Ari Alamäki
email: ari.alamaki@haaga-helia.fi

Lili Aunimo
email: lili.aunimo@haaga-helia.fi

Harri Ketamo
email: harri.ketamo@headai.com

Lasse Parvinen
email: lasse.parvinen@terveystalo.com

Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

Keywords: interactive machine learning, unstructured text, big data, anonymisation, information richness, collaboration, privacy

This case study focuses on an experiment analysing textual conversation data using machine learning algorithms and shows that sharing data across organisational boundaries requires anonymisation that decreases that data's information richness. Additionally, sharing data between organisations, conducting data analytics and collaborating to create new business insight requires inter-organisational collaboration. This study shows that analysing highly anonymised and professional conversation data challenges the capabilities of artificial intelligence. Machine learning algorithms alone cannot learn the internal connections and meanings of information cues. This experiment is therefore in line with prior research in interactive machine learning where data scientists, specialists and computational agents interact. This study reveals that, alongside humans, computational agents will be important actors in collaborative networks. Thus, humans are needed in several phases of the machine learning process for facilitating and training. This calls for collaborative working in multidisciplinary teams of data scientists and substance experts interacting with computational agents.

Introduction

Sharing data between organisations is becoming an important process for the cocreation of value in collaborative networks [START_REF] Alamäki | Business Roles in Creating Value from Data in Collaborative Networks[END_REF][START_REF] Lindquist | Data Science Under GDPR with Pseudonymisation in the Data Pipeline[END_REF]. But in most business or scientific research cases, sharing data over organisational boundaries requires anonymisation. For example, the European General Data Protection Regulation (GDPR) allows the secondary use of data, but pseudonymisation or anonymisation is required depending on the intended use for that information (EU GDPR, Article 89). There is, however, little research on how anonymisation affects the way in which data are used or how machine learning algorithms are able to process highly anonymised, unstructured textual data.

Sharing data between multiple organisations is becoming more and more common, with organisations looking to enrich their own data analysis by combining it with third party data. Gaining deeper business insight and ensuring the reliability of results both require the processing of several data sets and a collaborative network of organisations. Many organisations are also seeking new value by combining inter-organisational data and advanced data analytics methods such as machine learning [START_REF] Alamäki | Business Roles in Creating Value from Data in Collaborative Networks[END_REF]. Sharing data will become a crucial process in the digital transformation of business processes in the value chains of ecosystems. Additionally, machine learning is becoming a vital part of the process of digitising data that has been analysed.

There are numerous benefits to sharing data sets for collaborative networks of organisations. But there are also challenges. One is data privacy, anonymisation and the loss of information richness. Preserving data privacy is very important from the points of view of both lawful use and trust. Very often, data that have monetary value include confidential information. Organisations protect their customers' privacy by anonymising data before they use them in their own business processes, but they also anonymise data that are shared with their partner companies. However, anonymisation typically reduces the information richness of the data and thus also the value that can be created from the data.

Prior research on the relationship between anonymisation and information richness in sharing data between organisations is scant. This is even more true in the realm of natural language data. Organisations are increasingly sharing natural language text data, for example, data from voice messages, chat conversations and customer communications. The techniques for anonymising natural language data differ significantly from the many techniques used for anonymising sensitive structured data, such as k-anonymity [START_REF] Sweeney | k-anonymity: A Model for Protecting Privacy[END_REF], a traditional anonymisation model that has been used when sharing aggregated health records. In the field of electronic health record anonymisation, the trade-off between privacy and loss of accuracy is a well-studied problem [START_REF] Lu | Privacy-Preserving Access Control in Electronic Health Record Linkage[END_REF]5].

As with structured data, anonymisation the identity of any text data user may significantly decrease those data's information richness. However, the problem of how to preserve information richness while conserving data privacy has not been widely studied in relation to textual natural language data. This has resulted in two practical consequences: 1) Sensitive textual data are not shared outside the organisation at all, in the fear of breaking privacy regulations; or 2) if the data are shared, they are anonymised using unnecessary obfuscating anonymisation methods, resulting in a dramatic loss in data richness.

The goal of this experiment was to study machine learning and highly anonymized conversation data in the inter-organizational setting. This study also seeks to define information richness in textual conversation data, study the relationship between data anonymisation and information richness, and, lastly, describe the machine learning experiments related to using highly anonymised conversation data. Additionally, it suggests a model for how machine learning algorithms could create useful insight from highly anonymised textual conversation data.

The study contributes to discussions related to using big data, information management and artificial intelligence to automate business processes. The paper is organised as follows. After this introduction, Section 2 reviews the essence of anonymisation and information richness in conversation data. Section 3 describes the research method and data used in the case study. In Section 4, we then present our findings. In conclusion, Sections 5 and 6 discuss the contributions this study makes to the field.

Anonymisation and Information Richness

Information richness and conversation data

In-person conversations between customers and service providers use a wide range of symbolic systems and therefore contain semantically rich information. Conveying verbal and non-verbal messages in these conversations facilitates mutual understanding. When conversation takes place via the online chat, information is not semantically as rich as in face-to-face situations. However, using video as a part of a chat conversation does enable non-verbal cues to be transmitted and interpreted, allowing for a less ambiguous understanding than with simple textual information. Textual interaction does not create logical connections between various symbolic systems and cannot convey the meanings of conditional events or causes as well as multimedia or face-to-face interaction [START_REF] Lim | The Effect of Multimedia on Perceived Equivocality and Perceived Usefulness of Information Systems[END_REF][START_REF] Salomon | Interaction of Media, Cognition, and Learning: An Exploration of How Symbolic Forms Cultivate Mental Skills and Affect Knowledge Acquisition[END_REF]. This means that analysing chat conversations that include only textual information results in a decrease in information richness as compared to analysing multimedia or physical conversations. In addition, the anonymisation of textual conversation data decreases semantic information.

