Niclas Carlén
email: niclas.carlen@umu.se

August Forsman
email: august.forsman@umu.se

Jesper Svensson
email: jesper.svensson@umu.se

Johan Sandberg
email: johan.sandberg@umu.se

IoT Enabled Process Innovation: Exploring Sensor-based Digital Service Design Through an Information Requirements Framework

Keywords: Process innovation, Internet of Things, Information requirements

Through digitisation of physical artefacts and environments, the Internet of Things carries vast potential for process innovation. However, navigation of the quickly evolving technological landscape and identification of emerging opportunities for value creation remains challenging. To this end, we combine existing frameworks on information requirements, IT capability, and business value of IT. We evaluate the usability of these frameworks for IoT enabled innovation in our analysis of two sensor-based process innovation projects. We investigate the fit between process characteristics and technological functionality, and the implications of this alignment. Our analysis demonstrates that the framework provides a practically useful and theoretically coherent conceptual device for analyzing process characteristics and digital options to innovate processes. Furthermore, we find that IoT sensors are well suited to address connectivity and uncertainty requirements. However, in order to leverage them to address high equivocality requirements designers need deep contextual understanding to align IoT capability with information requirements.

Introduction

The ongoing pervasive digitisation of physical artefacts and environments, often collectively referred to as the Internet of Things (IoT), signifies a new paradigm in data processing and communication. It is not a new technology per se, [START_REF] Wortmann | Internet of Things -Technology and Value Added[END_REF] describes IoT as "a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies". IoT has evolved into an ecosystem of possible objects or things to connect to the internet where basically anything that has an on and off switch can be connected e.g. sensors. Combined with an expected battery life of several decades for many sensors allows for data collection around the clock. Sensors like this are making headway around the world with the emergence of smart cities, i.e. cities that are connected and efficient collecting data to manage its resources and serve its citizens in the best way possible [START_REF] Sundmaeker | Vision and Challenges for Realising the Internet of Things[END_REF]. The global market for IoT is growing tremendously, resulting in significant market opportunities and an expected turnover at around 8.9 trillion dollars combined with an estimated 26 -100 billion connected devices by the year 2020 (Statista, 2017). Currently, there are significant uncertainties when companies engage in IoT; lack of standards, low understanding of technology and security issues are all sizeable problems hindering development (Forbes Insight, 2017). Most companies feel uncertain which IoT-solution will collect the data they need, this results in a discrepancy in what companies need and what they get with regards to data and information (Forbes Insight, 2017). In this paper, we argue that this uncertainty can be mitigated by synthesising two theoretical frameworks combining both analysis of information requirements and corresponding digital options with effects on processes by different IoT implementations.

We have been involved extensively in two IoT-projects and have seen issues regarding information requirements and process analysis arise throughout both. Both cases illustrate the need to understand the working environment and the technology at hand. While most studies thus far have focused either on the technical or business side of IoT (Forbes Insight, 2017), we argue that there is a need to understand both aspects to generate successful outcomes. Since in most cases there is going to be humans interacting with the technology in their daily work life, we also argue that it is of great importance to understand the duality of technology and social activity. We have, therefore, focused on a micro-level perspective by investigating how IoT-systems affect work practices. This paper seeks to examine the effects of implementing sensor-based systems in organisations, i.e. how the implementation effects the process and value chain. This study will be limited to two separate case studies where sensor-based systems have been both designed and implemented in the organisations. Specifically, we explore the following research question:

How can IoT sensors be used for digital service design to innovate processes?

Internet of Things in business processes

With the emergence of ICT in the 20th century a lot of IT projects have had lacklustre results, one reason is the assumption that technological functionality solves organisational issues [START_REF] Alter | The Work System Method. 1 red[END_REF]. The argument against this is that an IT system should be viewed as a resource for the solution and not the solution itself [START_REF] Alter | The Work System Method. 1 red[END_REF][START_REF] Meyer | Internet of Things-Aware Process Modeling: Integrating IoT Devices as Business Process Resources[END_REF]. IoT is also argued to have a decentralising effect on the value chain in business as parts of it become connected. As the value chain becomes decentralised the decision-making rights are moved to the individual components in the chain, and the different parts become more independent, arguably creating a more efficient and streamlined process chain throughout [START_REF] Haller | The Internet of Things in an Enterprise Context[END_REF]. We would add to the current research by enacting these principles in real-world implementations. Doing so with the intention of showing real-world scenarios with the technology in place, we argue that the current body of research will benefit by the holistic perspective applied in this study.

A framework for analysing process effects with IoT and IoT capability

In this paper, we synthesise two different theoretical frameworks. The first is a framework developed to identify what digital options are available to organisations and what information requirements different processes have [START_REF] Sandberg | Digital Options Theory for IT capability investement[END_REF]. The second framework is intended to measure the effects of the chosen solution on the process [START_REF] Mooney | A Process Oriented Framework for Assessing the Business Value of Information Technology[END_REF]. By synthesising we refer to using the frameworks in tandem, iteratively, throughout the process analysis, implementation and evaluation of the systems. This in turn contributes to the body of research with a practical framework encompassing the entire process of implementing IoT in an organisation with intent to innovate business processes.

Information requirements.

Information requirements are used to identify what digital options there are for a specific business process. Digital options should here be understood as opportunities for leveraging IT in process innovation. The conceptualisation is grounded in earlier research, but [START_REF] Sandberg | Digital Options Theory for IT capability investement[END_REF] further develop the concept of a specific tasks requirements about uncertainty and equivocality by adding connectivity to modernise the theory. We extend existing applications of the framework by using the information requirements in an IoT context. Establishing information requirements for each case is the first step of the innovative work process to map if and how IoT can support or transform a business process. This analysis, enables generation of the digital options available to organisations. To identify information requirements there are three aspects to consider and analyse: connectivity, uncertainty and equivocality. Connectivity relates to the informational dependencies between processes and systems within an organisation, i.e. the need for information sharing across boundaries in an organisation (Malhotra et al., 2005). If the connectivity need is regarded as high, the focus for managers should be to counteract technical or social barriers by increasing information reach, characterised as "the number information sources that can be accessed during task execution" (Sandberg, et al., 2014, p.428). If information can be accessed across organisational and geographical boundaries, process information requirements have low connectivity requirements. When the connectivity need is low, it can be relevant to increase richness which refers to "the number of data points available regarding a given object during task execution" (Sandberg, et al., 2014, p. 428). Uncertainty refers to the availability and accuracy of information needed for actors to execute their task within an organisation. Uncertainty requirements can be addressed by continually balancing information production and information consumption (Ramaprasad & Rai, 1996). Information production occurs when actors generate new information based on stimuli in the process and its environment. Information consumption turns the existing and available information into business process actions. If the information requirements are high in uncertainty, i.e. the current information is inaccurate, unreliable or insufficient, organisations should aim to increase production of information as they should not want to consume unreliable information. When uncertainty requirements are low, organisations can instead focus on consumption of information.

If the information is reliable, available options for consumption emerge which can drive an organisation to make better data-driven decisions. Equivocality refer to confusion and lack of mutual understanding when executing a task, or the level of complexity and ambiguity in the tasks information processing [START_REF] Sandberg | Digital Options Theory for IT capability investement[END_REF]) (Mathiassen & Sørensen, 2008). When a task is to be executed, there may be a need for mutual understanding between actors or processes, as they may have to rely on contextual knowledge. Conversely, there can be situations where actors or processes can rely on their codified knowledge and routines. If information requirements are high in equivocality, then the situation or task context is unknown for the involved parties. A relationship enables success in these situations; there needs to be a high level of understanding and trust within the system and in the systems supporting situational knowledge and contexts. If the requirements are low, however, then the characteristics are based on encounters where standardised protocols and workflows are utilised.

IoT capabilities in organisations.

