Big Data. Kreativität aus rohen Daten. Eine technologische Revolution. Objektivierung des Bauchgefühls. 2.

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1 Heft 9 Software & Support Media GmbH Titelbild: Jer Thorp Big Data Kreativität aus rohen Daten Eine technologische Revolution Objektivierung des Bauchgefühls Software & Support Media GmbH bt

2 Translation and Publication Notice Irene Cramer s article was originally published in the magazine Business Technology Issue 9. The German article is available on Data not just from smartphones, electric cars, and refrigerators Big Data and the Internet of Things There are volumes of data that will not fit into a standard database and are almost impossible to deal with using current tools. The kind of smart products associated with the internet of things call for complicated logic in relation to security and data privacy as well as interaction and flexibility. This article describes the nature of these new challenges. Author: Irene Cramer If you wanted to take a provoking stance, you might say that IT specialists across the board have finally been forced to acknowledge that there are such things as volumes of data that will not fit into a standard database and are almost impossible to deal with using current tools. Only a few old favorites such as Google or Amazon dare and have done so for a number of years tackle these large, very large volumes of data. And they even manage to process them efficiently (so we assume) and profitably (as we can see). One sample application for Average Joe Besides clicks and links, the internet of things will also give rise to new types of data, emerging for instance from the collection of sensor data and the control of actuators. One typical example the IT-initiated use to try to explain the internet of things to those less ITsavvy looks broadly like this: Ellen s smartphone lets her browse her favorite food blog on her train journey home. She finds a great recipe which would certainly go down a treat with her two ravenous young sons this evening. She sends a relevant request to her fridge, which puts together a shopping list, and directly pre-orders a few items in the supermarket so that nobody can snap up those essential ingredients before Ellen gets there. Because her sons are not that keen on going shopping in the car after kindergarten, Ellen also sends a request to her home entertainment system. Based on her sons' preferences, it selects two episodes of their favorite TV series and downloads them to her car's multimedia system. If we were to spin out the example, we could preheat the oven remotely, we could find out where the children need to be picked up (from the playground, from the music room) with the help of the RFID chips sewn into their clothing, and perhaps Ellen s smartphone should check whether the battery in her electric car has been charged, etc.

3 A touch of math So where now do these huge volumes of data come from? Let's do a touch of math: there are approx. 40 million private households in Germany 1. We can reasonably assume that there is a refrigerator, a hot water boiler, an oven, etc. in more or less every one. Most of these appliances are already fitted with sensors; in a few years the experts assume they will not only have various sensors, but also be IP-capable. In addition, there is at least one car in every household, giving us a grand total of nearly 42 million. (Admittedly, not all of them have a multimedia system and only a handful are electric cars). Almost 57 million Germans (equivalent to approx. 70%) have smartphones, some 63% of these are for personal use and 8% for business 2. In addition, there are around 8,400 supermarkets in Germany (with apparently a whole range of sensors, or at least the potential for fitting them). Not to mention the vast number of children s garments that could potentially be fitted with RFID chips You might well ask yourself whether we actually need refrigerators to communicate with cell phones and supermarkets, or whether the home entertainment system should control your own children s television habits. That aside, in light of these figures the kind of estimates the experts normally work with, citing objects in the internet of things in future no longer seem that surprising 3. A few key points can already be derived based on the aforementioned example, which is supplemented below with an example from the area of smart factory, or Industry 4.0. If we imagine the internet of things as a depiction of reality (built up from all that sensor and actuator data, among other things), then that means: Protection and security Not only will the aforementioned model be influenced by reality, but and this is the crux the internet of things can also have a direct impact on reality, which is to say it will actively change aspects of the real world. In fact, in the cited example, from the cell phone to the home entertainment system to the car multimedia system to the RFID chip in children's garments and back to the (satnav in the) cell phone, etc. The data on the usage of things and the associated services are therefore extremely sensitive, comparable, say, to the sensitivity of data currently exchanged between the bank and user over the internet with online banking. But far worse than is the case today, unauthorized access in the internet of things will not only lead to potentially confidential information being divulged, it might also result in the owner or authorized user losing control of their things in the physical world. Just as today you cannot totally rule out falling victim to online banking fraud, presumably you might not be able to exclude the possibility in future that a hacker manages to turn on your IP-capable oven in the kitchen when nobody is at home or everyone is asleep. Leaving aside these security concerns, i.e. unauthorized access, for the moment, the question basically arises, who owns the sensor data that the things produce, who can control the actuators under what circumstances, and, in turn, who do the data from the corresponding services belong to. 1 Source: Statistical Yearbook Source: Accenture agency. 3 Source: J.-B. Waldner quoted according to the English Wikipedia page on the term "Internet of Things".