The concept of information richness [START_REF] Daft | Information Richness: A New Approach to Managerial Behavior and Organisation Design[END_REF][START_REF] Daft | Organisational Information Requirements, Media Richness and Structural Design[END_REF] provides a theoretical framework for discussing the richness of conversational data before and after anonymisation. Information richness refers to the data mediums, such as text, audio or voice, that deliver informational or emotional cues. The structure of text includes the logical connections and cues that form stories and meanings. Information richness, also referred to as media richness, is an objective property of media that indicates the extent to which a medium can facilitate understanding or interpretation within a specific amount of time [START_REF] Sun | The Design of Instructional Multimedia in E-Learning: A Media Richness Theory-based Approach[END_REF][START_REF] Alamäki | Triggering Effects of Mobile Video Marketing in Nature Tourism: Media Richness Perspective[END_REF]. Information richness does not have a causal connection to the actual performance of communication [START_REF] Dennis | Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality[END_REF]. Thus, richness of information does not directly correlate to the richness of data being used in data analytics. One level of information richness may also result in different levels of understanding for a particular piece of communication [START_REF] Mayer | Multimedia Learning[END_REF][START_REF] Fiorella | Effects of Observing the Instructor Draw Diagrams on Learning from Multimedia Messages[END_REF]. Similarly, results may change over time [START_REF] Tan | Conveying Information Effectively in a Virtual World: Insights from Synthesized Task Closure and Media Richness[END_REF], as information is also context-dependent.

Anonymisation and data analytics

The European Union (EU) [START_REF]679 of the European parliament and of the council of 27 April 2016[END_REF] defines personal data as information concerning an identified or identifiable natural person. A person can be identified directly or indirectly by these data, which could include an "identification number, location data, an online identifier or […] one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person" [START_REF]679 of the European parliament and of the council of 27 April 2016[END_REF].

Data anonymisation refers to the process of obfuscating data so that they cannot be used to identify any individual. Personal identifiers can be categorised into two classes: 1) identifying attributes, such as social security numbers, names, driver license IDs, etc.; and 2) quasi-identifiers, which are a combination of key attributes that can be used to narrow down identity to a certain individual. The terms de-identification and pseydonymisation are also used in this area. De-identification is sometimes used interchangeably with anonymisation [5], while anonymisation is sometimes used in a stronger sense, denoting an irreversible form of de-identification [START_REF] Garfinkel | De-identification of Personal Information[END_REF]. In this article, we use the latter interpretation of anonymisation. Pseudonymisation refers to the process of personal identifiers being replaced with artificial identifiers. Pseudonymised data can be re-identified if additional information is available. Encrypting is a commonly used pseudonymisation technique. An important difference between anonymisation and pseudonymisation is that pseudonymised data still fall under the scope of privacy legislation, while anonymised data do not [START_REF]European Medicines Agency Data Anonymisation: A Key Enabler for Clinical Data Sharing[END_REF].

In medical fields, structured data are typically shared between organisations in integrated health care and public health studies. Based on consent and ethical approval, patient records can be used for secondary purposes that include clinical trials and studies, even if the data in those records are not anonymised. However, as clinical data are increasingly shared between organisations, the techniques used to anonymise those data need more attention. When different datasets are linked with each other and when intelligent algorithms are used to mine the data, even anonymised data records can contain attributes that allow for an individual's identity to be narrowed down.

There are several methods for anonymising structured data. These include directory replacement, masking, scrambling and blurring. There are also several measures designed to tackle the problem of indirect identifiers, a problem which arises when datasets are joined (in the medical field, this is called record linkage) so that a new dataset includes records that identify an individual even if all direct identifiers have been removed. For example, if information about patients' native languages is added to a set of records that have been linked, a single patient with a unique combination of nationality and native language may be identifiable in that combined dataset. The problem with establishing sound methods for removing all indirect identifiers from a single dataset is that it is not always possible to predict which sensitive datasets might be combined in the future. Changes to datasets over time also affect the indirect identifiers. Methods based on k-anonymity are a well-known solution for deidentification of combined datasets [START_REF] Sweeney | k-anonymity: A Model for Protecting Privacy[END_REF]. K-anonymity requires that every individual must be indistinguishable from at least k other individuals within that dataset, where a greater k value correlates with stronger de-identification levels in the data.

Joining two datasets, i.e. record linkage, often occurs when data originating from different organisations is combined for research purposes [START_REF] Winkler | Overview of Record Linkage and Current Research Directions[END_REF]. The main principle behind secure record linkage is that information identifying a patient is separated from actual health-related information. This has resulted in the creation of independent linkage centres that ensure this principle is adhered to [START_REF] Winkler | Overview of Record Linkage and Current Research Directions[END_REF].

As stated before, research on the effects of data anonymisation on the information richness of textual data is scarce. As several authors have stated, de-identifying free text is more complex that de-identifying structured data [see, for example, 20]. Cardinal [START_REF] Cardinal | Clinical records anonymisation and text extraction (CRATE): an opensource software system[END_REF] presents a method for anonymising psychiatric patients' textual records which is based on fuzzy matching of recognizable phrases. This method uses cryptography to map patient identifiers to research identifiers (also called pseudonyms).

Methods

The case experiment

In this study, we analyse a case in which anonymised natural language chat data were shared between two organisations. These data were analysed using machine learning and neural network methodologies. The organisations represent different roles in the value chain of an ecosystem, making this case particularly relevant when considering the challenges of anonymisation and information richness. The study also provides a case experiment for using machine learning to analyse highly anonymised conversation data.

We used the Headai-artificial intelligence platform (https://www.headai.com/), which utilises machine learning methods. The platform combines semantic neurocomputing and learning algorithms to create semantic neural networks and deep insight based on unstructured or structured data. The data used in the study consist of medical information, an example of sensitive information with a high requirement for anonymisation. The data included 57,000 dialogue-loops and more than 800,000 trigger-response pairs. The data were anonymised using strict standards, removing all indications of personal information. After that, machine learning methods were applied to the data.

