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  • 1.
    Dudarenko, Natalia
    et al.
    Luleå University of Technology.
    Rana, Juwel
    Luleå University of Technology.
    Synnes, Kåre
    Luleå University of Technology.
    Ranking algorithm by contacts priority for social communication systems2010Inngår i: Smart Spaces and Next Generation Wired/Wireless Networking: Third Conference on Smart Spaces, ruSMART 2010, and 10th International Conference, NEW2AN 2010, St. Petersburg, Russia, August 23-25, 2010 / [ed] Sergey Balandin, Roman Dunaytsev, Yevgeni Koucheryavy, Springer, 2010, s. 38-49Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The paper presents the ranking algorithm by contacts with priority application for Social Communication Services. This algorithm makes it Possible to rank contacts in Different Social Communication Services by Their priority for the user, and find a preferable communication tools for everytime contact into the social graph. It Is Proposed to Determine Priorities Of The contacts Using communication history. The ranking algorithm is based on the Markov Chain Theory and Social Strength Calculation Approach. The paper exploits the Opportunities for measuring social strength for the contacts and also "prioritizes communication tools for Simplifying communication. The results are Lindsey village an example.

  • 2.
    Idowu, Samuel
    et al.
    Luleå University of Technology.
    Hagos, Desta Haileselassie
    Luleå University of Technology.
    Tesfay, Welderufael Berhane
    Lulea University of Technology.
    Famurewa, Abiola
    Luleå University of Technology.
    Rana, Juwel
    Lulea University of Technology.
    Synnes, Kåre
    Luleå University of Technology.
    NexTrend: Context-aware music-relay corridors using NFC tags2013Inngår i: 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Piscataway, NJ: IEEE, 2013, s. 573-578Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The rise of pervasive computing presents unique opportunities due to increasing availability of smart devices such as mobile phones and tablets equipped with various sensors enabling Near Field Communication (NFC) technologies. The growth of mobile computing has led to an increase in access to digital music. With the growth of digital music, the development of music information sharing services for users becomes important. The existing sharing methods are based on the users’ social network and preferences in music. However, sometimes, sharing music according to location and time is needed.This paper presents work on smart spaces equipped with NFC tags, deployed at different locations in hallways for discovering and sharing new music experiences. This concept provides a new way of interaction between passers-by for discovering music in relation to location. For example, the hallway locations use sensing devices to provide an automatic means of exchanging music information among the passers-by.We utilized NFC tags as Music-Relay hot spots. The hot spot retrieves information about the music a user is playing on her/his device while s/he is passing by the hot spot. The work contributes to a pervasive service that equips an environment with music context intelligence about a passer-bys choice of music and allows users to feel the musical presence of other users who have been in the same location at previous point in time. In general, this paper proposes a new music information sharing service using the music information captured from users at a specific location in time.

  • 3.
    Morshed, Sarwar J.
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Rana, Juwel
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Telenor Group, Norway.
    Milrad, Marcelo
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Active and Satisfied Users as a Key to Measure the Success of a Digital Mobile Service2018Inngår i: Proceedings 6th Artificial Intelligence for Knowledge Management (AI4KM), Stockholm, Sweden / [ed] Eunika Mercier-Laurent, Mieczysław L. Owoc, Nada Matta, Oliver Obst, 2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In order to develop, deploy and sustain a digital mobile service attractive to the users, one of the key parameters is to understand and to identify the satisfied users of such service and to maintain a user satisfaction-centric knowledge management. The classical way of achieving this is to collect users´ direct or indirect feedback and to measure their level of satisfaction. Digital mobile services have a global focus and address global audiences - which means getting users´ feedback and finding users´ satisfaction by performing studies using questionnaire for millions of users. Such an approach cannot be the most efficient way for doing so. This paper proposes an alternative approach of using data science knowledge and techniques, which performs a prediction on the usage data to distinguish between satisfactory and unsatisfactory users at a different level of granularity. First of all, this model generates some common predictors from users' access log data through descriptive analysis. Later, these predictive variables are used to predict the level of user satisfaction using Machine Learning Algorithms. Using this user-centric knowledge management model, digital service providers could measure whether their offered services would reach success by predicting the number of satisfied users.