4 Here at least the following scenarios are conceivable: 1. The sensor data belong to the owner of the thing producing the data; the owner is simultaneously the only authorized user of the actuators. Example: Ellen is the owner of her refrigerator, only she may, for instance, query its contents or give it the order to put together a shopping list based on the recipe she wants to cook. 2. The sensor data belong to the owner of the thing producing the data, the owner is simultaneously also the only authorized user of the actuators, but can also grant others (e.g. the owner of the things/services or the network operator) usage rights for the actuators and/or the sensor data. The user can insist that the data is only used in anonymized form and access to the actuators is password protected. 3. The sensor data belong from the outset to the owner (or provider) of the things/services or the network operator, neither equates to the user. Owner and network operator only grant the user usage rights; however an agreement can be reached with the user so that their data may only be used in anonymized form and/or access to the actuators is password protected. Example: Ellen rents her "smart heating system" from the power utility. Her charges cover her utility company supplying and maintaining the system, so it can also control the system to a certain extent (a key is needed), and allows the company to use the sensor and service data generated by the heating (i.e. Ellen's heating behavior) in anonymized form. While certain people assume that the aforementioned scenarios need to be protected by contracts developed and regulated by certain communities (similar to the mechanisms with software licenses), others assume that data privacy and security can only be implemented through active design decisions during software and system development 4. Regardless of whether it goes in one or both directions: in order that we can ultimately trace back how data records and actuators may be used in a huge network comprising more or less smart things, these data records and actuators must include relevant information, at least though a reference stating where they can be accessed if necessary. encapsulation is conceivable within subnetworks, similar to an intranet or a corporate network today. "In industry this approach [i.e. the ideas associated with the term Industry 4.0, IMC] leads to a paradigm shift, whereby the emerging product takes on an active role for the first time: [ ] The emerging product controls [ ] the production process itself, monitors the relevant ambient parameters via embedded sensors, and triggers relevant countermeasures in response to faults it becomes observer and actor at one and the same time." H. Kagermann, W.-D. Lukas, W. Wahlster (2011): Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. In: VDI nachrichten, No. 13, April And finally, the question of which of the aforementioned scenarios establishes itself, hinges crucially on the pain threshold of the refrigerator owners and car users. The pain threshold regarding data privacy and security of the next group namely manufacturing companies is not only much clearer, but also extremely low: divulge the absolute minimum, as disclosing too much essentially helps the competition. 4 Cf. the Privacy by Design project (as at April 13, 2012).

5 One sample application for Inspector Gadget The ideas put forward by advocates of the Industry 4.0 initiative (see inset) involve nothing less than a complete upgrade of software and hardware and digital networking of production plant. But what do we mean when we talk about smart products which can themselves control production processes, procurement, logistics, HR organization, and the like? One example from the automotive industry 5 might look like this: A car is made up of around 10,000-12,000 individual components on average, with just a fraction of these manufactured by the automaker itself. Usually cars of the same model, but with customized equipment and features, are manufactured on a production line over a certain period. Hence the production plant needs to allow for a certain degree of flexibility. For instance a five-door model could first be assembled with a certain engine, paintwork, and upholstery, followed by a three-door convertible with a different engine, paintwork, and upholstery. If a product is to actively help control the production process, it can only do so via its individual components (not exactly every individual bolt, but certainly larger units). The starting point for this smart vehicle might be the car multimedia system mentioned at the start. This is provided as the first element for the new car 6 and receives information for customization purposes. It now sends inquiries to various services, among other things, in order to collect information about which parts need to be ordered from which suppliers, in order to schedule the timing, logistical needs, HR needs, etc. into the assembly process. Once the suppliers and the necessary aspects of the production planning have been established, the car multimedia system (in other words the provisional "seed" of the car) sends a corresponding order to each supplier 7. Taking the wheel by way of example, the graphic illustrates that a relevant cascade of inquiries and orders is now produced with the suppliers (here for tires and wheel), similar to the ones described above. Only once the necessary production has been triggered and transport organized in the most far-reaching scenario right back to the raw-materials supplier can a confirmation be sent back to the car multimedia system, once everything is in place, which logically also includes information specific to the item (e.g. in the form of an embossed check number, such as an affixed barcode, or using an RFID chip), which is used to identify the individual parts during the marriage process, thus ensuring that each car is assembled correctly. The car multimedia 5 It is been noted that some of the aforementioned aspects are already widespread in automotive production and in other sectors, too. 6 Bearing in mind that it will not be the first thing to be fitted. 7 Whereby it presumably does not create and send this order itself but, for instance, merely requests the creation of a relevant SAP order.