The case study method

Since the aim of this research is to develop a new understanding of the relationship between anonymisation and information richness, the method we adopted is the case study approach [START_REF] Eisenhardt | Theory Building from Cases: Opportunities and Challenges[END_REF]. We extended our research approach to include abductive qualitative research methods, [START_REF] Dubois | Systematic Combining: An Abductive Approach to Case Research[END_REF] since our goal is to build a new model that assists companies in managing anonymisation without losing information richness. The abductive research method enabled the researchers to build explanations from the findings and elaborate on a conceptual model that combines a literature review and the study's empirical findings. This method also enabled researchers to simultaneously process prior literature on anonymisation and information richness and the analysis of the data gathered in the experiment [START_REF] Eisenhardt | Theory Building from Cases: Opportunities and Challenges[END_REF]. Using an iterative research process allowed for a deeper understanding of the experiment results while also contributing to the model of anonymisation.

The machine learning experiment procedure

This scientific research experiment grew out of the needs of a specific healthcare service provider. This organisation wanted to learn about patterns in chat conversations between patients and healthcare professionals. These chat conversations followed similar question-and-answer formats. The goal was to find patterns that could later be used to automate or improve customer experience, or to streamline business processes by improving information management practices related to the chat conversations. The conversation data were highly anonymised before being provided to data scientists.

Fig. 1. Experiment procedure

The anonymised data were tested using analytic methods including Bayesian machine learning, support vector machines and feedforward networks. They were then prepared for deeper analyses that would increase the understanding of conversation dialogues. Data were used to train machine learning algorithms, but due to the high levels of anonymisation, special vocabulary and open conversation flows, several humanmachine loops were needed to identify the dialogue used to create patterns.

Results

Our experiment showed that highly anonymised sensitive data included significantly less information than original datasets. In fact, the anonymised dataset was another dataset compared to the original dataset, in practice. The anonymised dataset excluded the job titles or roles of patients and healthcare experts, since this might have included personal information such as names, titles or organisations. Although machine learning did recognise question-answer pairs, it did not recognise which conversation partner was asking a question and which was answering and artificial intelligence could not conclude who was a patient and who a healthcare (although a human could easily make this determination based on the conversation content). The following question-answer pair illustrates the challenge that machines without the necessary algorithms have in concluding which conversation partner represents which role, as compared to human listeners: "My children had fevers and coughs yesterday" /"Would you like to make an appointment with a paediatrician?"

The experiment showed that, although this conversation was a professional discussion between a patient and healthcare expert, it nevertheless included lots of conversation with "blank substance," which we also call noise. This type of content includes words such as "hello", "hi", "how can I help you?", "what is your address?" and "video does work". This meant that data modelling using existing machine learning methods, such as Bayesian machine learning, support vector machines and feedforward networks, did not immediately detect meaningful patterns of conversation. These machine learning methods work well in analyses of question-answer data or news data, which include sentences with straightforward meanings and textual data with a lot of different kinds of content. The preliminary analyses revealed that the kind of professional conversation in this case study includes a lot of noise. This needed to be taken into account in order for the machine learning algorithms to be able to successfully detect which repeated question-answer pairs were useful for creating new business insight as opposed to being only noise.

Unlike conversations in public discussion forums, the data in this study followed a linear path. Noise was removed from the analyses, which helped create larger entities and more meaningful question-answer pairs. In this phase, human analysis was needed to differentiate noise from substantive conversation. After this, the basic Bayesian machine learning algorithms began to work properly and were improved by the use of reinforcement learning principles. Additionally, an analysis that recognised the meanings of words also helped to find the essential topics across all conversations. These findings could help service providers incorporate additional useful information into chat conversations, for example, related articles or instructions.

Discussion

Our experiment showed that applying machine learning to highly anonymised data requires several human-machine loops to aid in training the artificial intelligence software that is being used. This finding supports Holzinger's [START_REF] Holzinger | Interactive Machine Learning for Health Informatics: When Do We Need the Human-in-the-loop?[END_REF] research, which showed that using machine learning to generate meaningful results from sensitive and complex medical information requires several rounds of expert training. In interactive machine learning, human agents (experts) interact with computational agents in order to train those computational agents to create meaningful and correct analyses. The human agent or expert also needs to perform a final check to ensure the meaningfulness of analytic results. This kind of machine learning is especially relevant in new scenarios where neither data scientists nor experts have prior experience. A new approach is needed for developing and training machine learning algorithms so that they compute in a meaningful way. For example, analysing of professional conversation data or abstract and domain-specific data require typically several human-machine interactions.

There is little existing literature on the role of anonymisation and machine learning. This study has filled that gap by showing that anonymisation causes problems for machine learning. Unlike prior research, the present study examined highly anonymised data and its information richness using real-life data. Service providers need to anonymise highly sensitive conversation data-typically natural language data (text, voice, audio, image) that includes personal information or other identifying information not located in separated columns in the database. These data differ from structured textual data that locates in database cells according to the data model of software system. Locating direct identifiers that need to be anonymised is significantly easier in structured data sets, because the identifying data are located in specific columns and cells. For example, in unstructured conversation data, it is difficult to locate all personally identifying information because names, addresses or other physical, physiological, genetic, mental, economic, cultural, social or ethnic data are a part of the conversation. In addition to recognizing direct identifiers such as proper names, the data should also preserve at least some of the content of the information, such as different actor types (patient, physician, possibly a patient's relatives, etc.). If these were recognized in the data, the anonymisation method could consist of replacing personal identifiers with general terms, for example, all patient names may read "patient", all home addresses "home address" and so on. However, if simplistic anonymisation methods are used, this creates an unintended loss of information cues and their logical connections, which are needed for a full understanding of the meanings of sentences. One advantage of unstructured conversation data is that they provide richer conversation samples, unlike data that is entered into pre-defined information categories.

