combination of technical

and business analytics, imbedded data tools, advanced RF modeling and intelligent algorithms. The

platform provides a quick, cost-effective way to design and organize entire networks with extreme

accuracy and scalability, enabling operators to fully realize the promise of small cells.

 

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Vedicsoft, in partnership with Ensatus, offers Insta PAT —solution to

maximize revenue for Wireless Operators (Telco)

Big Data Solution Framework

 

Solution offered by Vedicsoft –Zinnia Systems partnership

Big data sources

Conventionally, data set which cannot be conveniently handled using RDBMS software are thought of Big Data. Big data arises obviously from transaction based systems like e- commerce operations, social networks, telephone calls, news item storing systems, gaming sites etc. Big data also arises where reference data are searched but reference data itself is very big. As for example, public photo storage sites search over very large data. Big data is also encountered simply where services are provided to millions of users like Skype etc. Since, every data bit gleaned from web / transaction has become valuable as it means something based on context and data usage is set to explode exponentially, it seems that even mid-size companies will be grappling with big data in near future.

Big data adoption speed

Big data adoption speed is very encouraging. Though it has not become main stream, IT teams in banks, telecom operators, e-commerce operators, social networks have started working with big data for their non-critical systems. As for example, customer experience solution vendors for telecom operators have started coming out with products based on big data. Gaming & search companies are already using these for long time. Enterprise customers in BFSI, manufacturing, travel etc. are still at experimentation stage.

Big data adoption challenges

It is noticed that, with some inconvenience, big data can still be handled by having multiple instances of RDBMS. However, as we know, these increases cost and typically thought of as a temporary solution until big data methods get mature. As for example, billions of telephone records (per day) are still maintained in RDBMS. Typically, big data implementation impediments are as follows:

1. Risk of changes in critical systems and associated cost 2. Lack of availability of in-house resources on

big data

3. Change over from RDBMS to big data infrastructure is not simple and mechanical

4.

Faith on big data infrastructure since benchmarks have just started coming

Our big data offerings

Our big data offerings are three-fold: 1. Big data infrastructure products 2. Big data solutions

3. Big data training

In addition, we are traditionally strong in integration with social network data.

Infrastructure products

are part of an integrated suite, named Nectar, to cover big data processing requirements end-to-end. Products automate data collection, storage,

Big Data Solution Framework

 

 

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Solution offered by Vedicsoft –Zinnia Systems partnership

treatment, aggregation and analytics for insight extraction. Nectar also includes a visualization product to enable interactive analysis and drill- down on big data in real-time. A brief of Nectar components are provided below for reference.

Data Stream Orchestrator (DSO) is an aggregation of software components to collect, validate, transform, enrich, and store information from multiple sources in real-time. Through a designer based UI framework, it drives data preparation and ensures that required information elements are made available in time and in appropriate format.

Hadoop Query Planner (HQP) provides a web based software environment where queries and scripts can easily be created and maintained. It encapsulates query execution complexities within itself and simply provides an intuitive UI based environment much like the tools common RDBMS provides, including Hive query creation and scheduling, Pig script creation and scheduling, and Job status tracking.

Holistic Predictive Modeler (HPM) provides an environment for performing data treatment, modelling, training, validation & scoring to solve advanced algorithm based forecasting & classification problems. HPM supports many off- the-shelf machine learning algorithms including Clustering: K-means; classification: Logistic Regression, Naive Bayes; Forecasting: Multiple Linear Regression; Dimensionality Reduction: Principal Component Analysis; Recommendation: Collaborative Filtering. Business Operations Center (BOC) is an interactive analysis and dashboard generation tool. It provides many statistical visualizations off-the-shelf including Andrews curve, parallel co-ordinates plot, histograms, tree map, hi-lo plot etc.

Big data solutions

typically use any or all of the infrastructure products to solve customer specific problems with specific UI flow. Solutions provide additional visualizations, adaptations to the generic algorithms, integration with customer specific data sources and work flows suited to the problem at hand. In essence, solution offering is suitable for you if you want a business problem to be solved end-to-end while leaving modeling, integration and UI flow to us. End result is a complete solution. List below provides some of the areas where we have worked with customers to provide end-to-end solution.

1. Telecom

a. Call centre data analysis for FCR, abandon call analysis, average talk time etc. for telecom operators

b. CDR analysis for an internet data compression service provider

2. Retail

a. Customer segmentation for retail

b. Customer selection for catalog delivery for retail c. Product recommendation for retail

3. Bank

a. Customer perception analysis using text mining for banks

b. Property price prediction for banks 4. Social

a. Twitter feed analysis for sentiment extraction

b. LinkedIn data integration for a data mining vendor 5. Miscellaneous

a. Movie recommendation for DVD rental companies b. Topic search across past Journals for news

aggregation companies

Big Data Solution Framework

 

Solution offered by Vedicsoft –Zinnia Systems partnership

Training

is meant to equip your team with knowledge about tools used in big data handling, development of parallelized algorithms, distributed storage handling, cluster creation and maintenance, performance of queries on big data etc. Depending on your need you can opt for trainings as mentioned below. We are one of the very early adopters of big data and hence come with very strong back ground on big data knowledge base, especially implementation of machine learning algorithms on big data. Teams in majority of the software services companies in India are trained by us.

1. Big data overview – 1 day

2. Hadoop or Spark developer training – 3 days

3. Advanced Hadoop or Spark developer training – 5 days 4. Advanced training on statistical methods using big data – 5

days

Big Data Solution Framework

 

 

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Solution offered by Vedicsoft –Zinnia Systems partnership

Advantages of Nectar based solution

Nectar provides a simple to use and technologically sophisticated environment to enable your business analysts to model machine learning problems and brings following business benefits:

• Lowest total cost of ownership compared to traditional offerings from data mining vendors

• Statistical and mathematical details are encapsulated within the tool itself. Allows business analysts with very little analytics background to model required business problems and receive domain specific recommendations.

• Once made, models can be reused for different problems and with different dataset

• Allows analysts to choose from among a number of prediction algorithms based on model fit statistics • Data treatment rules are user configurable and can be switched on/off depending on requirements in hand • Solution backed up by knowledgeable team at every step of engagement with you

• Intelligent enough to adopt memory based processing or distributed storage based processing depending upon the dataset volume. • Uses distributed computing and distributed storage based on Hadoop and Spark ecosystem for massive scalability and simple growth.