Measuring effects of IoT on processes

We draw on Mooney's (1995) framework for assessing the business value of IT. Business performance is best measured from the performance of its processes (Ray et al.

2004 [START_REF] Mooney | A Process Oriented Framework for Assessing the Business Value of Information Technology[END_REF]. Sensors collect a limited range of data in an environment which in turn supports business processes. Therefore, we conclude that examining sensor implementation impact is most efficiently done in the context of the processes which they intend to support. In this study, we analyse the automational, transformational and informational effects of the IT-systems designed for each case, which are classifications derived from Mooney's process-oriented framework and have been used in similar studies [START_REF] Stenmark | Enabling Process Innovation through Sensor Technology: A Multiple Case Study of RFID Deployment[END_REF][START_REF] Visich | Empirical Evidence of RFID impacts on supply chain performance[END_REF]).

The automational dimension relates to how sensors data collection can substitute manual labour. Different kind of sensors can continuously collect data which are to support or initiate processes. Automational effects on business value are gained through aspects such as improved customer service, increased productivity and a more efficient labour distribution [START_REF] Stenmark | Enabling Process Innovation through Sensor Technology: A Multiple Case Study of RFID Deployment[END_REF][START_REF] Visich | Empirical Evidence of RFID impacts on supply chain performance[END_REF]).

Informational effects are those that are caused by IT-enabled collection, storage, processing and spread of information acquired from the sensors. Case studies of RFID implementations have shown that business value can arise from improved resource management and reduced manual labour (Stenmark & Jadaan 2010, [START_REF] Visich | Empirical Evidence of RFID impacts on supply chain performance[END_REF]).

The transformational dimension affects and supports process innovation and transformation. Sensor data may support and improve existing processes but may also be utilised for business innovation. Data acquired to support a specific process chain can be used in combination with other aspects of the organisations' knowledge base to innovate the business (Stenmark & Jadaan 2010).

Methodology

For this paper we conducted a multiple case study consisting of two cases where sensorbased IoT-systems, through collection and visualisation of data, were designed and implemented to support different process chains. Case studies are a preferred strategy when research questions related to "how" and "why" are posed (Yin, 2003), and multiple case-studies when the logic of the study is to "produce contrasting results but for predictable reasons" (Yin, 2003, p. 47). We argue that this makes a case-study approach viable with regards to the framing and research question stated earlier. By evaluating this innovation process through multiple cases, we intend to generate general findings and propose practices which could be built upon in further research. We chose the cases based on an analysis of two different research sites. The classification for inclusion was that the case should present one or several concrete problems in a process chain where sensor-based technology could be a solution. These problems could either be a lack of ability in performing activities which could be enabled by the technology or addressing problems currently present in an organisation.

A process is "a structured, measured set of activities designed to produce a specified output for a particular customer or market" (Davenport, 1993, p. 5) and can be classified into two different categories; operational processes and management processes [START_REF] Mooney | A Process Oriented Framework for Assessing the Business Value of Information Technology[END_REF]. Operational processes are the set of activities an organisation performs to produce something that generates value and is referred to as an organisation's primary activities. Management processes are related to streamlining and improving the efficiency of an organisation's primary set of activities such as coordinating and handling different information. Process innovation in this context refers to the practice of analysing an organisation's processes and redesigning them using innovative technology to improve performance and support the processes [START_REF] Davenport | Process Innovation -Reengineering work through Information Technology[END_REF]. In this case, that innovative technology is LoRa-sensors, which enables remote monitoring and control of different aspects of a process, and the IT-artefacts of the software designed to visualise or manipulate the data generated by the sensors. Each case process chain was broken down into sub-processes depending on what type of activities and the complexity of the tasks performed. We then mounted sensors at each research site to collect data and designed IT-artefacts with the purpose of solving specific problems related to the sub-process. The effectiveness of these systems was analysed in the context of what type of value and effects the data generated when innovating operational and management processes.

Research sites and sensor technology

The sensors used in the project are based on LoRa technology, and a specific type of gateway delivers connectivity to the sensors. Because of lacking infrastructure in the northern municipality, we were tasked to mount two base-stations; this implementation took place in September 2017. One of these base-stations was installed on the highest point in the town, which is a water tower, the other one on the roof of the local secondary school. We also installed a base-station in the city where the cleaning company project took place to further the existing coverage. The base-stations have guaranteed coverage of a 3 km radius, however, depending on disruption and quality of air it may be greater than that (LoRa-alliance , 2015). LoRa stands for Long Range and is the physical layer utilised to create long-range communication links. LoRa is based on chirp spread spectrum modulation, using its entire spectrum of bandwidth and is therefore very resistant to channel noise. Lo-RaWAN is based on Low Power, Wide-Area Networks i.e. LPWAN, and defines the communication protocol and system architecture for the network. LoRaWAN has a great influence on battery times for nodes, network capacity and security. LoRaWAN is explicitly designed for sensors and applications that need to send small amounts of data over long distances at different time intervals, making it ideal for IoT sensors applications. As IoT is still a new phenomenon and standards are currently lacking, other technologies are competing to become the business standard. In likeness with LoRa, several LPWAN networks are emerging as competitors; examples are Sigfox and Narrowband IoT that operate similarly (LoRa-alliance , 2015).

In this study, we have used two different types of LoRa-sensors that measure different values while having some similar readings; temperature inside the casing, humidity at the sensor and battery-level.

Data collection and analysis

The data collection process consisted of an analytical phase where we in conjunction with each organisation collected data regarding the problem background of their process chains. An implementation phase studying the physical environment of the research site, collected data relevant to the practical implementation of the system, and an evaluation phase where the data related to the results of the system was collected.

All interviews conducted are semi-structured and were conducted throughout all three phases to capture viewpoints from the participants on both current problematic aspects, initial impressions of the implemented system, and impressions regarding its effects.

Observations refer to activities where we have studied staff members performing the process chains, as well as documenting areas of interest. This was performed mainly during the analytical phase in order discover details relevant to mapping the process chains and capture insights possibly missed using the other methods. Workshops are meetings where we collected data related to our subsequent design choices and was mainly conducted in the analytical phase with participants in each innovation project.

Informal encounters are the interactions with the organisation where we have performed different tasks or exchanged minor pieces of information related to the projects, a method which was applied throughout all three phases. A total of 9 interviews, 11 observations, 7 workshops and 46 informal encounters were conducted, spanning all three cases in the study during a time frame spanning from 20th September 2017 to the 29th of Mars 2018. The data analysis was performed in iterations together with the organisation throughout the time-frame of the data collection, where the input in the analytical phase formed the basis for our description of the information requirements and IT-capabilities in each case. The results of the evaluation period (testing the systems in practice) formed the input for the process effects each system had on the corresponding process chain it supported.

Results

This section consists of the results of our research, each case will be presented with a description of the research site, problem background and information requirements of the process chains, with a subsequent description of practical implementation and effects from each case.

Swimming pool

Research site.

The research site of the public swimming pool-case is in a municipality in the north of Sweden and is run by a small organisation of six people who maintain the pool, a gym and a gymnasium in the same building. The public pool is a facility open during weekdays and Saturdays on regular weeks.

Process chains & Problem backgrounds.