The findings of this study pointed out that anonymisation resulted in some loss of significant informational cues and that this, in turn, destroyed sentence logic and decreased information richness. This is not necessarily a problem for humans, who can easily determine which question-answer pairs belong together, however, machine learning interprets words mechanically and needs informational cues in order to create patterns. For example, machine learning needs to conclude who is a patient in a conversation. If informational cues do not have a clear logical connection to meaning, then machine learning algorithms cannot determine their internal connections. An issue may be, for example, stated as suggestion, example, conclusion, diagnostic or warning in the conversation. Thus, human agents or experts are needed in several phases of the machine learning process in order to facilitate and train machine learning. This consists of an iterative process of selecting the proper methods by running several trial-anderror loops that can map concepts logically and locate meaningful patterns. This calls for collaborative and multi-disciplinary teams of data scientists and substance experts interacting with computational agents. For example, substance experts can explain to data scientists why conversation goes forward with certain logic or why some experts ask questions before they present their recommendations.

Collaborative networks are data-rich environments that have started to adopt new technologies such as artificial intelligence and data management [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0. In: Collaboration in a Data-Rich World[END_REF][START_REF] Serrano | A Framework to Support Industry 4.0: Chemical Company Case Study[END_REF][START_REF] Valkokari | Business Impacts of Technology Disruption -A Design Science Approach to Cognitive Systems' Adoption Within Collaborative Networks[END_REF]. These findings contribute to discussions around the ways in which data are often used across collaborative networks. Healthcare sector is one of industries that are adopting data management and machine learning within collaborative networks [START_REF] Macedo | Cognitive Services for Collaborative mHealth: The OnParkinson Case Study[END_REF]. Healthcare and social welfare data, for example, can be shared and used in these networks, but it must be anonymised. This study demonstrates that the anonymisation of unstructured clinical or patient textual data often results in a significant loss of information richness. Thus, the secondary use of highly anonymised data creates potential reliability and validity problems for the interpretation of results, if these issues are not taken into account when conducting machine learning analytics, especially given that artificial intelligence software cannot automatically manage these challenges. Healthcare companies can, in addition, request their customers' consent to use their data for the company's own purposes, which allows for the use of richer information. The kind of anonymisation required when sharing personal data across organisational boundaries should pay particular attention to linguistic challenges and differences. For example, some languages have several word endings that pose a challenge when to locate all personal identifiers in a set of unstructured textual data. Data are anonymised, and their processing falls outside the scope of GDPR when it no longer includes any identifiable personal data [START_REF]European Medicines Agency Data Anonymisation: A Key Enabler for Clinical Data Sharing[END_REF].

The proposed method for anonymising unstructured textual data uses natural language processing techniques to identify all personal identifiers and to classify them according to a domain-specific ontology. Once this has been done, all personal identifiers are replaced with terms from that ontology. The first phase of the identification of personal identifiers is achieved using an off-the-shelf named entity recognition software. Named entity recognition software uses lemmatization or the stemming of words, as well as syntactic sentence analysis, to classify entities into categories such as: names of persons, organisations, locations, expressions of time, currency and other numerical expressions (see, for example, the GATE software created by the University of Sheffield). When these named entities have been identified, a human specialist is brought in to produce training data for the domain-specific personal identifier recognizer. This involves marking in the processed text all entities listed in the domain-specific ontology, which contains a hierarchy of terms. Based on this training corpus, patterns for identifying ontology-specific personal identifiers are created. Anonymisation is achieved by replacing the specific entities recognised in the text with the corresponding term from the ontology.

In many practical cases, researchers end up facing an overly anonymised dataset. This is typically because organisations wish to stay on the safe side of privacy regulations. While this is understandable from the organisation's point of view, for a researcher, this often strips the data of most of its utility. To tackle this challenge, we propose using an interactive machine learning method with a human specialist in the loop to assist the algorithm. This method uses machine learning in an iterative manner. After the first iteration with initial training data, a human specialist inspects the results and makes one systematic improvement to the training data. The goal of this improvement is to add to the data's information richness. For example, in our case study, the first iteration consisted of a human agent classifying the parts of the conversation as either belonging to the physician or the patient. After this, the algorithm is run again and the iteration starts from the beginning. This iterative process ends either when the results of the machine learning algorithm are satisfactory with regard to the task at hand or when the human expert cannot do any systematic improvements to the training data.

Conclusion

This study's results contribute to debates related to information processing and management and artificial intelligence in several ways. First, by reviewing the relationship between anonymisation and information richness in unstructured conversation data and secondly, by demonstrating that an interactive machine learning method in which humans and computational agents collaborate is the best mode of analysing highly anonymised conversation data.

These findings highlight that the digital transformation of business intelligence processes is not linear, but that it instead requires multi-disciplinary teamwork in interorganisational settings. Incorporating artificial intelligence into business processes requires an understanding the role anonymisation plays in information richness and machine learning methods. The insight generated by artificial intelligence is directly dependent on the ways in which data and human-machine interaction takes place. This study's most basic limitation is that its reliance on a specific case study limits the results' transferability. Nonetheless, the findings provide a basic understanding of anonymisation, information richness and interactive machine learning. The present research raised questions concerning interactive machine learning which merit further examination. This study's results should also encourage researchers to conduct empirical research into how best to involve topic experts in the process of interactive machine learning, in particular when data scientists are not able to solve all domainrelated conceptual and procedural problems.

In future research, the method proposed for anonymising unstructured textual data should be augmented with the capability to measure the level of anonymisation achieved with the data at hand. It should also be possible to define the desired level of anonymity beforehand. These two steps could be achieved by forming a structured database record based on each dialogue loop. The columns would consist of named entity classes and the rows would consist of the corresponding ontology-specific named entities extracted from a dialogue loop. The k-anonymity level of the anonymised data set could then be measured. If that level is lower than desired, the anonymisation method could replace the currently used ontology terms in the data with the original term. For example, the expression of location "City name: Kauniainen: role: home address" would be replaced by the "District name: Uusimaa, role: home address". In the original data, the location was the exact home address.

Acknowledgments. The authors would like to thank the Big data big business-project, all the parties behind the project, and Business Finland -for its support for this study.