  • 4.
    Morshed, Sarwar J.
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME). Daffodil International University, Bangladesh.
    Rana, Juwel
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME). Telenor Grp, Norway.
    Milrad, Marcelo
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME).
    Open Source Initiatives and Frameworks Addressing Distributed Real-time Data Analytics2016Inngår i: 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 2016, s. 1481-1484Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The continuous evolution of digital services, is resulting in the generation of extremely large data sets that are created in almost real time. Exploring new opportunities for improving the quality of these digital services, as well as providing better-personalized experiences to digital users are two major challenges to be addressed. Different methods, tools, and techniques existed today to generate actionable insights from digital services data. Traditionally, big data problems are handled on historical data-sets. However, there is a growing demand on real-time data analytics to offer new services to users and to provide pro-active customers' care, personalized ads, emergency aids, just to give a few examples. Spite of the fact that there are few existing frameworks for real-time analytics, however, utilizing those for solving distributed real-time big data analytical problems stills remains a challenge. Existing real-time data analytics (RTDA) frameworks are not covering all the features that requires for distributed computation in real-time. Therefore, in this paper, we present a qualitative overview and analysis on some of the mostly used existing RTDA frameworks. Specifically, Apache Spark, Apache Flink, Apache Storm, and Apache Samza are covered and discussed in this paper.

  • 5.
    Morshed, Sarwar J.
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME).
    Rana, Juwel
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME). Telenor Grp, Oslo, Norway..
    Milrad, Marcelo
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för medieteknik (ME).
    Real-Time Data Analytics: An Algorithmic Perspective2016Inngår i: DATA MINING AND BIG DATA, DMBD 2016, Springer, 2016, s. 311-320Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Massive amount of data sets are continuously generated from a wide variety of digital services and infrastructures. Examples of those are machine/system logs, retail transaction logs, traffic tracing data and diverse social data coming from different social networks and mobile interactions. Currently, the New York stock exchange produces 1 TB data per day, Google processes 700 PB of data per month and Facebook hosts 10 billion photos taking 1 PB of storage just to mention some cases. Turning these streaming data flow into actionable real-time insights is not a trivial task. The usage of data in real-time can change different aspects of the business logic of any corporation including real time decision making, resource optimization, and so on. In this paper, we present an analysis of different aspects related to real-time data analytics from an algorithmic perspective. Thus, one of the goals of this paper is to identify some new problems in this domain and to gain new insights in order to share the outcomes of our efforts and these challenges with the research community working on real-time data analytics algorithms.

  • 6.
    Noori, Sheak Rashed Haider
    et al.
    Daffodil International University, Bangladesh.
    Hossain, Md. Kamrul
    Daffodil International University, Bangladesh.
    Rana, Juwel
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för medieteknik (ME). Telenor Grp, Oslo, Norway.
    Key Indicators for Data Sharing - In Relation with Digital Services2016Inngår i: DATA MINING AND BIG DATA, DMBD 2016, Springer, 2016, s. 353-363Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Rapid growth of data intensive digital services are creating potential risks of violating consumer centric data privacy. Protection of data privacy is becoming one of the key challenges for most of the big data business entities. Due to thank of big data, recommendation and personalization are becoming very popular in digital space. However it is hard to find a well-defined boundary which illustrates privacy threat to consumers' in relation with improving already opted-in communication services. In this paper, we initiated identifying key indicators for consumer configured privacy policy in relation with personalized services taking into consideration that "Privacy is a tool for balancing personalization". We survey user attitudes towards privacy and personalization and discovered key indicators for configuring privacy policy by analyzing survey data about privacy concern and data sharing attitude of the consumers. We found that consumers did not want to stop using social media based communication services due to privacy risks. Moreover, consumers have attitude of sharing their data, provided that appropriate personalization features are in place.