6 system must now forward the received feedback to assembly planning so that the car is scheduled firmly in production or (if necessary) can be rescheduled. Mass and method As set out above, this example could also be extrapolated ad infinitum. As such, the car "seed" must always check that the right parts are assembled and do so even though it might be one of the last elements to be fitted. In the case of faults, suitable information must be sent to the peripheral systems, and in the worst case even to the party who initiated the order. The example illustrates how things are becoming smarter and the world (of the internet) relatively complex as a result. On the one hand, objects fitted with sensors in the internet of things can record information from their environment and process that information more or less intelligently, depending on their capabilities. On the other hand, they can respond to this information; and do so either directly, provided they themselves have the necessary actuators, or indirectly, by sending a relevant request to an object or system that has the necessary actuators. Such a system in which (virtually) every individual thing is (at least halfway) intelligent is clearly going to be a highly complex entity, and not just because of the sheer volume of data and the possibly unusual complexity of its inherent features. The fact that it will take various different approaches to model the logic specific to the objects is also obvious. In the case of large volumes of data you of course immediately think of probabilistic, data-driven processes; after all, large volumes of data tend to entail a certain degree of uncertainty and unclarity. However, certain phenomena will be subject to rules and regulations which will be best modeled descriptively and explicitly, in other words in the form of rules-based models or workflows. In this example however, the high degree of complexity is primarily attributable to the close networking between the automaker and its suppliers, and these in turn with their suppliers and other customers, logistics companies, raw material suppliers, and so forth. Regardless of whether communication functions via the sending of messages or events (say along the lines of an event-based architecture), or whether the suppliers are given direct access to a part of the automaker's data repository, the networking is closely meshed and the logic distributed across many nodes. Moreover, the volume of data produced per time unit is correspondingly large. An observer will not necessarily understand how the system behaves at a certain point; after all, the feedback from the various nodes, which themselves in turn communicate and interact with various other nodes, means a large number of parameters influence each order. Hence the following questions arise from a system theory perspective: Does a balanced, beneficial form of interaction ensue in this network of production facilities, taking the automaker by way of example? Does this actually help optimize the individual production facilities or the overall system? How are those problems detected and rectified that prevent optimum and costeffective workflows? In the aforementioned example, the raw-materials supplier for the tire manufacturer could provide frequent feedback, which leads to a rescheduling of production at the tire manufacturer and also, in turn, at the automaker. However, only the tire manufacturer can rectify this problem by asking its supplier to modify the delivery conditions and thus adjust the logic on the systems affected. The automaker has no such option; it can only complain directly to its supplier, only indirectly to its supplier's raw-materials supplier. As an alternative to this direct access and the explicit changes to the logic at the tire manufacturer s raw-materials supplier, the logic of the tire manufacturer s and automaker s systems can also be adjusted to take account of this "experience" on the basis of empirical

7 data, i.e. to adopt different scheduling from the outset. That can either be achieved by means of self-learning systems or by implementing a system that can very easily adapt to changes. In light of the addressed challenges, anyone reluctant to join the choir in full praise of big data and internet of things may well be right in keeping their voice down: as each individual node becomes smarter, so the logic that needs to be implemented in relation to security and data privacy as well as interaction and flexibility becomes more challenging. And let's be honest: as far as the aforementioned challenges go, the debate has only just got underway a debate that will (hopefully) lead us to sustainable solutions. Irene Cramer Responsible for prototyping and technical communication in Product Development at Bosch Software Innovations GmbH. She holds a doctorate in computer linguistics and regularly publishes popular-science and academic articles in the fields of business rules management, data mining, and voice technology.

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