Riku Viri
email: riku.viri@tuni.fi

Lili Aunimo
email: lili.aunimo@haaga-helia.fi

Heli Aramo-Immonen
email: heli.aramo-immonen@oru.se

Connected and Multimodal Passenger Transport through Big Data Analytics: Case Tampere City Region, Finland

Keywords: big data, analytics, collaborative network, API, cyber-physical social system, passenger transport, mobility, open data

Passenger transport is becoming more and more connected and multimodal. Instead of just taking a series of vehicles to complete a journey, the passenger is actually interacting with a connected cyber-physical social (CPS) transport system. In this study, we present a case study where big data from various sources is combined and analyzed to support and enhance the transport system in the Tampere region. Different types of static and real-time data sources and transportation related APIs are investigated. The goal is to find ways in which big data and collaborative networks can be used to improve the CPS transport system itself and the passenger satisfaction related to it. The study shows that even though the exploitation of big data does not directly improve the state of the physical transport infrastructure, it helps in utilizing more of its capacity. Secondly, the use of big data makes it more attractive to passengers.

Introduction

Passenger transport is becoming more and more connected and multimodal. Instead of just purchasing a ticket to a single means of transportation, the future passenger will purchase a mobility service that will take him from the point of departure to the point of arrival. A multimodal transport service combines several different ways of transport -such as bus, train and a bicycle ride -under a single transport service. This multimodal and connected transport service is regarded a MaaS (Mobilitity as a Service) if the following requirements are met: 1) the physical transport service is augmented with other services such as trip planning, reservations and payments, 2) the services can be used through a single interface, and 3) the services are packaged into a tailored mobility package similar to a monthly mobile phone contract [START_REF] Jittrapirom | Mobility as a service: A critical review of definitions, assessments of schemes, and key challenges[END_REF]. ABI Research [START_REF]ABI Research forecasts global mobility as a service revenues to[END_REF] estimates that MaaS revenues will exceed $1 trillion by 2030 and customers' mindset is moving towards buying a service instead owning.

Currently, during the journey, the passenger will typically use several different means of transportation and hopefully receive timely information concerning his itinerary. Ticket purchases, information seeking and sharing as well as real-time communications regarding the purchased journey will all performed using a digital application. Thus, instead of just taking a series of vehicles to complete a journey, the passenger will actually be interacting with a connected cyber-physical [see e.g. 3] social (CPS) transport system. The system is constantly producing stream data, such as IoT data from the vehicles, mobility data of the passengers and interaction data created by passengers and transport service providers. When all this data is combined with static and standardized data such as public transport trajectory, ticket fare, service provider and geographic data, we have a big data landscape on which the connected CPS transport ecosystem can be built. The different layers of a CPS transport system are illustrated in Figure 1.

Fig. 1. The CPS transport system divided into three layers from the point of view of the passenger interacting with the system. Each layer and dimension of the CPS transport system is illustrated with an example.

The research on multimodal collaborative CPS transport systems is scant. There are existing models for dealing with some aspects of the multimodal collaborative transport systems. One example is the proposal for a common architecture for payments handling in such a system [START_REF] Osório | On Reliable Collaborative Mobility Services[END_REF]. However, there is no systematic study about all aspects of a multimodal collaborative CPS transport system. In this paper, we study through a case study the data-architecture of a multimodal collaborative CPS transport system in the Tampere city region. Special attention is given to all available open data -stream and static as well as to open interfaces that can be used to control the system (such as the traffic light API) as well as to how passengers interact with the data using digital applications. Based on this, a novel data architecture for a big data driven multimodal and connected CPS transport system is developed.

There are currently several initiatives that foster the advent of transport 4.0. Transport 4.0 means that different and previously disconnected forms of transportation are being integrated into one single passenger interaction that combines several modalities of transport and that takes the passenger from door-to-door. In this paper, we adhere to the general definition of Industrie 4.0 [START_REF] Hermann | Design principles for industrie 4.0 scenarios[END_REF] from which the concept of Transport 4.0 is derived. We also regard MaaS as a business model that implements the concept on Transport 4.0. The initiatives towards Transport 4.0 are related to either legislation or availability and standardization of transportation related data and application programming interfaces (APIs) or both. One example of such an initiative of the level of the European Union is the first phase of National Access Point (NAP) system which will become mandatory by the end of 2019. The NAP system has to be implemented in every member state. It contains the essential information about every national mobility service provider with more detailed information coming in subsequent phases scheduled in December 2020 and 2021 [6].

Currently there are multiple sources of transport related open data. Most common are SIRI and GTFS -APIs that are used in public transport. SIRI is standardized in Europe and it is used in several cities and regions to offer real-time location data on public transport [START_REF]Intelligent transport systems[END_REF]. GTFS is a globally used specification used to publish bus routes and timetables for journey planning [START_REF]GTFS static overview[END_REF]. It was originally created for Google Maps, but now days majority of big public transport operators in Europe provide GTFS formatted timetables. GTFS has also been extended to support real-time fleet updates [START_REF]Google Developers[END_REF].

In this study we present how big data and analytics can produce value to transport organizations, cities and end users. In addition, a data-architecture derived from the case study is presented. The goal of the study was twofold. Firstly, it aimed to find ways in which big data and different interfaces can be used to improve the multimodal and collaborative CPS transport system itself and the passenger satisfaction. Secondly, the study aimed to illustrate the data-architecture of such a system. The study contributes to the big data, transportation 4.0 and CPS transport systems debate where various types of data is collected from several actors participating in the collaborative network. The paper is organized as follows: First, we review the fundamental concepts behind the study, namely: 1) Big data analytics for decision makers, 2) Big data in MaaS and 3) Collaborative networks in passenger transport. In Section 3, we present both the research methodology used and the case of Tampere City region in Finland. Sections 4 and 5 present the system architecture and results through example cases. Section 6 includes a discussion on the significance and contribution of the results.

Big Data and Connected Multimodal Passenger Transport Systems

The expected impacts of big data analytics in passenger transportation are related to both the assistance of decision makers and to the development of new data-driven services for passengers. Big data also serves as a technological enabler for new business models and for a new platform economy.

