Kinetica Analytics Platform enables users to concurrently ingest and process time series data at ultra-high speed and at IoT scale. Kinetica can perform powerful time series analysis such as aggregations, window functions, native track data analysis and inexact joins without needing to send data to a separate time series database. Users can incorporate analysis on live and historical data simultaneously and utilize other analyses such as geospatial aggregations.
Users can work with a myriad of simple and complex datatype and analytical techniques via well-known industry standard SQL calls. One can use relational, geospatial, graph, text, and time series operations as part of their queries. It is possible to find new insights by mixing analytics and data sets. High frequency streaming data and huge swaths of historical data can be handled in the same system. Results can be calculated and fetched in seconds instead of hours or days.
It is possible to generate graph data from relational and spatial data with a simple API call. Pre-packaged distributed and sophisticated graph algorithms can be used to work with geospatial and relational functions for fast analysis without must deploy a separate graph database. One can optimize routes, predict relationships, and forecast outcomes algorithmically on very large data sets in real-time.
Data scientists of an enterprise can combine different types of analysis to explore datasets and develop new feature calculations. They can utilize Kinetica’s performance at scale to rapidly calculate complex features at high velocity. Machine learning (ML) results can be used to augment accuracy of the analytics.
The following table describes briefly how Kinetica can be used to tackle some of the most common operations in different industries.
Table 2. Use-case Scenarios
Finance |
Banking and capital markets - real-time portfolio risk analysis, mortgage risk and opportunity analysis, accelerated transaction cost analysis and trade optimization |
Utilize Kinetica Active Analytics Workbench (AAW) to unify streaming, historical, and location analytics with machine learning on a single platform to power real-time trade and risk decisioning applications |
Healthcare |
Patient care 360, medical IoT |
Leverage Kinetica vectorized processing to simultaneously ingest, analyze, and visualize medical IoT data for real-time monitoring and predictive maintenance |
Telco |
Adaptive coverage, network performance analysis, 5G small cell network planning |
Dynamically visualize and analyze network performance in real-time instead of running the process in batch, delaying results |
Logistics |
Demand forecasting and dynamic inventory replenishment, demand-responsive supply chains and routing, data-driven operations and fleet management |
Leverage Kinetica graph analytics for shortest point analysis, and geospatial analytics at scale to visualize fleet in real time |
Automotive |
Autonomous vehicle testing and reporting, In-car analytics, root cause analysis |
Use Kinetica to continuously collect driving movements to record and measure driver behavior and vehicle data, compute statistics, and make recommendations and predictions |
Utilities |
Grid optimization, operational efficiency, customer insights, predictive maintenance |
Use Kinetica to analyze and visualize streaming smart meter and sensor data in real time. Leverage geospatial capabilities to identify power surges and usage trend |
Energy |
Drilling and well analytics, supply chain optimization |
Leverage Kinetica’s geospatial capabilities to perform visual analysis of public and proprietary data on oil wells. Additionally, perform time slicing and analyze historical data |
Retail |
Inventory and supply chain management, customer 360, precision marketing |
Use Kinetica to act on real-time retail data in subseconds to optimize in-the-moment customer engagement and inventory as it enters and exits the floor |
Climate |
Oil spill cleaning, air quality fluctuation measurements, drone imagery usage for estuary cleanup |
Machine learning algorithms accelerated by Kinetica to auto-identify wastes in images |
Public Sector |
Data driven governance - emergency response, disaster management, real-time intelligence, cybersecurity, dynamic logistics |
Act on data in real-time to track and detect national security risks and cyber threats, optimize logistics and mobilize supply-chains |
