How to SPEEDUP your Android Studio ?

Here are some quick trips that I have tried to speed up Android Studio on my Windows 8.1 Laptop. I hope you have a laptop with 4GB RAM because that is recommended.

1) Un-Comment  (if there are commented) or if they are missing then add these 2 lines to the project gradle.properties file (See image below)

org.gradle.parallel=trueorg.gradle.configureondemand=false 

2) Add these 2 line to the gradle properties file at the location - C:\Users\DataScience\.gradle where 'DataScience' is the user that I have logged in with to my Windows machine. If your Windows user is Jim then the path for you should be C:\Users\Jim\.gradle

3) In android studio, you can enable offline work to make android studio run faster. To enable offline in android studio, just follow following step.  In Android Studio, go to file >> setting and click Compiler from side menu and then type --offline in the command-line options box and hit OK button like this:

Tracking Game : Big Data & Logistics

How can Amazon, FedEx, Flipcart of the world leverage the huge amount of customer and logistics data to improve, optimize the process and increase profits. Do you remember the  IBM RFID TV commercial in which ' A call center attendant is sitting on the highway stops a trucker and tells him "The Boxes you are carrying have told me you are lost and you are on the wrong road". The driver replies "May be the boxes should drive the truck'. (https://www.youtube.com/watch?v=oAvQcYcvyaw ) The advertisement was about RFID technology and since newer technologies are being used in logistics world.

Today's logistics is all about
1) Visualizing, Monitoring & Optimizing Delivery Routes
2) Ensuring Timely & Accurate Delivery by real time intervention
3) Predicting Future Demand and Peak Load by analyzing data
4) Reducing Inventory and optimizing processes

Big Data can help in achieving all the above goals. Amazon uses Big Data to optimize the supply chain. By using predictive analysis based algorithm like 'Anticipatory Delivery', many time Amazon surprises us by delivering items in a very short time -  X number of popular products are prepacked and already delivered to warehouses in certain cities based on the recent trend. Bluedart uses GPS based technology chip called 'SenseAware' that provides complete detail of each shipment including parameters like temp and humidity. 

These systems are nothing but solutions based on combination of current technologies like GPS, Hadoop, Complex Event Processing, Sensor Technology, Predictive Analysis and Recommendation Engine etc. Gartners, IDB and Forresters have already projected integrated Hadoop-like-systems that will work like a framework. Multiple flavors of Hadoop are already offered by different vendors and the competition has just started. What is important is that Logistics landscape has changed and by applying technology it has become affordable to monitor, track, optimize, innovate and at the same time reduce the operating cost. Today Haddop is not an option but a integral technology for the new generation Logistics Systems.

Big Data Strategy - Smarty & Hasty Learning Series

21) Is Big Data only for large enterprise? How do small enterprises leverage Big Data?

Whether you run a small business with just a few employees or you a large multinational enterprise you can benefit from big data. The constant stream of data flowing to and from us through everyday devices and products generates more market data than ever before. The amount of data we’re producing is growing at an incredibly rapid rate. In less than five years from today (2015), experts predict that our annual data creation will be in excess of 45 trillion gigabytes. With the amount of data available to individuals, corporations, and governments, there is no question that your business needs a Big Data Strategy.

                                    When we say Big Data Strategy what is required is a focused, data-driven strategy that will not just collect information but to use that information in the most effective ways possible to help your business overcome existing business challenges but also help improve your bottom line.

 

 

To define your Big Data strategy you should first have answers to these questions.

1) What is the biggest challenge for my enterprise/business today?

2) How can I know my customer better?

3) If I have better understanding of my data will it help to address business challenges?

4) How can I use analytics to get insights about my customers and their buying and spending patterns?

 

Once you have answered these questions you know what is the expected outcome of your BD Strategy and then you shall be able to define the strategy for Big Data for your business (you might take help from an expert).  Big Data is not just for big businesses with huge amount of data and it can help small business to strategize and increase their customer base and retain customers.

 

Take this example of a family owned small company that helped tourist find cheap and comfortable accommodation with homeowners. Company helps around 700+ homeowners to generate additional income by renting their spare rooms. The small company used an analytics tool to tap into data ranging from spreadsheets to databases and make the analysis available to home owners and contractors. The predictive analysis helped company save a large amount of money by better planning and demand/supply management. The company had expected the Big Data spending to provide returns in 3 years but they realized returns in 2 years. There are many more examples and I will share few of them in coming days.