Big Data Analytics for Decision Makers

Decision makers at different levels of public administration as well as in the wide industry of passenger transportation and travel benefit from convenient tools for strategic planning like feasibility studies, environmental, competitor, and trend analyses. Analyzed big data gives keys to follow and understand consumer and traveler behavior better, but also in some cases to upgrade customer service through optimization. The taxi business provides a good example of this. Several studies show how passengers' travel trajectories and city structure have been mined from taxi ride data [10; 11]. This information may be used in order to understand passenger behavior and in transport planning. In addition to this, the taxi business may also use taxi ride data for optimizing its business [12; 13]. Typical optimization cases in the taxi business include optimizing the location of idle taxis at a given point in time and optimizing the trajectory of a taxi that has a passenger at a given moment. This contributes to the goal of more sustainable cities as the existing resources (taxis and routes) can be exploited in an optimal manner and the need for new resources may be decreased.

In addition to being able to optimize the usage of resources, it is also very important to provide fast reaction to changes in traffic flows. Examples of events that produce changes are mass events, sudden malfunction in some part of the transport system and changes in weather. To be able to react to these events timely, real-time or near realtime information as well as information about the future -such as upcoming events and weather forecasts -are needed. This kind of data is readily available e.g. from devices that are located in vehicles, but also from passengers' devices such as mobile phone location data and smart card data. Chen et al. [START_REF] Chen | The promises of big data and small data for travel behavior (aka human mobility) analysis[END_REF] have performed human mobility pattern mining on mobile phone data and Tao et al. [START_REF] Tao | Exploring Bus Rapid Transit passenger travel behaviour using big data[END_REF] have used smart card data in passenger transport related case studies.

In addition to the above-mentioned proprietary data sources, freely available open data sources and open interfaces for retrieving the data are a very important source of transportation-related data. Open datasets have been used e.g. in data driven customer demand prediction and dynamic pricing [START_REF] Noulas | Mining open datasets for transparency in taxi transport in metropolitan environments[END_REF]. Pereira et al. [START_REF] Pereira | Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios[END_REF] use freely available event data from the Internet to predict public transport rides in the City of Singapore.

Big data analytics supports transport companies as well as public communal, local district and governmental partners and planning associations in their strategic decisions. It also complements the decision process with data for potential assessment, different studies, and analyses. Big Data analytics contributes to itinerary and route planning of transportation companies (e.g. coach/bus and car rental companies), but probably also to tour operators, incoming agencies and travel management departments in private companies and public institutions including fleet management.

Big data analytics tools can inform mobility center how they can include the data in their day-to-day-work for example. Big data analytics allows to improve the customer service of travel intermediaries and travel service suppliers by allowing to react faster e.g. in strikes and/or unpredictable occurrence of traffic disruptions.

In order to fully utilize big data analytics in passenger transportation EU-wide guidelines and recommendations regarding the integration of new data sources as well as a novel data formats are needed. Furthermore, existing privacy and legislation frameworks need to be noted. For example, in Europe, the GDPR (General Data Privacy Regulation) [START_REF]679 of the European parliament and of the council of 27[END_REF] has to be taken into account when dealing with data that may contain privacy issues. Only data that has been properly anonymized is free from regulations concerning privacy. In turn, big data analytics provides an infrastructure and analytical functionality enabling the prediction of mobility and congestion hotspots.

Big Data in Multimodal Transport Services

Multimodal transport services mean that the passenger only purchases one ticket from the point of departure A to the point of arrival B. The journey from A to B typically involves several modalities of transport, such as airplane, ship, train, tram, bus, car sharing, taxi and/or bicycle. The transportation service providers that offer different modalities of transport are an example of a collaborative network. A collaborative network (CN) is a network of largely autonomous, geographically distributed, and heterogeneous organizations that collaborate to better achieve common goals, and whose interactions are supported by a computer network [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF]. The modalities may change dynamically during the journey due to traffic incidents, weather or traffic conditions, among others. An important part of the multimodal transport service is the application that is used to communicate between the collaborative network of service providers and the passenger.

The MaaS business model is an example of an implementation of a multimodal transportation service where the digital applications related to it are an important part of the service. These digital applications are typically are to route planning, payments and communicating timely information from the transport provider to the passenger.

The tools used in big data analytics enhances opportunities also for start-ups and other companies to innovate new mobility services and enable new business models such as new platforms, interfaces and APIs for example. All this is possible given that the MaaS companies adhere to the local privacy regulations. One good solution for sharing mobility data containing personal identifiers between organizations is to anonymize it. It is important to choose the correct level of anonymization so that the data still has value.

Big data analytics could be integrated in existing mobility services like Moovel, Switch and Whim. However, also completely new mobile service possibilities will appear in connection to analyzed data available. Big data analytics also contributes to the emerging MaaS business model that many of the new start-ups have adopted.

Collaborative Networks in Passenger Transport

The collaborative network of multimodal transport service providers presented in the previous section is just one example of the existing and future networks in passenger transport. There are several other examples of collaborative networks in transport. Most of them involve organizations, but some also involve passengers in an active role. Crowdsourcing and sharing economy are typical examples of collaborative networks in transport. The use of open data also calls for collaborative networks because the producers and users of open data seldom are those who build the applications, Crowdsourcing and social transportation offer new possibilities for passengers to organize their journeys [START_REF] Zheng | Big Data for Social Transportation[END_REF]. Crowdsourcing may also be used in cases of traffic disruption where the official travel service providers fail to deliver accurate and ontime information. Crowdsourcing is a sourcing model in which individuals or organizations obtain goods and services, including ideas, from a large, relatively open and often rapidly evolving group of internet users. This type of group can be called collaborative network. Crowdsourcing divides work between participants to achieve a cumulative result. [eg. 21; 22] Sharing economy -also called collaborative economy -is a mode of consumption where goods and services are not owned by a single user, but temporarily accessed by members of a collaborative network with or without charge. Sharing economy can refer to social peer-to-peer processes that include sharing of access to goods and services [START_REF] Hamari | The Sharing Economy: Why People Participate in Collaborative Consumption[END_REF], or any rental transaction facilitated by a two-sided market, sometimes also including business to consumer (B2C). [START_REF] Eckhardt | The Sharing Economy Isn't About Sharing at All[END_REF] 3 Method and Case Description

