A time series is a series of data points indexed, listed or
graphed in time order. Many applications deal with such
data: those that feature financial transactions with
associated timestamps, inventories that continually
change over time, and other processes involving logs
and Internet of Things (IoT) sensor events coming in 24
hours seven days a week.
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Analysis of the Internet of
Things or IoT sensor data is expected to grow at a rapid
pace, with the number of cellular IoT connections
exceeding 3.5 Billion by 2023. With 90% of senior
executives focusing on IoT rollouts in 2018, worldwide
technology spending on IoT is forecasted to exceed $1.2
Trillion by 2022. In order to go beyond basic monitoring,
all organizations must figure out how to analyze the
avalanche of IoT sensor and other time series data in a
timely manner to gain business benefits from the
investment.
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Why TigerGraph, a Native Parallel Graph Database for Analysis of Time Series Data?
Time series Analysis of Power Grid IoT Data
From meter readings to the constant flow of
information from sensors and network
components, utility companies are being flooded
with the IoT sensor data. Working closely with the
leading energy and utility companies, TigerGraph
has pioneered native parallel graph approaches
that help companies monitor and analyze power
flows, detect bottlenecks, and alert personnel
about grid performance issues.
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Balancing a
power grid requires consolidating signals from
multiple levels of the power infrastructure and
matching demand and supply with complex linear
equations, which is deep link analytics taken to
the extreme on time series data. Using
TigerGraph to process all of their power grid IoT
(Internet of Things) sensor data in real-time,
operators can respond immediately to sudden
spikes in demand or drops in supply, thus
reducing operational risk and operating costs
while improving reliability, efficiency, and
customer experience.
Time Series Analysis of Network and IT Sensor Data to optimize resources
Virtualization has transformed the Network and IT
landscape, leading to the pooling of computing, storage,
and networking resources. Network and IT infrastructure
administrators have the flexibility to share the resources
and deploy workloads for multiple applications, services,
and business units. Increasingly, data centers are using
IoT sensors to monitor the health of each network or IT
resource. The time-series data from hundreds of
thousands of sensors attached to network & IT resources
is analyzed by TigerGraph in real-time to detect when a
resource such as a storage array, server, network switch
or router shows the signs of wear, requires maintenance
or is nearing its peak capacity.
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Using TigerGraph, the
administrators can also figure out which workloads are
affected and how to minimize the impact.