Digital Marketplaces Unleashed
Page 86
Key challenges for further research in this direction are (1) increasing accuracy of predictive models built from data, (2) enable model scalability by adaptive (e. g., requirement‐sensitive) abstraction in feature space and time scales, and (3) establishing methods yielding statistical guarantees for systems that act under adaptive abstraction and concept drift in their application domains.
References
1.
Barry Francis Porter and Roberto Rodrigues Filho. Losing control: the case for emergent software systems using autonomous assembly, perception and learning. 2016 IEEE 10th International Conference on Self‐Adaptive and Self‐Organizing Systems (SASO).
2.
Sebastian Thrun and Lorien Pratt. Learning to learn. Springer Science & Business Media, 2012. MATH
3.
Max Kuhn and Kjell Johnson. Applied predictive modeling. Springer, 2013. CrossrefMATH
4.
Edwin T Jaynes. Probability theory: The logic of science. Cambridge university press, 2003. CrossrefMATH
5.
Samuel J Gershman, Arthur B Markman, and A Ross Otto. Retrospective revaluation in sequential decision making: A tale of two systems. Journal of Experimental Psychology: General, 143(1):182, 2014. Crossref
6.
Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley. A survey of multi‐objective sequential decision‐making. Journal of Artificial Intelligence Research, 2013.
7.
Thomas T Hills, Peter M Todd, David Lazer, A David Redish, Iain D Couzin, Cognitive Search Research Group, et al. Exploration versus exploitation in space, mind, and society. Trends in cognitive sciences, 19(1):46–54, 2015.Crossref
8.
Norha M Villegas, Hausi A Müller, Gabriel Tamura, Laurence Duchien, and Rubby Casallas. A framework for evaluating quality‐driven self‐adaptive software systems. In Proceedings of the 6th international symposium on Software engineering for adaptive and self‐managing systems, pages 80–89. ACM, 2011.
9.
Edward H Glaessgen and David Stargel. The digital twin paradigm for future nasa and us air force vehicles. In 53rd Struct. Dyn. Mater. Conf. Special Session: Digital Twin, Honolulu, HI, US, pages 1–14, 2012.
10.
Thomas Gabor, Lenz Belzner, Marie Kiermeier, Michael Till Beck, and Alexander Neitz. A simulation‐based architecture for smart cyber‐physical systems. In Workshop Models at Runtime, Proceedings of ICAC 2016, 2016. to be published.
11.
Mark Harman and Bryan F Jones. Search‐based software engineering. Information and software Technology, 43(14):833–839, 2001.
12.
Donald Berndt, J Fisher, L Johnson, J Pinglikar, and Alison Watkins. Breeding software test cases with genetic algorithms. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii In‐ ternational Conference on, pages 10–pp. IEEE, 2003.
13.
Steven Carl Bankes. Robustness, adaptivity, and resiliency analysis. In AAAI fall symposium: complex adaptive systems, volume 10, page 03, 2010.
14.
Tomáš Bureš, Vojtěch Horkỳ, Michal Kit, Lukáš Marek, and Petr Tuma. Towards performance‐aware engineering of autonomic component ensembles. In International Symposium On Leveraging Applications of Formal Methods, Verification and Validation, pages 131–146. Springer, 2014.
Part XIII
Big Data and Analytics
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_56
56. Preface: Big Data and Analytics
Rolf Schumann1
(1)SAP SE, Walldorf, Germany
Rolf Schumann
Email: rolf.schumann@sap.com
Extract from the published book “Update – Why the data revolution affects us all” (Rolf Schumann, Prof. Dr. Michael Steinbrecher, 2015, Campus Verlag. ISBN 978-3-593-50332-5).
56.1 Why Are We Talking About a Data Revolution? What Is Actually Meant by the Notion of “Big Data” and “Analytics”?
In the last 20 years, the amount of data in existence has risen 100‐fold. However, this data surge is not unique in history – one similarly rapid increase has occurred before, between the years 1450 and 1500. The volume of data in the world doubled during this period thanks to the advent of Gutenberg’s printing press, which meant a revolution in society at the time. Today, the worldwide data volume is doubling every 18 months. However, what is often not considered in this context is this: while in the year 2000 almost three‐quarters of all data were still in analog form, for example on paper, less than 15 years later this figure is less than 1%. A previously analog world has gone digital, which changes everything.
Although data is becoming ever more important in our lives, it has not yet been possible to establish a widespread understanding of the change our society is undergoing. If you cannot yet imagine the actual meaning behind terms such as “big data” or the “Internet of Things,” you are not alone.
