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Big Data And The Internet Of Things

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bigdata

The data that is derived from the Internet of Things may easily be used to make analysis and performance of equipment as well as do activity tracking for drivers and users with wearable devices. But provisions in IT need to be significantly increased.Intelligent Mechatronic Systems(IMS) collects on an average data points no fewer than 1.6billion on a daily basis from automobiles in Canada and U.S.

Deep Learning and AI using Python

The data is collected from hundreds of thousands of cars that have on board devices tracking acceleration, the distance traversed, the use of fuel as well as other information related to the operation of the vehicle.This data is then used as a means of supporting insurance programs that are based on use.Christopher Dell, IMS’s senior director recently stated they they were aware that the data available were of value, but what was lacking is the knowledge on how to utilize it.

But in the August of 2015, after a project that lasted for a year, IMS added to its arsenal a NoSQL database with Pentaho providing tools related to data integration and analytics. This lets the data scientists of the company increased flexibility to format the information. This enables the team of analytics to make micro analysis of the driving behavior of customers so that trends and patterns that might potentially enable insurers to customize the rates and policies based on usage.

In addition to this the company further is pursuing an aggressive growth policy through asmartphone app which will further enhance its abilities to collect data from vehicles and smart home systems making use of the Internet of Things.Similar to the case of IMS, organizations that look forward to analyze and collect data gathered from the IoT or the Internet of Things but often find that they need an upgrade of their IT architecture. This principle applies to enterprise as well as consumer sides of the IoT divide.

The boundaries of business increasingly fade away as data is gathered from fitness trackers, diagnostic gears, sensors used in industries, smartphones. The typical upgrade includes updating to big data management technologies like Hadoop, the processing engine Spark,NoSQL databases in addition to advanced tools of analytics with support for applications drivenby algorithms. In other cases all it is needed for the needs of data analytics is the correct combination of IoT data.

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The Role of Big Data in the Largest Database of Biometric Information

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BIG DATA

Aadhaar project from our very own India happens to on the most ambitious projects relying on Big Data ever to be undertaken. The goal is for the collection, storage and utilization of the biometric details of a population that has crossed the billion mark years ago. It is needless to say that a project of such epic proportions presents tremendous challenges but also gives rise to an incredible opportunity according to MapR, the company that is serving the technology behind the execution of this project.

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Aadhaar is in its essence a 12 digit number assigned to a person / an individual by the UIDA , the abbreviated form of “Unique Identification Authority of India” The project was born in 2009 and had former Infosys CEO and co-founder Nandan Nilekani as its first chairman and the architect of this grand project which needed much input in terms of the tech involved.

The intention is to make it an unique identifier for all Indian citizens and prevent the use of false identities and fraudulent activities. MapR which is head-quartered in California is the distributor and developer of “Apache APA +0.00% Hadoop” has been putting into use its extensive experience in integrating web-scale enterprise storageand real-time database tech, for the purposes of this project.

According to John Schroeder who is the CEO and co-founder of MapR, the project presents multiple challenges including analytics, storage and making sure that the data involved remains accurate and secure amidst authentications that amount to several millions over the course of each passing day.Individual persons are provided with their number and a iris-scan or fingerprint is taken so that their identity might be proved and queried to and matched from the database backbone to a headshot photo of the person. Each day witnesses over a hundred million verifications of identity and all this needs to be done in real-time in about 200 milliseconds.

India has a percentage of rural population many of which are yet to be connected to the digital grid and as Schroeder continues the solution had to be economical and be reliable even under low bandwidth situations and technology behind it needed to be resilient which would work even with areas with low levels of connectivity.

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Source: Forbes

 

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The Pros and Cons of HIVE Partitioning

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The Pros and Cons of HIVE Partitioning Hive organizes data using Partitions. By use of Partition, data of a table is organized into related parts based on values of partitioned columns such as Country, Department. It becomes easier to query certain portions of data using partition.

Partitions are defined using command PARTITIONED BY at the time of the table creation.

We can create partitions on more than one column of the table. For Example, We can create partitions on Country and State.

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Syntax:

CREATE [EXTERNAL] TABLE table_name (col_name_1 data_type_1, ….)

PARTITIONED BY (col_name_n data_type_n , …);

Following are features of Partitioning:

  • It’s used for distributing execution load horizontally.
  • Query response is faster as query is processed on a small dataset instead of entire dataset.
  • If we selected records for US, records would be fetched from directory ‘Country=US’ from all directories.

Limitations:

  • Having large number of partitions create number of files/ directories in HDFS, which creates overhead for NameNode as it maintains metadata.
  • It may optimize certain queries based on where clause, but may cause slow response for queries based on grouping clause.

It can be used for log analysis, we can segregate the records based on timestamp or date value to see the results day wise / month wise.

Another use case can be, Sales records by Product –type , Country and month.

 

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Big Data and the Cloud- An Eclectic Mix

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Big Data and the Cloud- An Eclectic Mix
The FINRA or The Financial Industry Regulatory Authority, Inc. makes analysis of up to no less than 75 billion events each and every day. It is little wonder then that it finds its data center nearly filled to capacity. FINRA is looking forward to migrating to the cloud in order to continue to provide the protection for investors and continually respond to the market that it is famed for.

According to Matt Cardillo who is the Senior Director at FINRA, they are eyeing the elasticity that is enabled by cloud storage. He further continued also on their radar was an approach change in order to respond to market and volume data change along with changes in the behavior of users. Volatile markets result in usage spikes and also attract a whole lot more of users in their system.

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The surveillance program undertaken by FINRA performs analysis of data for suspicious activities as well as potential fraud. Their algorithms go through and analyze the data for any abnormalities or activities that might not be normal. They have in place alerts and exceptions that take stock of situations and then have access to analytics that help to determine if there is indeed a problem or whether it is a false call.

Stay Ahead of the Big Data Curve

Almost every day a new tool emerges to take stock of Analytics in the brave new world of Big Data Tech. According to Cardillo the kudos for staying ahead of the big data curve goes to the skilled staff at FINRA. He says that his people are innovative and are only too keen to embrace the latest advancements in technology. He confesses that after reverting to the cloud some of their present tech as well as tools will become irrelevant. But they are banking big on open source especially frameworks like Hive, Hadoop and Spark to get most out of the elasticity needed by their business.

 

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The Rise of the AI in Big Data

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The Rise of the AI in Big Data

The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.

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Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.

To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.

The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.

The analysis of Big Data and Engineering of Features

As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.

This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

The ultimate destination for all your data-related queries and assistance is DexLab Analytics. Being a premier Data Science training institute Gurgaon, DexLab Analytics takes pride in offering excellent data analytics courses for aspiring candidates.

 

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