Getting the Best Out of Big Data. Roland Krieg Senior Executive Vice President IT Frankfurt Airport
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1 Getting the Best Out of Big Data Roland Krieg Senior Executive Vice President IT Frankfurt Airport
2 Getting the Best out of Big Data Frankfurt, November, 18, 2014 Dr. Roland Krieg
3 Topics to discuss Does Big Data exist at airports? If yes, how can we use this data to improve the passenger experience and business performance? What is the right architecture for getting the best results from the data at airports?
4 Big Data: One approach "Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away. George Dyson (US-Historiker) big TERABYTES 1 TB = 1024 GB PETABYTES 1 PB = 1024 TB EXABYTES 1 EB = 1024 PB small Process data (structured) Office-Documents (semi-sructuretd) Man- & Machine-generated Data (unstructuretd)
5 Big Data: Another approach Big Data is often used as a blanket term referring to the collection of data sets so large and complex that it becomes difficult to process them using traditional database management tools or data processing applications. according to Wikipedia
6 So we have the choice! Information Overload Business Value Extraction
7 How to apply this on Frankfurt Airport A powerhouse of economic activity 8.4% of Germany s GDP is generated here. Frankfurt is: A business metropolis with 35 million consumers within a radius of 200 km A banking center with over 220 financial institutions and the seat of the European Central Bank. Host to over 100 trade shows a year and about 3.6 million visitors from around the world. Source: frm-united.com, Helaba, Statistisches Jahrbuch Frankfurt 2012
8 Frankfurt Airport facts and figures about 22.5 sq km area, currently up to 94 aircraft movements per hour 4 runways altogether, thereof 2 runways and 1 runway for aircraft landings and 1 runway for takeoffs 2 passenger terminals and 1 AIRail Terminal (long-distance ICE train station) 154 flight gates, 204 aircraft positions Air cargo rail track On a single day*: 158,000 passengers 75,000 pieces of outbound baggage 5,800 metric tons of cargo 397 trains serving the airport train stations *average figures of 2013
9 The challenge is not the data volume, it is the complexity of perfectly meshing processes
10 This is refelcted in our AODB INFOplus, the information hub for all flight movement data, with some 60 interfaces Airport Operational Database (AODB) Input Output Interfaces - Season and day schedule - Aircraft and airport-related data - Estimated and actual flight data - Gate allocation, handling information - Flight information display systems - Operational airport systems Flight Information Display System Departure Control System Environmental Monitoring System ATC Baggage Handling System Airspace Information System Building Management System Flight and Airport Information System Airport Operational Data Base Airlines & H andling Agents Gate Allocation System Airport Invoicing System Ramp Management System SITA Message Server Cargo Information System SITA N etwork Design principles: - client/server applications (incl. web technology) - modular architecture, structure, development process - customer-specific, migration-capable
11 Types of Big Data views at an airport The different processes generate very inhomogeneous views: General data like weather, punctuality information from ATM Data related to aircraft movements (eg. time stamps, load and performance data) Data related to passengers - average figures only and NOT related to individuals. Data related to individual but ANONYMOUS passengers. The amount of data might be small compared to those of a retail chain, but the complexity is high.
12 Types of Big Data views at an airport The different processes generate very inhomogeneous views: General data like weather, punctuality information from ATM Data related to aircraft movements (eg. time stamps, load and performance data) Data related to passengers - average figures only and NOT related to individuals. Data related to individual but ANONYMOUS passengers. The amount of data might be small compared to those of a retail chain, but the complexity is high.
13 1. Online Publication of General Data Aircraft Noise Emissions: Sustainability homepage of Fraport, showing the daily share of operational directions (Approach/Anflug 07 vs. Departure/Anflug 25)
14 Types of Big Data views at an airport The different processes generate very inhomogeneous views: General data like weather, punctuality information from ATM Data related to aircraft movements (eg. time stamps, load and performance data) Data related to passengers - average figures only and NOT related to individuals. Data related to individual but ANONYMOUS passengers. The amount of data might be small compared to those of a retail chain, but the complexity is high.
