IoT (Internet of things) big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics system is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time.

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IoT Big Data Analytics Insights from Patents
IoT (Internet of things) big data analytics is becoming important to process unimaginably large amounts of
information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big
data analytics system is Hadoop, an open-sourcesoftware framework that supports data-intensive distributed
applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the
architectural framework MapReduce, collects both structured data and unstructured data, processesthe collected
data set in a distributed network cluster in parallel, and extracts valuable information from the processed dataset
within a short time.
IBM patent application US20160070816 illustrates a system for processing large scale unstructured data in real -
time. The interconnected IoT sensing devices continuously generate massive information at a very high speed.
Thus a technology for effectively processing a huge amount of information in the form of a data stream in real-time
is very important. The real time big data analysis system includes a receiver for receiving streamed input data from
live data sources, a pattern generator for deriving emergent patterns in data subsets, a pattern identifier for
identifying a repeating pattern and correspondingdata subset within the emergent patterns, a compressorfor
reducing the identified data subsetand identified pattern to a compressed signature and a repository for storing the

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streamed input data with the compressed signature and without the identified data subset in which the data subset
can be rebuilt if necessary using the compressed signature.
In IoT, millions of events often generated from IoT sensors and devices. Thus, it is very important to develop real-
time data streaming and processingsystems for IoT analytics. There are several open-sourcereal-time data
streaming and processing systems are available including Apache Kafka, Storm, and Spark. Most of open-source
real-time data streaming and processing systems offer default schedulers that evenly distribute processing tasks
between the available computation resources. However, such schedulers are not costeffective because substantial
computation resources are lost during assignment and re-assignment of tasks to the correct sequence of
computation resources in the stream processing system, which results in significant latency in the system.
Salesforce.com, inc. patent application US20170075693 illustrates a costeffective improved real-time data
streaming and processingsystems for IoT analytics.
In Industrial IoT (IIoT) applications (e.g., manufacturing, oil and gas, mining, transportation, power and water,
renewable energy, heath care, retail, smart buildings, smart cities, and connected vehicles), it is not practical to
send all of that data from sensors embedded in industrial machines to cloud storage becauseconnectivity not
enough bandwidth in a costeffective way and difficulty in practical implementation of effective real-time decision
making and prediction systems. FogHorn Systems, Inc. patent application US20170060574 illustrates a real-time

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edge IoT analytics system (e.g., FogComputing) that can handle the large amounts of data generated by industrial
machines and provides intelligent edge computing platform.

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Data monetization is a business model to generate revenue from available data sources or real time streamed data
by instituting the discovery, capture, storage, analysis, dissemination, and use of the data. Data monetization
leverages data generated through business operations as well as data associated with individual actors and with
electronic devices and sensors participating in a given network. IoT can facilitate generating location data and
other data from sensors and mobile devices. Big data system enables identification, analysis, selection and
capitalization of the IoT data monetization opportunities. Data monetization value chain includes the data
producers, data aggregators, data distributors and data consumers. Data as a service (DaaS) models for transactions
involving big data can be possible.
mFrontiers, LLC patent application US20160050279 illustrate a system for operating the IoT big data analysis
service for data monetization. The system analyzes the stored data from the IoT devices in the cloud and produces
a big data analysis report. A client can purchase or sell information on the analyzed big data analysis report through
the virtual big data marketplace. The big data analysis report can include information on a preliminary analysis
report whose results vary according to analysis time or period. When a third party analyzes big data using the
information on preliminary analysis report, a writer who uploaded the information on preliminary analysis report to
the analysis report marketplace charges fees for the information.

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New Technologies & Associates, Inc. patent application US20150179079 illustrates a healthcare IoT big data
analytics system for real time monitoring of a patient's cognitive responseto a stimulus. The big data analysis of
massive data obtained by the sensing devices can provide many value-added healthcare services. The real time
monitoring system includes a mobile or tablet device, a user interface disposed onthe mobile device, sensors
monitoring user interaction with the mobile device and capturing kinesthetic and cognitive data. The real time
monitoring system also includes a processor for comparing the kinesthetic and cognitive data and comparing the
data to a baseline, and identifying relative improvement and impairment of cognition skills from the comparison
exploiting big data analytics.