What accounts for the new quality of big data? There is no single, universally accepted definition of big data. But there is an approach cited most often in journalism and science when we talk about the topic, and which will certainly help you get to grips with it.
56.2 Definition of Big Data and Analytics
This approach is based on the four Vs: volume, velocity, variety and veracity (see Fig. 56.1). We’ve already established that big data is large – after all, the name does hint at it. However, big data is also fast, varied and can sometimes even contain vague data. And this is supposed to trigger a revolution that turns our lives on their head? Exactly.
Fig. 56.1The four Vs of big data
The velocity in particular has engendered a real spirit of optimism in business. Companies can monitor which traffic light could soon malfunction, which parcel is currently where, and which pipe needs to be replaced in real time – and react immediately. But it doesn’t just present opportunities for businesses – it will change your day‐to‐day life too. In a networked home, you can observe while on vacation how high the room temperature is or in which room a conversation is currently taking place. Many things become possible. The question you will ask yourself again and again is: How do I want to live?
What is special about the third V, variety? We used to gather data for ONE specific purpose. Once we had used the data to find out what we wanted, we perhaps saved it somewhere, but it was then generally useless. However, in the world of big data it remains valuable. Because it is precisely this linking of seemingly non‐related data that makes big data so exciting.
The fourth V is for veracity. Even inaccurate, vague data can be useful in the age of the data revolution. Although it sounds unspectacular, in combination with the other three Vs it has far‐reaching consequences.
Many constituent parts of big data already existed as individual elements. There has always been data, and computers have also long been with us. However, it is not, as one may assume from the term BIG data, the data VOLUME alone that is changing the world. It is all four elements together, with all of their interactions: volume, velocity, variety and veracity. It is a cocktail consisting of these four elements that is intoxicating so many. A cocktail that releases energy and imagination.
Four Vs are simply not enough for some, so further Vs are sometimes added, for example the value of the data. But as we do not wish to further complicate the definition, we will stay with the original four Vs.
56.3 The Technical and Economic View of Big Data and Analytics
The data cocktail has created a new situation in a structural sense, too. We all know that the array of possibilities for transferring data have accelerated our lives. The days of the stagecoach that would carry our hand‐written letters will not come back. We no longer have to wait days for an answer. With the advent of e‐mail, an exchange is
possible in seconds – a far‐reaching change that has long since become part of day‐to‐day life in our society.
From the perspective of companies, however, being able to process large projects and complex data quickly was until recently anything but normal. The latest technological developments enable data to be processed at up to 3000 times the speed at a comparable cost. How does this technological leap affect us? In order to deepen our understanding of this, we will perform a little thought experiment. Imagine a flight from San Francisco to Frankfurt, Germany. Today, such a flight usually takes between nine and eleven hours. However, if one applied the latest advance in information technology to the aviation industry, this flight could take only 20 s.
You read it right – 20 s! This is reminiscent of the vision of “beaming” that has for decades appeared utopian and fascinating in equal measure, and not just to Star Trek fans. Would you still see this as a “proper” flight? Surely it would no longer be the same. Exactly this is the point. The idea of something being technically impossible and therefore not worth pursuing further has become obsolete on many levels.
In connection with our topic, this means that progress will significantly change the way in which we handle and live with data and information. After all, the latest technologies allow these enormous quantities of data, originating from completely different information, to be processed efficiently and in full.
These changes open up previously undreamed‐of possibilities. Interactions at different interfaces are changing – on the one hand between people, but also between people and objects, i. e., devices and machines. You still frequently see yourself as being in control. You have to read operating manuals and know how to make the device do what you expect it to. However, an entirely new user experience is emerging.
Because of the rapid pace of change in our globalized world, the sheer number of changes in the complexity of how companies, markets and people are networked far exceeds anything we could have ever imagined before. The latest technology makes it possible to respond to every piece of information in real time and using cognitive intelligence. Many large corporations have recognized this development or even driven it forward themselves. It offers them great opportunities while at the same time raising new questions for society.