The organisation lacks data on the number of visitors and which hours and days during the week generate most activity. The main areas of activity which the organisation found interesting were the entrance to estimate the overall number of customers, the cafeteria to investigate the air quality during peak hours, and the locker rooms to investigate differences in attendance between the genders. This data is interesting for the organisation when optimising staffing and air quality, and to create an overview of when and how much the facilities are used during the week. To achieve these informational effects, the sensor implementation sought to address the high uncertainty and low connectivity information requirements through continuous collection of visitor data. Further, the information regarding the number of visitors is non-equivocal as the collected data is readily interpreted in the context. The second process chain in the case is optimising heating of the swimming pool. According to the person responsible for this routine, the pools are heated to 32 °C every week during Tuesday nights, and then the temperature falls successively to around 27 °C during the weekly cycle. This practice leads to uncertainty amongst the customers on the current temperature and generates phone calls to the organisation increasing the workload. Further, the facilities have shown signs of increased wear in forms of mould and moisture damage due to the increased evaporation generated by higher temperatures. The organisation seeks informational effects on the managerial level through an increased amount of temperature data points and implementing sensors to address the connectivity and uncertainty requirements. The equivocal requirements were low as temperature data is readily understood in the context. The third process chosen for this case is the documentation of pool water quality, which is a process chain performed by the staff daily to discover anomalies and potential health risks related to the pool water. Water samples are collected, analysed and documented as the first task of every day: water temperature, pH-value and chlorine-levels. To collect this data, the staff places a thermometer in the pool water where it is submerged for 15 minutes. During this time, they gather two water samples which are analysed using a pool water quality kit establishing its pH-value and amount of chlorine. This data is then documented manually in a binder and stored in the staff office of the facilities. Due to the repetitive manner of the data collection and documentation, the organisation seeks to explore to what extent it could be automated using IoT sensors. To achieve these effects, automatic production of information regarding temperature, pH-value and chlorine levels is required, which characterises an information requirement high in uncertainty. We found the connectivity and equivocality requirements to be low as the information is to be utilised within task entity boundaries and well understood in the context.

Implementation.

To support the first process chain, we mounted four sensors at areas for which the organisation had expressed interest. The main units of observation these sensors were to measure were motion activity and temperature in each respective area. The sensors were placed at the entrance of each respective area at the height of around 150 cm's ensuring measurements of every individual passing.

This motion activity and temperature-data is uploaded every 30 minutes to a database, imported into tables and transformed into graphs, both real-time and historical. The information was made accessible to the organisation through a web application where it could be studied and form part of the basis for process innovation. The implemented system has the characteristics referring to the production of information to address uncertainty requirements. As this process aims to collect information about motion and temperature in the facility we argued that the four sensors would address the uncertainty requirements.

To support the second chain, we mounted one temperature sensor in the bottom of the swimming pool, hidden behind a ladder. This sensor uploaded water temperature every 30 minutes to a database and was imported into tables and graphs made available to the organisation. The historical data generated by the sensor can be utilised to measure how much time it takes to heat the pool to the preferred temperature and get a more detailed overview of its heating cycle. This data could serve as a basis for innovating the heating process chain and minimise the problems of their current practices. The third process chain utilised the same temperature sensor as the second as the only relevant unit in the process chain collected and documented is the temperature data. This data was then uploaded every 30 minutes and presented in the form of tables and graphs. For this process, we addressed the information requirements with the intent to increase richness by collecting data with the sensor as well as produce more information to lower uncertainty.

Effects.

The first process chain showed primarily informational effects. The data collected from the motion sensors generated an estimate of which areas has the most activity, and during which hours can be established by studying the graphs in the web application. The organisation had ideas of using the data to optimise the air conditioning. However, the functional capabilities of adjusting the air conditioner cycles to reflect usage or be automated by the data seemed to be limited which was uncovered later in the project when this issue was discussed with a janitor responsible for the air conditioning. A side-effect of studying the comprehensive dataset was the discovery that the temperature in the cafeteria rises around 2-3°C during the nights when the facility is closed. This occurrence was unknown to the organisation when they were informed of it, and according to one of the staff members, may be related to the underfloor heating being active during the night time when the ventilation is inactive.

The effects on the second process chain had informational effects. The historical data generates a clear and consistent timeframe over how long it takes for the swimming pool to reach its intended temperature and shows some anomalies. An example is the re-warming of the pool, which usually happens around 4 hours after it has reached its maximum temperature. Why this happens is unknown to us right now but will be of interest in further evaluation of the system. The third process chain has potential to be completely automated, generating both automational and transformational effects, but since the sensors automate only 1/3 of the data collection, the staff must still perform a majority of the process chain in the same manner as before. A future update of the system will be to implement sensors collecting data of the chlorine level and pH-value. With a complete system in place, the whole process will be performed continuously and automatically document the data in the same way as current practice. The permanent character enables transformational possibilities in the sense that with a system documenting the water quality continuously, anomalies in the water can be discovered faster.

4.2

Cleaning company

Research site.

The research site for the case of the cleaning company is located in a university building. The organisation is responsible for cleaning all facilities and have a staff of six managing and executing this task at the research site.

Process chains & Problem backgrounds.

The staff have expressed a problem of prioritising which order that classrooms are cleaned after the weekend in an effort to work more condition-based. The current situation is such that the activity in the classrooms during the weekends is unknown to the staff when they begin cleaning on Mondays, and they clean each room in a set routine. The consequence of cleaning in a routine-based manner could result in rooms with less cleaning needs getting cleaned, and rooms with higher cleaning need left unattended. The cleaning process of each room constitutes four sub-processes: cleaning the floor, cleaning the tables, wiping the whiteboard and emptying the waste bin, and the cleaning need of each sub-process in combination is what constitute the cleaning need of the classroom. The organisation seeks informational effects through collection of information regarding classroom cleaning need. This information requirement has the characteristics of high equivocality, as assessing cleaning need is non-algorithmic and is based on situated knowledge about the specific context. The process chain has high connectivity and both high and low uncertainty requirements as cleaning need information must be collected and accessed remotely. Since the organisation currently lacks data on the activity in the classrooms during the weekends, it cannot innovate its processes in such a way that it aligns with the ambition of working more condition-based.

The second process chain presented as problematic by the organisation is assessing if a room is vacant. In the current situation, the staff do not clean rooms which are occupied and wait until the rooms are vacant to clean them. This problem means in practice that they sometimes spend time visiting rooms only to discover that they cannot be cleaned, and delay that process until later, having wasted time moving to the classroom. The organisation seeks informational effects on the operational level through remote access to information with regards to classroom vacancy. To achieve these effects, vacancy information need to be produced and remotely accessible to cleaning staff, which characterises the information requirements as high in connectivity and uncertainty. Lastly, information regarding classroom vacancy is non-equivocal as the room is either vacant or not.

The third process chain is the comparison between the presumed usage based on the booking schedule and actual usage of the specific classrooms. According to the organisation, it is not uncommon for a room to be booked during the week, but its actual usage is unclear. The staff can plan the cleaning of classrooms only to discover that they have not been used and, therefore, not in need of cleaning. This could also be used as a basis when negotiating terms with its currently largest customer which is the university itself. Part of how many hours the company can bill the university is based on the number of hours booked in the electronic booking schedule. The organisation seeks informational effects on the managerial level through remote information collection of classroom usage. High connectivity, high uncertainty, and low equivocality characterises its information requirement. The multiple data collection points increase reach and production of this non-equivocal information.

Implementation.

To support the process chains described in the case we mounted sensors in classrooms collecting motion data. The sensors were placed at the entrance around 170cms from the floor, registering every motion near the entrance door. This data was uploaded every 10 minutes to a database. We then designed a web-application containing various artefacts which utilise this data to address the information requirements in the process chain. Due to the high connectivity requirements the purpose was to increase reach through multiple data collection points. For the uncertainty requirements, there was a need for both production and consumption to address the requirements relating to uncertainty. The sensors installed addressed the information production aspect, and the web application was developed to increase consumption of information. As the equivocality requirements were high, the need for a relationship characteristic was of high priority. Due to the organisation wanting to measure cleaning need in the rooms, which is a highly equivocal measurement, the sensors and web application needed to be utilised in conjunction with the cleaning staffs' knowledge and routines.

The artefacts contained in the application display three sets of data; accumulated motion in each room, a two colour-button signalling if motion has been detected the last 10 minutes and historical data available for export in the form of graphs. The application was made accessible for the cleaning staff in their day-to-day work by a tablet placed on their cleaning cart.