In this study, we firstly have a review in conceptual part of the paper. Secondly, we picked a case region to support our findings on what kind of open data platforms and APIs there are and what is already being implemented on top of the APIs to make the stakeholders benefit from the data. Since the value from the data for the citizens using the services cannot be presented using a numerical method, we use a set of case examples to illustrate how the solutions built upon the data generate benefits to different stakeholders. Both pilots and known products are considered. The region chosen is the Tampere Region, which is the second largest metropolitan area in Finland with about 385 000 inhabitants. The city center of Tampere is located between two lakes limiting the connection possibilities through the city. Thus, it is important to develop a system that exploits the currently existing physical infrastructure and uses it as efficiently as possible.

On national level, Finland moved towards new legislation about mobility services, which aimed to unify all transport regulations into one code. A large part of the changes in new act was to achieve cost savings in transportation due to information system development and new open data interfaces. One of main targets was to open all the essential data of transportation services to public and to define the provisions of more unified ticket and payment systems, which would allow better combination of different service providers and forms of transportation [START_REF]Hallituksen esitys liikenteen palveluista annetun lain muuttamiseksi[END_REF]. Finland as a country has also been early adopter of new data interfaces, such as Digiroad and Digitraffic [26; 27]. Therefore, many of Finland's largest cities have some similar APIs and sources freely available compared to Tampere. To simplify the results, the paper is chosen to use one case area, in this case, Tampere, which is familiar to the writers and is one of the cities having the most data and APIs available. The city also has a program to create new smart city solutions in cooperation with local research facilities and businesses.

On top the Finnish data, the City of Tampere has also multiple data sources that support the mobility development. Some of the APIs are only in use in the City of Tampere but some also cover the whole region. Examples of different APIs, in addition to already mentioned GTFS-and SIRI-APIs, available consist for example for traffic lights, route planning (for different modes), parking and incidents and roadworks [START_REF]ITS Factory Wiki[END_REF]. A large part of the APIs are available freely, though some special information is available upon agreement. For example open parking API gives basic information, but fares and exact capacities are in closed API [29]. Different sources of both national and regional data can be seen in Figure 2. The Figure also shows examples of amount of regional data provided. 2, most of the data sources are linked together. For example, Digiroad and national maps provide basis to local mapping solutions and Digitransit combines regional GTFS-data to build national data [START_REF] Digitransit | [END_REF]. It should also be noted that these services itself rely on different global data sources, like Digitransit uses OpenStreetMap-data based mapping solution [START_REF] Digitransit | [END_REF].

The City of Tampere is also focused on collaboration in mobility area to create new solutions and pilots. ITS Factory is a network that consist of the city and local authorities, local businesses and research facilities. The main concept of the network is to connect different stakeholders. The city provides different test areas and opens new mobility data sources to allow companies to pilot and develop new services and products. Different research facilities are closely connected in the process as a part of development and pilot phases. The City of Tampere also hosts test sites to pilot for example 5G networking, autonomous cars in everyday traffic and indoor positioning.

Results

Based on the case study of the Tampere region mobility services and on the different information sources in use, the authors propose a novel data architecture for digital services that enable MaaS. This architecture is depicted in Figure 3. As can be observed from the figure 3, there is a collaborative network of organizations and end users that all contribute to creating the big data that is the fuel of the MaaS solutions. In the bottom layer of the picture, we can observe that there is IoT data generated from the vehicles. This may be real-time location data as well as realtime occupation rate data. Special sensors may also measure the availability of wheelchair, children's buggy and luggage slots, for example. The middle layer of the image consists of the big data collected by different organizations of the collaborative network. The leftmost data source contains open data such as the data available through the NAP-interface or SIRI and GTFS data regarding timetables and trajectories. The transport and infrastructure providers create this data and it is reliable and well structured.

The next data source is created or obtained from sensors from the transport service or infrastructure providers. Typical transport infrastructure providers are route keepers and parking facility businesses. In addition to the data collected from the first layer, this layer contains mobility data from the passengers and business data.

The third data source contains data from digital services for passenger transport. In addition to business data, this data contains significant quantities of usage data from passengers. This data includes data such as all saved trajectories, paid trips and performed searches for each user of the service.

The last data source contains crowdsourced data, which is created by passengers and it may sometimes be unreliable and unstructured. It contains data such as ratings of service providers and services as well as real-time information about traffic disruptions and about shared transportation resources such as ride sharing.

Discussion

In our case area Tampere region, multiple different APIs are used to serve as a backbone of providing new services creating value through data; these include for example realtime location of city busses and congestion data of road network through the city's traffic lights. Nowadays, different public transport and routing APIs can be seen presumable to support. It should still be remembered that it is just a start when the data is open and accessible. The key of creating from the data is to make different solutions and applications that the end-user uses and benefits from. In this study, examples on what kind of different benefits are found from APIs and big data in our case region and how they affect users, organizations or the city, are given.

For the user, value can mean many things. It is normal to assume that different apps can show possible routes around city or that public transport timetables can be found digitally, which doesn't make the user consider it as a benefit. Same applies to accident reports and basic traffic flow information that are provided in many areas around the world. In our case area traffic flow can be better estimated through traffic lights that share the traffic volume and wait time information through the API [START_REF]User documentation (ver 1.2)[END_REF]. Same API can also be used to check the status of a specific light (green/amber/red). The status itself doesn't necessarily give any value to the user, but there are different pilots that use the connected traffic lights. For example, mobile app GLOSA aims to read the status of next set of traffic lights and then show the car driver the desired speed to approach so that no stop on red lights would be necessary [START_REF]Liikennevalot kommunikoivat entistä monipuolisemmin kuljettajan kanssa Tampereella[END_REF]. Currently, the pilot runs on 16 of the city's 173 traffic lights [37]. For traffic light junctions with pedestrian and cyclist crossing button, CrossCycle is a mobile app, which tracks the route of the cyclist and reserves the green light in advance, turning it green when the cyclist arrives. The app is supported in 36 light guided junctions around the main cycle paths heading city center. [START_REF]Pyöräilijälle vihreää valoa ja etuisuutta 36 liittymässä mobiilisovelluksella[END_REF] Added value for user can also be seen in arrival guidance to parking areas, which can be implemented in current navigation applications. Since the status and current capacity of the area can be queried beforehand, the user can be automatically routed to a location that will have free spaces [29] and be also given extra information about parking fares and different services, like EV charging.