If we apply these new technical achievements and knowledge to the theoretical plane journey mentioned above, you will already have an idea of what the possible effects might be. These technologies fundamentally change existing business processes and business models, while also enabling completely new business ideas that were previously unimaginable. Just think what a flight from San Francisco to Frankfurt in 20 s would mean. An airline would not simply market them as “faster” flights, as that wouldn’t come close to the quantum leap in air travel they were offering. Imagine how the whole process would change! Would a classic catering service really be profitable for a journey time of 20 s? How many booking classes such as Economy, Economy Plus, Business and First Class would generally be needed? If a flight only took 20 s in the future, you would have to ask yourself what the product actually is if passengers spent 30 min waiting for their luggage or going through security. All of these questions immediately arise if you are suddenly able to perform business processes in real time while using enormous amounts of data. In other words, we have to question existing processes, find out what possibilities real‐time processing offers us and moreover generate completely new ideas.
From a corporate perspective, big data is producing new business models that unleash optimism and euphoria. But, to continue with our example, who still needs flight attendants on a flight that only lasts 20 s? Do we even need pilots? What will happen to the catering companies that previously supplied the airlines? Or, returning to your own life: Do you actually like the further acceleration of our lives? Wasn’t such a long‐haul flight also an opportunity to break the flow of work and watch the films you missed over the previous weeks? And even if the person sitting next to you takes up more space than you might like, haven’t we all at some point enjoyed an interesting conversation on board a flight?
Many publications examine the opportunities big data and analytics presents to businesses in the finest detail. New business models are drawn up, new paths for innovations illustrated. It’s certainly important to understand the logic and the new philosophy of companies and industry, and we will have another look at this topic in the following four papers in this chapter.
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_57
57. Unlocking the Doors of Frankfurt Airport’s Digital Marketplace: How Fraport’s Smart Data Lab Manages to Create Value from Data and to Change the Airport’s Way of Thinking
Katharina Schüller1 and Christian Wrobel2
(1)STAT-UP Statistical Consulting & Data Science GmbH, Munich, Germany
(2)Fraport AG, Frankfurt on the Main, Germany
Katharina Schüller (Corresponding author)
Email: schueller@stat-up.de
Christian Wrobel
Email: c.wrobel@fraport.de
57.1 Background: Transformation of Airports
57.1.1 from Product‐Centric to Customer‐Centric Perspective
Airports around the world are confronted with the challenge to transform themselves from infrastructure managers to service providers in order to compete successfully in the global market. To a great extent, this transformation is driven by digitalization. While so far technology was mostly used to support business processes and thereby enable airports to realize their business models effectively, digital transformation means that new business models are developed around the technology itself. From a European perspective, the major airports in Europe, Middle East and North Africa are directly affected by that challenge since they are the key hubs for transferring intercontinental passengers around the globe. In general, for most enterprises digital transformation means a big change in an organizational and cultural way which directly affects all employees and the nature of their thinking and collaboration. The main objective of this effort is to establish new products and services which help airports to set themselves apart from competitors and gain market shares.
Fraport has given itself a new corporate mission statement: “Gute Reise! We take care.” This mission statement reflects the transformation process in particular and switches the strategic focus from the “Airport Manager’s” perspective to the customers’ perspective. In the past, an airport’s strategic focus was about providing and maintaining runways, buildings and processes for handling aircraft operations of freight and passenger flights – the so‐called product‐centric perspective. Today passengers and visitors are strongly moving into the attention of an airport’s business interest. However, running a successful airport business from a customer‐centric perspective requires much more than providing a smooth handling of aircraft operations. As well, if not even more, it requires the provision of a positive and stress‐free experience for all people around the airport. This involves for example attention on entertainment, information services, process optimization, simplification of travelling, shopping experience and assistance.
As most of us certainly know, travelling by plane involves both positive and unpleasant situations. While the planning process of the new trip is often connected with pleasant anticipation, the mood often turns into tension – at the latest when the journey finally starts. This tension sometimes results in heavy stress especially if queuing at check‐in is backed up, if security screening takes very long or if problems with luggage occur. A pleasant journey seems hardly imaginable at that point and the desire to go shopping or to use other services just evaporates. Those situations need to be avoided to guarantee the most comfortable trip and to persuade passengers to use Frankfurt
Airport again in the future.
57.1.2 Unlocking the Digital Marketplace with a Smart Data Lab
Digitalization supports the fulfilment of those requirements by providing data about passengers, visitors, shops, transactions, flights, freight and so on – almost anything could be tracked digitally and the data generated could be used to generate knowledge and value. However, many stakeholders are involved. To create value from data, data must be harvested by reduction and abstraction (“How can we track meaningful data?”), cleaned and linked through processing and organization (“How can we generate information from data?”), analyzed, interpreted (“How can we gain knowledge from information?”) and applied (“How should we act, based on that knowledge?”). Data “form the base or bedrock of a knowledge pyramid” [1].