The system was tested for three weeks, during which the staff had access and utilised it when performing their tasks. Furthermore, they graded the experienced cleaning need which was defined on a three-grade scale where one was clean, two was normal and three related to a high cleaning need. Interviews with the staff were conducted before, during and after the test period. During this period, we also tested the hypothesis that an increased motion value from a classroom during the weekend represents a higher cleaning need. This hypothesis was tested by photographing every aspect related to the sub-processes of the cleaning process chain after the weekends, comparing the empirical findings with motion data captured by the sensors.

Effects.

The implemented system had various effects on the organisation depending on which process chain it supported, but how well it improves the general organisational performance remains inconclusive and needs to be evaluated further. Although the system generated the motion data we presumed when designing and implementing the system, the usability of this data in the context of the first process chain, determining the cleaning need of a specific classroom, is still unclear. The system was designed to have mainly informational effects on the first process chain, by presenting information to the staff which could be used to determine which classrooms that had a more significant cleaning need. The empirical findings, however, related to the hypothesis that higher motion value represents a higher cleaning need are vague. We believe that further evaluation of the system is necessary to establish its effects.

The effects on the second process chain, which was to inform the staff if a classroom is vacant and possible to clean, has mainly been of informational and automational character, with the intended ability successfully generated. According to the staff, the system correctly identifies if a classroom is vacant or if there are students present, which informs them in a way that improves their performance. Since they do not have to spend time collecting this information manually, it has automated the process chain. The longterm effects of having this ability, its possible flaws (no motion input if students are very still) and how great of a value it brings to the organisation, due to it solving a relatively minor problem, will have to be further evaluated.

The system has generated the desired ability to analyse and compare between the booked hours in the electronic booking schedule and the actual amount of activity in the classrooms, which would classify it as having a transformational effect on the organisation's ability to innovate. This ability may contribute to having informational effects which improve performance, depending on how generated data is utilised. The organisation has expressed an ambition to integrate the graphs, and sensor data with the current electronic booking schedule to easier compare the data, but this feature has yet to be implemented.

Discussion

To provide actionable guidance for the use of IoT sensors to innovate processes through digital service design, we have illustrated the applicability of a synthesised framework facilitating opportunity recognition, design and analysis of effects. The analysis provides insight both to the general applicability of the framework across the innovation process, and the bearing of specific components of the framework for IoT sensors.

Although developed for different tasks, our application of the framework suggests that it is beneficial to apply the whole chain of analysis in the different subparts of the innovation process. While the business value of IT provides support for retrospective analysis of effects [START_REF] Stenmark | Enabling Process Innovation through Sensor Technology: A Multiple Case Study of RFID Deployment[END_REF][START_REF] Visich | Empirical Evidence of RFID impacts on supply chain performance[END_REF], the desired outcomes in terms of informational, automational and transformational effects should guide the design of the digital service system. Thus, in accordance with Alters (2006) arguments regarding a holistic view in systems design, such ambitions need to be considered in the initial analysis phase. By establishing information requirements for a process chain or specific sub-processes during this phase, potential complexities related to generating the desired effects can be discovered, e.g. processes with a high level of equivocality. With a desirable effect-outcome and the information requirements necessary for generating this outcome established, we argue that this provides a more well-grounded basis for process innovation with sensors.

For sensor-based process innovation, the process information requirements in part determine the degree of automational effect that can be achieved. A process with the purpose of simply collecting or communicating one type of data may be automated in its entirety through implementing a sensor-based system. Automational effects of this kind are shown in the swimming pool case where the process chain of documenting pool water quality has the potential of being automated completely by the utilisation of sensor-based systems. This high degree of automational effects arises from alignment between information needed to complete the task and sensor capacity to produce data output. This outcome differs from a process with high equivocal information requirements, such as the process of evaluating the cleaning need where there are four subprocesses to complete the process chain. Each sub-process requires information with regards to its specific cleaning need, and the sum of the informational output from these sub-processes are then what constitutes the cleaning need of the classroom. The sensor used to support this process chain provides information of movement around the entrance to the classroom. This information of movement does not map precisely to any of the sub-process outputs of assessing a cleaning need. For example, to assess if a whiteboard needs cleaning there is a requirement of visual examination of the whiteboard and from this draw a conclusion regarding its need for cleaning. Information of movement is only a proxy variable that does not directly respond to use of the whiteboard. Similarly, to assess if the floor needs mopping, information is required on the amount of dust and dirt that is currently present on its surface. Again, the type of information provided by the sensor used does not immediately support an assessment of mopping need.

The sensor-produced data may be used to make assumptions of cleaning need based on the information of activity around the sensor, without showing a one to one relationship between the amount of movement and the equivocal cleaning need. For instance, while movement information does not show how dusty or dirty the floor is, it shows the cause of this effect: people have walked on the floor. Any value of activity data means that the floor has been walked upon. If the floor has been walked upon, it is reasonable to assume that some amount of dirt and dust have been transferred from the shoes to the surface of the floor. Thus, a value of activity data increases the need of mopping the floor.

This reasoning may be used on the other sub-processes of the cleaning case as well, though with a weaker conclusion. We have argued that movement information show a relationship with the degree of dirt on the floors. However, movement information is not directly correlated with the use of whiteboards. Recorded activity data means that there has been a person around the entrance of the classroom. It does, however, not capture the type of activity the person has engaged in, e.g. if they used the whiteboards. Similarly, the data does not show if the person(s) moving close to the classroom entrance also throw waste into the waste bin. The IoT sensor capabilities do not address these equivocal information requirements directly. Thus, conclusions regarding the cleaning need of these processes cannot be drawn solely from the information provided by the sensor. Movement information may, however, be used in conjunction with visual evaluation of the state of classrooms over time to show statistical probabilities of whiteboard cleaning need, waste bin level and table dirt. Cleaning staff collected this data during the testing phase of the cleaning case by grading the classrooms total cleaning need in conjunction with them cleaning it. This method of evaluation could be improved by splitting it into an evaluation of each sub-process, thus increasing its accuracy. There are degrees to this relationship between process information requirements and sensor data output. This relationship spans from misaligned, exemplified by assessing cleaning need of whiteboards with movement sensors, to aligned, as shown by addressing temperature requirements with a temperature sensor. Thus, we argue that IoT sensors could be implemented to processes with various degrees of success depending on the process equivocality information requirements.

High connectivity Reach

IoT-sensors by nature increases reach due to many individual data collection points that are accessible over organisational borders

High uncertainty Production

IoT-sensors can continuously produce data outside human intervention, day as night

High equivocality Relation

IoT-sensors can supply relations, however, important to match sensor capabilities with the process information requirements

Low connectivity Richness

IoT-sensors add richness and granularity due to continuous, focused data collection

Low uncertainty Consumption

IoT-sensors do not directly address consumption, but through data-analysis and visualisation of sensor-data consumption can be addressed

Low equivocality Encounter

IoT-sensors can fortify encounters through stabile data collection that supports codified knowledge and routine-based tasks

Conclusion

In this paper, we provide a synthesised framework for IoT sensor-based process innovation. The framework draws on extant theory on (1) the role of information requirements analysis in identification of IT-based process innovation opportunities and (2) effects on business value. The synthetisation of these theoretical devices enables a holistic analysis, from opportunity recognition to evaluation of achieved effects. We have explored the practical usability of the framework through an analysis of two different implementations of IoT systems and associated organisational effects. We found areas where the sensors have shown potential for process innovation and demonstrated the applicability of an information requirements perspective in an IoT context. Through this we have been able to identify areas where complexity becomes an issue for implementation of IoT systems. In particular, the results point to IoT sensors general capacity for responding to high connectivity and uncertainty requirements, and the need for aligning functionality with organisational needs to respond to high equivocality requirements. Thus, the functionality provided by IoT sensors does not reduce the importance of organisational ability for process analysis and identification of values to measure. We also identify the effects and relate them to levels of alignment between information requirements in the process and sensor capacity. Lastly, we have argued for a need to understand IoT through a micro-level perspective in organisational processes and proposed a set of practices for overcoming challenges encountered in the cases. As the field continues to grow we believe it is essential for organisations to further understand how to utilise IoT-technology when innovating their work processes.