For different infrastructure users, the value can be defined more easily. For transport providers, the better the information about delays and accidents, the better it is to minimize delays of own fleet and to calculate new routes when needed. For example, the public transport operator can get an priority in the connected traffic lights through the public transport information systems, which can be used to improve the punctuality and shorten the journey times [START_REF]Tampereen seudun joukkoliikenteen tietojärjestelmiä uudistetaan[END_REF]. In a study conducted in Helsinki, it was noted that there is no common results of the effects of these benefits. They ran a pilot by removing priority from public transport for a day. As a result, the punctuality dropped on average by 3%, but the results were largely route dependent. It was also noted that the main effect of these benefits is to improve the punctuality and not to shorten the actual journey time. [40] In Tampere, 118 traffic light junctions support the public transport priority, which covers practically the areas where public transport is widely used [START_REF]Traffic light controlled intersections in Tampere. From data.tampere[END_REF]. Through an own system, also emergency vehicles can be given priority on junctions to make the traffic flow in the desired direction [START_REF]The Association of Finnish Local and Regional Authorities[END_REF].

The city can also benefit from the different services build on top of data. Different routing solutions that take the congestion into account can separate the traffic flow into different paths, which generates virtually more capacity, since infrastructure is better used. Different applications that create value to the user can also collect information to the city, which gets information about the routes and choices that users normally take.

The different solutions build on mobility data and APIs can also help to reach different sustainability targets. Different routing applications, which all rely on public APIs to gather information, can be used to promote new methods of transport and make them more attractive to the user by for example showing different recommendations for walking (interesting and walkable) and cycling (avoids large ascents) directions. This can promote modal shift towards sustainable modes of transport. Big data and analytics give possibility to route passengers and transport more efficiently and thus lower congestion and emission in cities. Better traffic management will allow greater capacity for passengers and goods allowing present infrastructure to be used more efficiently and therefore resources to be used more effectively.

Conclusion

Big data analytics offers new opportunities for both decision makers and new businesses as well as for passengers. Many of the new opportunities contribute also to sustainable development -from an environmental or from a social perspective. Open data and APIs have a key role since they allow to seamlessly connect different services, and in this case transport modes, into one easy-to-use solution for the passenger, who does not have to be aware on what is happening in the background. The system would also work in case of different incidents and congestion situations, since all this data would be available for the CPS transport platform that handles the journeys of users.

The study showed that even though the exploitation of big data does not directly improve the state of the physical transport infrastructure itself, it helps in utilizing more of its capacity. In the Tampere region case analyzed in this study, this means for example that the existing road network can be used more efficiently as transport service providers and individual drivers can plan their itineraries to avoid traffic congestions due to mobility data available from the road network. Another result of the study was that the use of big data makes the CPS transport system more attractive to passengers. An example of this is that displaying the real-time locations of the fleet to the passengers typically does improve passenger satisfaction with the system. This applies especially in cases where there is delay in the public transportation timetables.

In the future, new sensors and IoT could allow entirely new ways to collect and provide data to users. For example, IoT-sensors would allow to track the current occupancy rate of different public transport services and all the data could be integrated into new services. Data from different transport modalities is already available. However, the collaborative network is the core for the functioning CPS transport system. Via collaboration such as sharing and sourcing data and services within the network, the connected multimodal passenger transport systems are completed.

Interesting further research questions could relate to new business model possibilities in collaborative networks and transport systems. Another line of future research could relate to the usability of the CPS transport system by different passenger groups, since sustainable mobility is not just about improving transport infrastructure and services, but also about overcoming socio, economic, political and physical challenges. It would be important to study which passenger segments have a risk of being excluded from new digital mobility services. The CPS transport system requires different skills and attitudes from the passengers than a very traditional transport system that only has a physical dimension. Even though big data, open APIs and collaborative networks do provide significant improvements to the CPS transport system, the most important factor remains the usability of the system. Even the most intelligent system provides only very little value if it is not being used by the passengers.

Fig. 2 .

 2 Fig. 2. Examples of different data sources -Case Tampere Region [29; 30; 31; 32; 33]National data sources include for example different official platforms, like Digiroad, Digitransit and Digitraffic. Even though shown separated in Figure2, most of the data sources are linked together. For example, Digiroad and national maps provide basis to local mapping solutions and Digitransit combines regional GTFS-data to build national data[START_REF] Digitransit | [END_REF]. It should also be noted that these services itself rely on different global data sources, like Digitransit uses OpenStreetMap-data based mapping solution[START_REF] Digitransit | [END_REF].The City of Tampere is also focused on collaboration in mobility area to create new solutions and pilots. ITS Factory is a network that consist of the city and local authorities, local businesses and research facilities. The main concept of the network is to connect different stakeholders. The city provides different test areas and opens new mobility data sources to allow companies to pilot and develop new services and products. Different research facilities are closely connected in the process as a part of development and pilot phases. The City of Tampere also hosts test sites to pilot for example 5G networking, autonomous cars in everyday traffic and indoor positioning.

Fig. 3 .

 3 Fig.3. The data architecture for a MaaS service. The journey of the passenger starts with a bus and ends with a bicycle ride. The bottom layer illustrates the physical dimension of the system. It could consist of any combination and number of transport modes operated by different service providers as well as of physical infrastructure that is connected to the network. In this figure, the traffic lights are connected to the data repository as well as to the digital services.