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Access 2019 capstone project ac 3 working with a sales database capstone federal credit union careers oxford history admissions good afternoon everybody I'm Jim Chloe's from trans voyant and I'm going to talk about precise predictive intelligence and I'm gonna start off with video to keep everybody awake at transplant we see things in ways no one else can we even see the future for years we've collected real-time data from billions of devices that make up the Internet of Things over one-trillion events every day we collect data for meters sensors news outlets and drones video cameras radar satellites and smartphones and use it to see things no one else sees we know the real-time location of aircraft in the sky ocean vessels on the water and trucks on the roads for supply chain customers we know what products fail their payloads where the shipments are going and when they'll arrive for national security and intelligence customers we see all kinds of assets and behavior from physical facilities to people vessels vehicles and more in cities on the sea in adverse environments almost everywhere our machine learning algorithms predict events by learning from past behavior weather traffic socio-political events people movements and more our data scientists constantly improve our predictive algorithms and develop new ones always learning using open source and proprietary data we know about foot traffic on the streets where people are going and where they've been we apply advanced analytics to these massive big data streams to predict future events and behavior so you know what's coming and when so you're prepared and connect proactively for analysts and decision-makers we deliver predictive insights on risks we track all this in real time at big data scale with our CDI platform we are precise and predictive we solve tough problems that tougher the better remember our name it says what we do transform [Music] so thank you so we were founded in 2012 again were trans voyant headquarters up in Alexandria Virginia our smart guys the development teams are out in Minneapolis Minnesota where there's a great depth of geospatial expertise we're in the segment of live and predictive big data analysis and we are the leader we feel in this area we ingest over 1 billion 1 trillion big data events per day and processes using our CDI platform continuous decision intelligence platform so what we do for organizations and companies that we give them the power to predict and influence events to give them opportunities and reduce risk so as you can see some of our clients and our customers that we have we're heavily focused in the commercial arena and what we do for them is focused on their supply chain and risks around that supply chain and in the government side we're an inky tail portfolio company that they've assessed our technology and they feel it will be successful both in the commercial side and the government we're working on five different programs right now in different agencies so how do we do it so it's all about the data we have relationships with lots of commercial data providers and also public data streams that we are able to ingest and then once all the data is inside of our platform which is currently in Amazon we also have an azure or behind behind firewalls and then we do the advanced analytics so we have build models and we choose the machine learning capabilities to provide the insights which are ultimately what you need so we both do live and predictive analytics give these insights and then we have API so it can be connected into any systems that you have so you can do intelligent actions with that so the sensor response concept and behind all that we have our world data science world class data Sciences Berkeley training to be able to make it all possible so how does it work so we're factory supply chain kind of guys so on the left side the input here we have the data edge s so we have different data such as Twitter all the social media feeds weather data NOAA provides buoy information that you have sea heights the temperature all the aircrafts in the world we're tracking we know their course airspeed altitude we get inputs from from news media's both broadcast so we have the information to be able to transcribe that and translate it we have oceans shipments all the vessels in the ocean we know where they are we have information from satellites where they do car counting so we get information from that as well as cell phone locations so we track individuals of where they're going and where they're going so we ingest this then we make sure we heal all the data as it's coming into the platform give it proper quality and then we have a lambda architecture which enables us to do both the real-time data and then historical data so at the top you'll have a discriminant rule something like if you want to know when an aircraft is flying between 180 and 220 knots between a thousand feet and three thousand feet and loitering a certain geofence that you describe and same time about four hours later a ship comes by you want to get an immediate alert that's what we can do in the real time and then predictive you're looking at that and laying in real-time streams as they come in analyzing with the historical updating those models continuously and finding out what characteristics are that are driving that and then do modeling and characteristics for that then we output that through packaging to deliver it as needed and we have different inputs you can go into systems as I said with API into your systems we have some packages we call it precise predictive X P 2x we had different ones with logistics and consumer risk trade and finance and intelligence we all as I said we do this in the cloud so it's very scalable to be able to handle that the t2 else you're into the supply chain side and logistics so if you need to track information all we need is a bill of lading you give that to us and then we can track your wherever it is going and we've been watching the ships at sea and they don't generally follow their routes plan so we have come up over the years and developed our own patterns to follow them we know how long that the 12 times are we know when there's going to be custom delays we monitor social media so we know if there's going to be a potential strike going on we've put all these into our model and we provide these companies with the ability to know when their supplies are going to arrive and the intelligence size we've had different agencies ask us to look at different things like countries cities buildings Airport any geofence area we look at all the open-source data that's available for that and then we start mapping it those out and determine which is the normal behavior and then we look at what standard deviations there are away from it which are anomalous detection is there and then we do each one of those individually then we simply bring this all together and develop that pattern of life and then at that point then we lay on an event so if you're looking to determine when there might be a country that's going to do a missile launch or you might be looking when the next ie D is going to occur or when an airport might be having an attack upon it you look at historical ones you lay it over the pattern life of that area and you do the predictive modeling on that so what does this means for national security our focus is always about the automation because we feel like there's so much data that's coming out at this point that the analysts are not able to handle it so we need to develop these systems so that is a key for us of getting the models to be able to handle this autonomously and have the ability to scale and move the different use cases to different geographical locations with these that's the end of my presentations if you have any questions that I'll be all for this idea to answer thank you very much [Applause] internet of things inc 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