Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT 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 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. Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology development.

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IoT Big Data Analytics Insights from Patents
Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT
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 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.
Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology
development.
US20140297826 (SYSTEM AND METHOD FOR BIG DATA AGGREGATION IN SENSOR NETWORK;
ETRI) illustrates a system for big data aggregation in a sensor network. The most important part of the IoT big data
analytics is collecting data before storing the data, and many data collection tools based on Hadoop supports
collecting data in Hadoop Distributed File System (HDFS). HDFS is an open source for storing big data
dispersedly, that is, a technology for storing collected data reliably. The big data aggregation system includes a
sensor network which comprises many sensor nodes connected to each other over a wired/wireless network and is
configured to transfer sensor data generated by each of sensor nodes to a big data management unit by setting a

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destination address in the sensor data as an address of a big data management unit. The big data management unit
configured to distribute and dispersedly store the sensor data based on the set destination address of the sensor data.

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US20150134704 (Real Time Analysis of Big Data; IBM) 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 corresponding data subset within the emergent patterns, a
compressor for reducing the identified data subset and identified pattern to a compressed signature and a repository
for storing the 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.

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US20150179079 (MOBILE DEVICES AS NEURAL SENSORS FOR IMPROVED HEALTH OUTCOMES AND
EFFICACY OF CARE; New Technologies & Associates) illustrates a system and for real time monitoring a
patient's cognitive and motor response to a stimulus. The big data analysis of massive data obtained by the IoT
healthcare/medical devices can provide many value-added healthcare services. The real time monitoring system
includes a mobile or tablet device, a user interface disposed on the 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 comparing the kinesthetic and cognitive data and comparing the data to a baseline, and
identifying relative improvement and impairment of cognition and motor skills from the comparison exploiting big
data analytics that can be accessed locally over a WAN/LAN or in the cloud or across multiple clouds.

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US20150012502 (Big Data Centralized Intelligence System; JPMorgan Chase Bank) illustrates a system and for a
central intelligence system for managing, analyzing, and maintaining large scale, connected information systems
such as the IoT device networks. The centralized information system may receive data from servers, databases,
mainframes, processes, and other technological assets. A user is able to use the centralized information system to
run analyses on the data associated with the connected systems, including: historical analysis, real-time analysis,
and predictive modeling. The system can monitor the data and automatically correct identified errors without the
need of human intervention. The centralized information system can also generate risk management profiles and
automatically modify data to conform to the risk management profiles.

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US 20150186972 (SUMMARIZATION AND PERSONALIZATION OF BIG DATA METHOD AND
APPARATUS; Indix) illustrate a big data analytics system for the business IoT applications. The business IoT
devices can collects a large amount of data regarding products, product attributes, prices, and price attributes. To
be understood by a person, this large amount of data and analytic output must be summarized, personalized, and
organized in relevant terms. The summarization and personalization of such a large and complex set of data
presents challenges in the selection and refinement of information as well as with respect to identification of
patterns and arrangement of information in a user interface. The big data analytics system provides a user interface
to summarize and personalize a large amount of price and product information, to identify patterns therein, and to
generate recommendations in relation to the information.