Information Requirement Corresponding digital option characteristic Example of IoT capability investment Connectivity High

  [START_REF] Sandberg | Digital Options Theory for IT capability investement[END_REF] refer to the organisation's IT capabilities as a firm's previous investments in IT resources, such as technology or IT competence. We use the concept

	and expand upon it to fit the field of IoT and thereby refer to them as IoT capabilities.
	Table 1 shows both information requirements, their corresponding digital options and
	examples of IoT capabilities that reflect the requirement characteristic.
		Reach: the number of information	Open data sharing of sensor
		sources that can be accessed	data between organisational
		through IoT during task execution	departments,	generating
			easy access to new infor-
			mation
		Richness: the number of data	Flow sensors generating ex-
	Low	points available through IoT about	act measurements of waste
		a given object during task execu-	flow in a waste management
		tion	facility
		Production: the extent to which	Multiple sensor measuring
	High	IoT supports the creation of infor-	soil and crop health in mod-
	Uncertainty	mation from stimuli	ern agriculture
		Consumption: the extent to	Heatmaps showing move-
	Low	which IoT support translation of	ment patterns of visitors in a
		information into action taken	public building
		Relationship: extent to which IoT	Correct analysis of contex-
		supports contextual consideration	tual environment with the
	High	and development of trust by adap-	implementation of sensors
		tation and sharing of information	that measure the exact values
	Equivocality	across subsequent episodes	needed for the task
		Encounter: IoT based on a stand-	Photoelectric sensors meas-
		ardised approach without varia-	uring visitors entering and
	Low	tion across customers; limited re-	leaving a facility from a per-
		garding time and flexibility but ef-	manently fixed position
		ficient due to uniformity

Table 1 .

 1 Information Requirements and Digital Options Characteristic (Adapted from Sandberg et al. 2014)

Table 2 .

 2 LoRa Sensors utilised in projects with corresponding properties

	Sensor name	Properties
	ERS	Passive infrared (PIR) sensor registering movement in its field of
		view
	ELT-1	Analogue input sensor capable of coupling with external analogue
		measurement tools, e.g. thermometer, voltmeter, ultrasonic level
		indicator

Table 3 .

 3 Data collection overview

	Case	Interviews Observations	Workshops	Informal
					encounters
	Swimming pool	4	5	2	7
	Cleaning company	5	6	5	39

Table 4 .

 4 General descriptions of IoT capabilities addressing information requirements.


Ricardo Reis
email: reis@inf.ufrgs.br

Strategies for Reducing Power Consumption and Increasing Reliability in IoT

Keywords: Internet-of-things, Optimization, Physical Design, Fault Tolerance, Radiation Effects, Nanoelectronics

The Internet of Things (IoT) demands new challenges in the design of computing and electronics components. One of the challenges is the power reduction of this expanding network of connected devices, where the majority is permanently connected. In a large set of applications, another significant issue is reliability, especially on critical areas as health and transport. This paper shows an overview of design strategies that we have developed to reduce power consumption and to increase reliability in circuits that are components of the IoT, as the reduction of the number of transistors in IoT devices, using optimisation techniques and the physical design of circuits tolerant to radiation effects.

Introduction

The growing number of connected devices in the Internet of Things (IoT) is one of the reasons for the ever increasing increase in the number of transistors produced annually in the world. Figure 1, based on [SIA 2005], shows the number of transistors manufactured annually in the world, year by year. This impressive growth is due to 3 main factors: the increasing number of transistors integrated into a chip, the growing number of products that include embedded chips and the increasing number of manufactured copies of each product. The manufacturing cost of a transistor is relatively cheap. In [The Economist 2010] a comparison is presented between the cost of a grain of rice and the cost of a transistor. The price of a rice grain can be equivalent to the manufacturing cost of more than 125,000 transistors. This would indicate that there is no need to optimise the number of transistors in a design, since the cost of them is relatively small. But the cost of energy required for the operation of a transistor is increasing a lot. We also have to consider that a high-power consumption can reduce the lifetime of a system, as well as increase the effects of variability that can cause an integrated system to malfunction and/or also reduce its useful life. With the increasing connection of electronic and computational devices on the Internet, that is, in the Internet of Things, power consumption problems tend to get worse, and a lot. How much Power Plants we will need to cope with the IoT/IoE (Internet of Everything) world? This is a major issue.

So, an essential keyword on the Internet of Things is optimisation, especially the optimisation of power consumption, which must be addressed at all levels of abstraction in the design flow of a computer or electronic system. The total power optimisation is a summation of the optimisation done at each level of design abstraction. So, sustainable computing requires optimisation at all design levels of a computer or electronic system design.

2

Internet of Things

The term Internet of Things has already given rise to several other associated terms, such as the Internet of Health (IoH), Internet of People (IoP) and the Internet of Everything (IoE). In fact, the latter term becomes the most comprehensive, but each one of the others has some specific characteristics. When talking about the Internet of Health, which includes real-time monitoring of a person's clinical conditions, as well as chips injected in a person, the issue of reliability is a key one. And reliability is also related to power consumption in most cases. High power consumption can reduce the lifetime of a system. When it comes to the Internet of People, the issue of people's security and privacy is of great relevance. But in all cases, the importance of optimising energy consumption is growing more and more.

When considering optimisation, it means that integrated systems must increasingly be dedicated to the intended application to optimise the number of components, that means the number of transistors. Another important strategy for optimisation is the hardware and software codesign, where one can manage the compromise between performance, consumption, and reliability.

Devices connected to the Internet of Things (or the Internet of Everything), can have very different complexities. If it is analysed the complexity considering the number of components, we can find small devices with few transistors and large devices with billions of transistors. Of course, large devices will consume much more power, but we have to consider that most devices on the Internet of Things are devices with a low number of transistors. But, because they are found in large quantities, they can represent a total consumption more important than the consumption of the so-called large devices that are present in a lower number. Therefore, consumption optimisation must be performed on both large and small devices that are present in large quantities. Another aspect to consider is that some devices require the application of reliability techniques (such as those related to transport or health systems), which can increase the number of components, while other devices are not critical, such as a camera or video, where an error in viewing a pixel of an image does not cause significant problems.

Also, we can expect that many systems connected to the Internet of Everything will be Cyber Physical Systems (CPS), that are systems composed by different classes of components like electronic elements, mechanical elements, optical elements, physical sensors, chemical sensors, organic components, and many others. So, it is needed to obtain EDA tools to cope with the design of CPS composed of all these classes of devices.

Figure 2 [The Connectivist 2014] shows an estimate of the number of devices connected to the Internet since 1992 when they were about 1 million devices. By 2020 when it is estimated that there will be more than 50 billion devices connected in the network, and there are currently around 35 billion connected devices. In [START_REF] Ihsmarkit | IoT Trend Watch[END_REF]] the number of devices connected to the network in 2018 is shown by industrial and commercial sectors, where almost half is in the area of communication. The significant growth in the number of connected devices to the Internet has naturally led to a considerable increase in the energy consumed in the Internet of Things. For how long will we have the energy to meet this growing demand? Therefore, it is necessary to use techniques to minimise the energy consumption of each connected device in the Internet of Things.