Smart City Consulting - my blogs

Smart City blog posts links-

1) What Indian government should consider for planning Smart City

2) Planning For A Smart City

20) Tackling Top Telco Fraud's with Big Data and Complex Event Processing - Part- II

I am going to share a sample architecture that will help implement solutions to prevent fraud. The architecture combines Big Data technologies with Complex Event Processing (CEP) to provide a Smarter, Proactive system along with a Dashboard that will 'show relevant fraud events or potential fraud events' which will help prevent fraud.
The high level diagram of the Big Data-Complex Event Processing solution is self explanatory and the key points to be noted are :
1) Technologies  & products implemented in sample solution
2) Real Time Architecture to handle large amount of data
2) The way data in ingested, filtered & crunched
3) The way events are 'acted on' on-the-fly
4) The operations dashboard that will provide alerts and notifications so manual checking will be reduced
5) How analytics is going to be plugged into this architecture

Please feel free to mail me if you have questions.

19) Tackling Top Telco Fraud's with Big Data and Complex Event Processing - Part- I

Those of you who have not worked for a telecom company would be surprised to learn about the 'different type of fraud' faced by the telecom industry. Frauds are biggest cause of revenue loss to telecom companies and in general the top 5 types of fraud that a telecom faces are as follows (priority might differ for different  telcom companies)-

1) Sim Card Cloning Fraud : 

Fraud happens because there is no efficient way to check cloning, CSP have to perform manual checks to detect cloning, not efficient

2) Subscription Fraud Using Fake Identity: 
Subscription fraud involves the acquisition of telecommunications services using stolen or false credentials and/or identity  with no intention of paying. With subscription fraud, service providers lose revenue.
3) Roaming Data Fraud : 
Delay in getting Customer Roaming Call Data from roaming partner to home network paves the way for fraudsters to make roaming calls resulting in financial loss for the CSP, which is categorized a roaming fraud.

4) Internal fraud activity :   
Internal manipulation of the system, Account adjustments via Voucher Administration Terminal & Tampering with billing/rating systems are frauds that can be done by people who have access to systems
 
5) Frauds related to prepaid telecom services: 
A retail agent or call center agent may attach a value-added service (VAS) to an unsuspecting subscriber. For example, a ringtone can be added without the customer’s knowledge or permission, resulting in a commission for the agent. Currently there is no effective means to analyze the data and detect such kind of fraud. 

18) Telcos, challenge of Big Data & even Bigger Challenge of Telecom Fraudsters

Big Data brings an interesting set of technologies like Hadoop, Sentiment Analytics, Predictive Analytics. Big Data also brought a new generation of solutions. The only challenge as I have mentioned before is that technology & business need to sit together to analyze and identify the 'right' use cases before investing in Big Data.
      I have implemented Complex integration solution for telcom companies and realized that telco is a world by itself. An outsider will never realize the complex working of a telco and the huge volume of data that they generate! Telcom companies have always been in forefront of adopting latest technology innovations and rightly so. The ever growing customer base & network of Telco world generates humongous amount of data. Telcos requires their software to crunch data faster, filter out the noise & process faster.  Big Data makes way for Distributed Data Processing hubs for various Telco use cases. I am listing a few use cases that I have discussed with some of the top think tanks and innovators of Telcos.

Telecommunication Companies, Fraud Prevention & Big Data

One of the biggest challenge faced by telecom companies is fraud. Telecom companies loose more money to fraud then one can imagine. The fraudster use innovative technology to commit fraud so to detect and prevent fraud we need to use Predictive Analysis coupled with Big Data Processing on the large data generated by each phone, each switch, each tower, every second.

Take a quick look at some facts -

1.Telecom fraud is estimated  at $40 B globally and it is the single biggest cause of revenue loss for operators, costing them between 3% and 5% of their annual revenue. With rising competition & extremely low average revenue per user (ARPU), detecting fraud and plugging revenue leaks have become extremely important to reduce costs.


2.One study reports that the internal fraud (40.3%), roaming fraud (11.4%), pre-paid (10.8%), subscription (11.6%) and premium (13.1%) are the most important in terms of losses by values. 

3.Fraud connected to prepaid accounts is much easier to commit and harder to combat, since there is very little information on the subscriber, unlike postpaid accounts, where a credit check is usually done. Entry-level fraudulent activities such as subscription and impersonation are very serious since the cost is coming straight from the bottom line in the form of commissions and incentives.

4.The fraud management becomes more and more important as the new methods of access become available such as Cable networks,  Wireless networks, DSL, Satellite, Metropolitan Optical Networks running Ethernet, Broadband Wireless Systems (radio, microwave, or infrared).

5.Although there is an abundance of data generated by mobile devices and systems a large amount of data is not processed in real time.

6.Telco would like to detect critical events and patterns across all its data sources in real time, perform advanced in-memory analysis in real time and take preventive or corrective action in real time to providing better service to its customer and reducing the financial looses