15 2.a Intelligent Airport Map Airport Duty Manager have access to the near real time - Data Warehouse System showing the live position of aircraft, enriched with context on the background of the airport layout. Aircraft-related information for planes at the gate or on the apron, as well as A-CDM time stamps are visible from tool-tips by simply clicking at the plane symbol.
16 2.b Management Cockpit Online extract from Data Warehouse: Traffic light symbols for airside, terminal and ground handling operations, punctuality bar diagrams and actual forecast tables.
17 Types of Big Data views at an airport The different processes generate very inhomogeneous views: General data like weather, punctuality information from ATM Data related to aircraft movements (eg. time stamps, load and performance data) Data related to passengers - average figures only and NOT related to individuals. Data related to individual but ANONYMOUS passengers. The amount of data might be small compared to those of a retail chain, but the complexity is high.
18 3. Predictive Analytics... Hours ahead prognosis (dotted line) and real passengers number at the security check at Pier A, measured with two different electronic methods (full lines)
19 ...and how it helps to avoid queues improve the passenger experience Big data methods help to improve customer experience
20 Imaging the situation you arrive from abroad and try to pass immigration but unfortunately you are not alone A320= ~200 Passagiere A320= ~200 Passagiere CAPACITY IMMIGRATION CONTROL 2450 Pass./h >> 810 Pass./h Terminal B (90Pass./h) x 9 Control Booth = 810 Passenger/h
21 To avoid untolerabel queues we use predictive analytics and explore millions of flights Airline LH and passengers, Wochentag... AD Dienstag DestinationM Brückentag N Flugzeugtyp A320 Ferienfaktor 0.0 Prognose 220 Pass. Land Spanien STT Zeit 09:15 Uhr Saison S Woche KW38 Decision Tree Monat September Bewegung Departure
22 ... than, based on a detailed passenger forecast, we calculate the way of each passenger through the terminal 1. PAX Forecast 2. Real Time Data Airlines 3. Simulation Bundespolizei EPS Eingabe Umsteuerungen Flirt aktuelle Datenbasis Simulation INFOplus HIST Sensor Data Training PAX Prognose -Model Trainingslauf 1 x pro Monat 2 Jahre historische Flugdaten - Total-On Board pro Flug - Umsteiger Parameter Layout
23 ... than, based on a detailed passenger forecast, we calculate the way of each passenger through the terminal 1. PAX Forecast 2 Real Time Data Airlines 3. Simulation Takes less than 5 minutes 4. Presentation updated every five minutes EPS Eingabe Umsteuerungen Endnutzer EPS, BPOL, ASM, Mgt. Bundespolizei Flirt aktuelle Datenbasis Simulation INFOplus HIST Training PAX Prognose -Model Trainingslauf 1 x pro Monat 2 Jahre historische Flugdaten - Total-On Board pro Flug - Umsteiger Sensor Data Parameter Layout PFA
24 Prediction of passenger number at a control area without preemptive measurements...
25 ... and after repositioning and change of gates
26 Types of Big Data views at an airport The different processes generate very inhomogeneous views: General data like weather, punctuality information from ATM Data related to aircraft movements (eg. time stamps, load and performance data) Data related to passengers - average figures only and NOT related to individuals. Data related to individual but ANONYMOUS passengers. The amount of data might be small compared to those of a retail chain, but the complexity is high.
27 4. Visual Analytics is not only the next hype but a powerful tool to manage airport processes Prototype: Geo-mapping report of retail revenues (represented by colour) related to the departure gate and flight destination (above) and to the destination for a defined gate area (below).
28 Staging Online ODS Staging EDS All this is based on ONE powerful Retail Data archtecture Aviation AirportOperational DataBase Aviation Systems Ground Handling Systems Enterprise Data Store Ground handling Security Retail & Properties Airport Security Systems Weather data a.s.o. Operational BI / Near Real Time o... BAM Reporting Operative System Raw Data Unstructured Data Staging Operational Data Store Staging EDS Enterprise Data Store Data Marts
29 Thank you for your attention!
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