The Internet of Health (IoH) is a significant way to increase the life of human beings but also to improve life quality. Some of the examples of devices to be connected to the IoH are: Glasses that can advise eye correction; Toothbrush that can find cavities and breath issues; Razor that identify acne; Pacemakers that broadcast data to cardiologist; Underwearables that can provide early detection of cancer and other anomalies; Combs that can scan for fungus and hair loss; Earphones that does measurement of hearing, analysis of emotional level; Watches able to measure parameters like blood pressure, heart rate and others. In critical areas such as the design of implanted devices (chips) in humans (Figure 3), the reliability of the implanted systems is obviously critical. Some of the techniques used are based on the triplication of circuits and the temporal analysis of the propagation of a signal. Previously, the design of fault-tolerant circuits, to cope with radiation effects, was mainly in circuits that were sent to space. With the reduction of the value of the supply voltage of integrated circuits, nowadays the integrated circuits for use at ground level are also sensitive to errors caused by the radiation incident on the earth. Therefore, in critical areas such as implanted chips in humans, it is necessary to implement radiation effects tolerance techniques [START_REF] Velazco | Radiation Effects on Embedded Systems[END_REF]]. Also, critical systems used on the Internet of Health should be tolerant to any kind of noise (internal or external to the human body). They also must have a larger lifetime as possible, for obvious reasons and also should cope with environmental variability. Another effect that causes failures in circuits throughout their life is the effect of electromigration, which can cause short circuits or rupture of connections (Figure 4). In order to increase the lifetime of the chips, it is necessary to use physical design techniques that reduce the probability of electromigration [Posser 2017]

Electronic Design Automation (EDA) Tools

The use of EDA tools is essential for optimising energy consumption and increasing reliability, as the design flow has a large set of steps as well the number of components of a cheap can reach billions off transistors. In Figure 5 we can see the floorplan of an integrated circuit, where the hotter colours show regions (hot spots) with higher energy consumption, indicating that in some points there is a significant concentration of power consumption. One way to deal with the problem is to modify the placement of the logic cells in the circuit to distribute the cells with the highest energy consumption over the entire circuit area. But this must be done without compromising the area, wirelengh and operating frequency specifications (much depends on the routing). Another way is to decrease the number of transistors, since the static consumption is related to the number of transistors [Reis 2011A].

Power Consumption Reduction by Reducing the Number of Transistors

The reduction of the power consumption of a System on a Chip (SoC) is a function of a sum of techniques and strategies of design applied in different levels of abstraction in the design flow of an integrated system [START_REF] Reis | Redução de Consumo pela Otimização de Componentes[END_REF]]. The summation of the gains is that it will set the total gain in power reduction. When we deal with the physical synthesis of a system on a chip, one technique is the optimisation of the number of components, that is, the number of transistors. In Figure 6 [Reis 2011A] we can observe two solutions for the implementation of the same equation. The first solution makes use of 4 basic logic gates (3 NOR 2-input ports and one CMOS inverter), using a total of 14 transistors. The second solution makes use of only one logic gate, which performs the same function but with only 8 transistors. That is, the second solution, having a reduction in the number of transistors, will also have a proportionally smaller static power consumption. Furthermore, in the example of Figure 6, we can see that the first solution also has 3 connections between the basic gates (and therefore even vias and contacts) that are eliminated in the second option with only one logic gate. This elimination of connections is increasingly important because it decreases the number of connections to be implemented using the different metal layers. The decrease in the number of connections decreases the density of connections and, therefore, increases the routability of the circuit and also contributes to reduce the average length of the connections, which implies in a reduction of the delay. In modern technologies, the delay in connections is so or more significant than the delay in the switching of logic gates. A greater spacing between the connections also contributes to an increase of reliability, due, for example, to the reduction of the possibility of electromigration, as already mentioned above. The reduction of the number of transistors depends on the use of efficient Electronic Design Automation (EDA) tools that transform the logical equations of a system so that in addition to mapping equations in CMOS gates, make optimum use of complex logic gates. In [Conceição 2016] we present a tool to reduce the number of transistors in a circuit through the fusion of networks of transistors that present fanout equal to 1. Also, it is fundamental the use of an automatic synthesis tool that can perform the automatic layout of any logical function. There is no use to achieve a logical optimisation if it is necessary to map (transform) the equations according to the logic gates available in a traditional cell library [which have few functions, in general, no more than 100 functions], as is still done when using traditional EDA systems. This mapping step is called technology mapping, and it represents a step of deoptimization. With this aim, we have developed automatic layout synthesis tools such as ASTRAN (Ziesemer 2015) (Figure 7), which allows automatic generation of the layout of any network of transistors [Reis 2011]. Another technique to reduce consumption is through the sizing of the transistors. Modern integrated circuit manufacturing technologies show a significant increase in static power consumption that is often greater than dynamic power consumption. One way to mitigate power consumption, especially the static one, is to carry out a sizing of transistors to optimise power consumption. In [START_REF] Reimann | Challenges of Cell Selection Algorithms in Industrial High Performance Microprocessor Designs[END_REF]] significant decreases in consumption are obtained through the use of automatic transistor sizing tools. This is also called cell selection, where the cells are selected from a cell library. In this case, cell selection means the selection of cells with a specific size and Vth (threshold voltage). In traditional cell libraries, one function has in general 3 sizings (one for less area, one for less power, and one for less delay) and 3 Vth (threshold voltage).

Transistors 8 Transistors

Reliability

As in the reduction of power consumption, in the design of critical systems, it is needed to use techniques to increase reliability at different levels of design abstraction. At the architectural level, a very applied method is the redundancy of modules, especially triple module redundancy (TMR) [START_REF] Kastensmidt | Fault-Tolerance Techniques for SRAM-Based FPGA[END_REF]]. Another is the temporal redundancy [START_REF] Nicolaidis | Time redundancy based soft-error tolerance to rescue nanometer technologies[END_REF]] where a signal traverses two paths, one with higher delay and another one with less delay. The difference of delay must be longer than the duration of a transient. Comparing the signal after traversing the two paths indicates whether there has been a transient propagation or not. At the physical level, we can apply different techniques to reduce or avoid problems such as electromigration [Posser 2015].

In the example of Figure 8, the position of the output pin in the centre (point 4) increases the lifetime of the circuit because it allows reducing the maximum density of current in the segments of the metal layer.

In [START_REF] Velazco | Radiation Effects on Embedded Systems[END_REF]] it is presented a series of works aimed at mitigating the effects of radiation on integrated circuits. In [START_REF] Kastensmidt | Fault-Tolerance Techniques for SRAM-Based FPGA[END_REF]

6

Hardware Accelerators

The evolution of computer architectures, that today means, the evolution of microprocessor architectures has been very significant. In the 1970s, one marketing argument from microprocessor producers was the number of instructions that the microprocessor could execute as well as the clock frequency of the microprocessor. In the last decades, there has been a change of paradigm, discontinuing the race for the increase of the clock frequency, because the increment of the clock means an increase of the dynamic consumption. Instead, there was an increase in the number of cores (CPUs) aiming at increasing performance. Initially with homogeneous cores and later with heterogeneous cores. Currently, we can find chips with multiple CPUs and several GPUs (as can be seen in Figure 9 [Shao 2016] showing the floorplan of the A8 microprocessor (from Apple). In this same figure, it can be observed that about half of the area is occupied with hardware accelerators, which are modules dedicated to the execution of a specific function. For example, an encryption module placed next to the output/input pins and which will encode the output data and decode the received data. So, the execution of this function will be faster, because it is done by a dedicated module (that means smaller) and with only the needed number of components to perform that function. It also will consume less power.

A more important fact is that the use of hardware accelerators leads to greater energy efficiency (allowing more sustainable computing), mainly due to the reduction in the number of components used to perform a function. At any given time, only the hardware accelerators in use at that time are being powered. So, the hardware accelerators that are not in use are disconnected from the power supply. This strategy is also known as "Dark Silicon". We can even predict architectures consisting essentially of hardware accelerators, with only one or two small CPUs to manage these hardware accelerators. The introduction of an NPU in A11 is another element characterising the heterogeneity of the SoC (chip system). And we can expect increasingly heterogeneous architectures, with dedicated modules for different operations to be performed by a SoC. In Figure 10 [Techinsights, 2017] the floorplan of the Apple A11 microprocessor is presented, where one of the modules is an NPU (Neural Processing Unit). NPU is mostly dedicated to facial recognition [START_REF] Techinsights | [END_REF], processing machine learning tasks more efficiently, consuming less energy than CPUs do. The CPUs occupy about 15% of the area of the chip and 6 GPUs occupy about 20% of the area. Most of the area is filled with the hardware accelerators. That is, it is growing in the architecture of Apple microprocessors the use of hardware accelerators.

Conclusions

To have sustainable computing, when the number of connected devices in the Internet of Things is fast increasing, it is fundamental the design of devices optimised regarding energy consumption. Most of the chips produced today use much more transistors than necessary to perform a function. So, there is a significant space for the optimisation of the number of components. In many devices related to critical applications, the application of techniques for fault tolerance is also fundamental, as nowadays circuits at ground level can have faults due to radiation effects. The reduction of power consumption must be treated at all design abstraction levels in a synthesis flow of integrated systems, from the specification of them in high-level languages to the physical synthesis. It was presented several works that were developed to reduce the power consumption and increase the reliability of integrated systems on a chip, and more details are shown in the mentioned references. The keyword in the age of the Internet of Things is optimisation.

Fig. 1 .

 1 Fig. 1. Number of transistors produced annually in the world [adapted from SIA 2005]

Fig. 2 .

 2 Fig. 2. Number of devices connected on the Internet [adapted from The Connectivist 2014]

Fig. 3 .

 3 Fig. 3. The implantation of Chip Systems in humans demands reliability and ultra-low consumption

Fig. 4 .

 4 Fig. 4. Exemplo of a Void (open circuit) and hillock (short circuit) (GEDEN, 2011).

Fig. 5 .

 5 Fig. 5. View of the consumption density on a chip

Fig. 6 .

 6 Fig. 6. Two options for the implementation of the same function [2011A Reis]

Fig. 7 .

 7 Fig. 7. Transistor Network Layout Generated Automatically [Ziesemer 2015]

  ] [Neuberger 2014] [Gennaro 2017] [Aguiar 2016] [Lazzari 2011] we present some of the results that our research group has obtained in the development of techniques aiming the design tolerant to faults due to transients, as the effects due to radiation.

Fig. 8 .

 8 Fig. 8. Changing current density with the change of position of the output pin [Posser 2015]

Fig. 9 .

 9 Fig. 9. Apple 8 Floorplan with 29 Hardware Accelerators [Anatech, 2014], [SHAO 2016]

Fig. 10 .

 10 Fig. 10. Apple 11 Floorplan with an NPU[START_REF] Techinsights | [END_REF]

Acknowledgements

We thank CNPq, FINEP, Fapergs, and CAPES for financial support for the development of our team's work, as well as the master's and doctoral students of PGMICRO and PPGC and students of Scientific Initiation who have contributed to the research works that served as the basis for this paper.


Wil M P Van Der Aalst
email: wvdaalst@pads.rwth-aachen.de

Responsible Data Science in a Dynamic World The Four Essential Elements of Data Science

Keywords: Data science, Responsible data science, Process mining, Big data

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Data Science

This extended abstract is based on a keynote given at the IFIP World Computer Congress (WCC 2018) on 18 September 2018, in Poznan, Poland. The main theme of WCC 2018 was "Information Processing in an Increasingly Connected World: Opportunities and Threats". Data science is the main driver for the changes that create these opportunities and threats. Recent reports [START_REF] Frey | The future of employment: How susceptible are jobs to computerisation?[END_REF][START_REF] Hawksworth | Will Robots Really Steal Our Jobs? An International Analysis of the Potential Long Term Impact of Automation[END_REF] indicate that many jobs will cease to exist because of advances in machine learning, artificial intelligence, robotics, and other forms of smart automation. These advances are only possible because of both the availability of data and progress in data science.

It is not easy to define data science. The data science pipeline shown in Figure 1 illustrates the breadth of the discipline. The "infrastructure" part of the pipeline is concerned with the huge volume and incredible velocity of data. Hence, the primary focus is on making things scalable and instant. The "analysis" part of the pipeline is concerned with extracting knowledge. This is about providing answers to known and unknown unknowns. 1 The "effect" part of the pipeline is concerned the impact of data science on people, organizations, and society. Here legal, ethical, and financial aspects come into play. The uptake of the Internet of Things (IoT) illustrates the pivotal role of data science. More and more devices (light bulbs, clothes, refrigerators, containers, bicycles, etc.) are connected to the internet and produce data. These devices are becoming "smart" by learning from the data collected. The Internet of Things (IoT) depends on the whole data science pipeline shown in Figure 1. We are (or will be) surrounded by smart devices collecting data and the impact of this cannot be overestimated.

In the remainder, we define the four essential elements of data science. As metaphor we use the classical four elements: "water", "fire", "wind", and "earth". According to the Empedocles, a Greek pre-Socratic philosopher who lived in Sicily in the fifth century B.C., all matter is comprised of these four elements. Other ancient cultures had similar lists, sometimes also composed of more elements (e.g., earth, water, air, fire, and aether) that tried to explain nature and complexity of all matter in terms of simpler substances. Today, we know that this is not the case. However, for data science, we are still in the phase where we are looking for the essential elements. This paper uses "water" as a placeholder for the availability of different forms of data, "fire" as a placeholder for irresponsible uses of data (e.g., threats to fairness, accuracy, confidentiality, and transparency), "wind" as a placeholder for the way that data science 1 "There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns the ones we don't know we don't know." (Donald Rumsfeld, February 12, 2002) can be used to improve processes, and "earth" as a placeholder for education and research (i.e., the base of data science) underpinning all of this. These four essential elements are discussed in the remaining sections.

2

The "Water" of Data Science

The first essential element of data science ("water") is the data itself. The exponential growth of data is evident. Figure 2 (inspired by the analysis in [START_REF] Brennenraedts | The Impact of ICT on the Dutch Economy[END_REF]) shows the rapid developments in terms of costs (things are getting exponentially cheaper), speed (things are going exponentially faster), and miniaturization (things are getting exponentially smaller). This is not limited to processing (i.e., CPU and GPU processors), but also applies to storage and communication.

Consider for example the costs of storage. To store one megabyte (MB) of data in the sixties one would need to pay one million euros. Today, one can buy a 10TB harddisk for less than 300 euro, i.e., 0.00003 cents per MB. Another example is the bandwidth efficiency, also called spectral efficiency, which refers to the information rate that can be transmitted over a given bandwidth. It is the net bitrate (useful information rate excluding error-correcting codes) or maximum throughput divided by the bandwidth in hertz of a communication channel or a data link. The spectacular progress of our data handling capabilities illustrated by Figure 2, explains why data science has become on of the key concerns in any organization. In the sixties, we only had a few "drops of data" whereas today we are facing a "tsunami of data" flooding our society.

Clearly, data science has its roots in statistics, a discipline that developed over four centuries [START_REF] Van Der Aalst | Process Mining: Data Science in Action[END_REF]. John Graunt (1620-1674) started to study London's death records around 1660. Based on this he was able to predict the life expectancy of a person at a particular age. Francis Galton (1822-1911) introduced statistical concepts like regression and correlation at the end of the 19th century. Although data science can be seen as a continuation of statistics, the majority of statisticians did not contribute much to recent progress in data science. Most statisticians focused on theoretical results rather than real-world analysis problems. The computational aspects, which are critical for larger data sets, are typically ignored by statisticians. The focus is on generative modeling rather than prediction and dealing with practical challenges related to data quality and size. When the data mining community realized major breakthroughs in the discovery of patterns and relationships (e.g., efficiently learning decision trees and association rules), most statisticians referred to these discovery practices as "data fishing", "data snooping", and "data dredging" to express their dismay [START_REF] Van Der Aalst | Process Mining: Data Science in Action[END_REF][START_REF] Breiman | Statistical Modeling: The Two Cultures[END_REF][START_REF] Tukey | The Future of Data Analysis[END_REF].

Put differently; most statisticians were focused on techniques to make reliable statements given a few "drops of data". Such viewpoints turned out to be less effective when dealing with "tsunamis of data". This can be generalized to storage and transition and also applies to costs and speed.

3 The "Fire" of Data Science

The second essential element of data science ("fire") refers to the dangers of using data in an irresponsible way. Data abundance combined with powerful data science techniques has the potential to dramatically improve our lives by enabling new services and products, while improving their efficiency and quality. Many of today's scientific discoveries (e.g., in health) are already fueled by developments in statistics, mining, machine learning, artificial intelligence, databases, and visualization. At the same time, there are also great concerns about the use of data. Increasingly, customers, patients, and other stakeholders are concerned about irresponsible data use. Automated data decisions may be unfair or nontransparent. Confidential data may be shared unintentionally or abused by third parties.

From 2015 until 2017, the author led the Responsible Data Science (RDS) initiative where the strongest Dutch data science groups joined forces to address problems related to fairness, accuracy, confidentiality, and transparency (www.responsibledatascience.org). The goal of RDS is to show that data science techniques, infrastructures and approaches can be made responsible by design.

Responsible Data Science (RDS) revolves around four main challenges:

-Data science without prejudice -How to avoid unfair conclusions even if they are true? -Data science without guesswork -How to answer questions with a guaranteed level of accuracy? -Data science that ensures confidentiality -How to answer questions without revealing secrets? -Data science that provides transparency -How to clarify answers such that they become indisputable?

The term green data science was introduced for cutting-edge solutions that enable individuals, organizations and society to benefit from widespread data availability while ensuring Fairness, Accuracy, Confidentiality, and Transparency (FACT) [START_REF] Van Der Aalst | Responsible Data Science: Using Big Data in a "People Friendly[END_REF]. Naïvely one could think that "fire" can be controlled by "water", however this is not the case. When considering RDS, it is better to consider data as "oil" rather than "water". It needs to be controlled and stored carefully.

There is a need for new and positive data science techniques that are responsible (i.e., "green") by design. This cannot be solved by stricter laws. Using the metaphor of "green energy": We should not be against the use of energy ("data"), but address the pollution caused by traditional engines. Fortunately, there are plenty of ideas to make data science green. For example, discrimination-aware data mining [START_REF] Pedreshi | Discrimination-Aware Data Mining[END_REF] can be used to ensure fairness and polymorphic encryption can be used to ensure confidentiality.

The "Wind" of Data Science

The third essential element of data science ("wind") is concerned with the way data and processes interact. Storing and processing data is not a goal in itself. Data are there to support processes. The campaign "The best run companies run SAP" illustrates that the purpose of information systems is to ensure that processes run well. Data science can help organizations to be more effective, to provide a better service, to deliver faster, and to do all of this at lower costs. This applies to logistics, production, transport, healthcare, banking, insurance, and government. This also applies to individuals. Data science will increasingly support our personal workflows and take over tasks, or at least support them. Data ("water") can be used to manage and support processes ("wind") through the use of data science technologies.

An emerging technology linking "water" and "wind" is process mining [START_REF] Van Der Aalst | Process Mining: Data Science in Action[END_REF]. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and datacentric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically) [START_REF] Van Der Aalst | Process Mining: Data Science in Action[END_REF]. The process-mining spectrum is broad and includes techniques for process discovery, conformance checking, prediction, and bottleneck analysis. These techniques tend to be very different from mainstream data mining and machine learning techniques which are typically not process-centric.

Consider for example the topic of Robotic Process Automation (RPA). RPA is an umbrella term for tools that operate on the user interface of other computer systems in the way a human would do. RPA aims to replace people by automation done in an "outside-in" manner [START_REF] Van Der Aalst | Robotic Process Automation[END_REF]. This differs from the classical "inside-out" approach to improve information systems. Unlike traditional workflow technology, the information system remains unchanged. The robots are replacing humans while leaving the back-end systems intact. RPA is a way to support processes in a more cost-effective manner. However, this requires learning what humans do by observing them. Data science approaches like process mining can be used to learn the behavior of people doing routine tasks. After the desired behavior has been "played in", it can be "played out" to handle new cases in an intelligent manner.

RPA illustrates that data science will lead to new trade-offs between what humans do and what robots do [START_REF] Frey | The future of employment: How susceptible are jobs to computerisation?[END_REF][START_REF] Hawksworth | Will Robots Really Steal Our Jobs? An International Analysis of the Potential Long Term Impact of Automation[END_REF]. These trade-offs are interesting: How to distribute work between given breakthroughs in data science? Obviously, the question needs to take the "fire" dimension into account.

The "Earth" of Data Science

The fourth essential element of data science ("earth") is concerned with the foundations of a data-driven society: education and research. Education (in every sense of the word) is one of the fundamental factors in the development of data science. Data science education is needed at any level. People need to be aware of the way algorithms make decisions that may influence their lives. Privacy discussions reveal the ignorance of policy makers and end users. Moreover, to remain competitive, countries should invest in data science capabilities. This can only be realized through education. Data science research plays a similar role. On the one hand, it is key for our education. On the other hand, research is needed to address the many technological and societal challenges (e.g., ensuring fairness, accuracy, confidentiality, and transparency).

Currently, eight of the world's ten biggest companies, as measured by market capitalization, are American: Apple, Alphabet (incl. Google), Microsoft, Amazon, Berkshire Hathaway, Facebook, JPMorgan Chase, and Bank of America. 2The two remaining companies are Chinese: Alibaba and Tencent Holdings. This shows the dominance of a few countries due to investments in IT. Most of the companies are relatively new and emerged through the smart use of data. Amazon and Alibaba are dominating the way we buy products. Google is controlling the way we search. Facebook is controlling the way we socialize. Apple, Alphabet, and Microsoft are controlling the platforms we use (iOS, Android, and Windows). Consider for example Facebook. On the one hand, many people are expressing concerns about the use of data. On the other hand, Facebook has Fig. 3. The "water", "fire", "wind", and "earth" of data science. over 2 billion monthly active users that provide personal information in order to use social media. One of the problems of data science is that due to economies of scale "the winner takes it all". This may also apply to education, e.g., on Coursera a few US universities are dominating data science education.

Data science literacy and major public investments are needed to address these concerns. This cannot be left to "the market" or solved through halfhearted legislation like the European General Data Protection Regulation (GDPR) [START_REF]Proposal for a Regulation of the European Parliament and of the Council on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data (General Data Protection Regulation)[END_REF].

Epilogue

This extended abstract aimed to present some of the key messages of the keynote presentation for the IFIP World Computer Congress (WCC 2018). It stresses the importance of data science for people, organizations, and society. Just like computer science emerged as a new discipline from mathematics in the early eighties, we can now witness that the data science discipline is emerging from computer science, statistics, and social sciences.

In this paper, we discussed the four essential elements of data science: "water" (availability, magnitude, and different forms of data), "fire" (irresponsible uses of data and threats related to fairness, accuracy, confidentiality, and transparency), "wind" (the way data science can be used to improve processes), and "earth" (the need for data science research and education). By presenting data science in this manner, we hope to get more attention for process-centric forms of data science (e.g., process mining), responsible data science, data science education, and data science research. The dominance of a few companies and countries

Fig. 1 .

 1 Fig. 1. The data science pipeline showing that different capabilities are needed to turn data into value.

Fig. 2 .

 2 Fig.2. Moore's law predicts an exponential growth of the number of transistors per chip. This can be generalized to storage and transition and also applies to costs and speed.

Based on market capitalization data by Bloomberg on

March 2018.

when it comes to data science is undesirable and requires the attention of politicians and policymakers. The IFIP could and should play an active role in this discussion.