  • 7.
    Rana, Juwel
    et al.
    Telenor Research, Norway.
    Bjelland, Johannes
    Telenor Group, Norway.
    Couronne, Thomas
    Telenor Research, Norway.
    Sundsoy, Pål
    Telenor Research, Norway.
    Wagner, Daniel
    University of Cambridge, UK.
    Rice, Andrew
    University of Cambridge, UK.
    A Handset-centric View of Smartphone Application Use2014Inngår i: 9TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC'14) / THE 11TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC'14) / AFFILIATED WORKSHOPS, Amsterdam: Elsevier, 2014, Vol. 34, s. 368-375Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Studying the use of applications on smart phones is important for developers, handset designers and network operators. We conducted a study on Android devices by installing an instrumentation application, Device Analyzer, on participants’ handsets. Over a 4 month period we collected 10.9 billion records from 674 different users. In this paper we describe how to use the research study features of Device Analyzer to control participant selection and to access information (with consent) that is withheld for privacy reasons from the main dataset. We describe our data processing architecture and the steps required to preformat and analyse the data. Our data contains 3329 distinct applications (from the Google Play store) but despite this, on average, a user makes use of only 8 unique applications in a week. Almost 100% of our users make use of some email application on their phone. Fewer users (85%) made use of the Facebook application but 4–5 times more frequently than for email with sessions lasting almost twice as long. We also investigated whether different applications have correlated usage using a network analysis and a principal component analysis. We see that application usage tends to correlate by vendor more than by activity. This is potentially due to vendors integrating or cross-promoting services between applications.

  • 8.
    Rana, Juwel
    et al.
    Telenor Research, Norway.
    Bjelland, Johannes
    Telenor Research, Norway.
    Sundsoy, Pål
    Telenor Research, Norway.
    Couronne, Thomas
    Telenor Research, Norway.
    Qureshi, Taimur
    Telenor Research, Norway.
    Canright, Geoffey
    Telenor Research, Norway.
    Smartphone applications co-usage: Could we predict your next app?2015Inngår i: Network Science x2015, 2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Smartphone applications are becoming part of our everyday life. Cost of smartphone devices is dropping and development of smartphone applications is getting simpler. Moreover, these applications are spanning from entertainment to education, health, productivity, finance, payment, transportation - and much more. Based on a study of handset usage analytics we find that users are spending more than 88 minutes with direct interaction of their devices’ screen, and initiates at least 38 Apps on a daily basis. Until September 2014, Apple Store has 1.3 million of active Apps and a cumulated download number of 75 billion apps. Google Play also shows similar volumes. In summary, these numbers lead us towards the era where our daily activities would be Apps centric, and our productivity would be driven by an appropriate selection of Apps. 

  • 9.
    Rana, Juwel
    et al.
    Luleå University of Technology.
    Hallberg, Josef
    Luleå University of Technology.
    Synnes, Kåre
    Luleå University of Technology.
    Kristiansson, Johan
    Ericsson Research.
    Harnessing the cloud for mobile social networking applications2010Inngår i: International Journal of Grid and High Performance Computing (IJGHPC), ISSN 1938-0259, Vol. 2, nr 2, s. 1-11Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The cloud computing model inherently enables information from social networking services (Twitter, Facebook, LinkedIn, and so forth), context-based systems (location, activity, interests, etc.) and personal applications (call logs, contacts, email, calendar, and so forth) to be harnessed for multiple purposes. This article presents an agent-based system architecture for semantic and semi-automated applications that utilize the cloud to enrich and simplify communication services, for instance by displaying presence information, prioritizing information, and dynamically managing groups of users. The proposed architecture is based on the concept of aggregated social graphs, which are created from harnessed information about how people communicate. This article also presents challenges in achieving the envisioned architecture and introduces early prototyping results.

  • 10.
    Rana, Juwel
    et al.
    Telenor Group, Norway.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    Data Matters: Reflection on User Defined Social Prioritization2014Inngår i: ASE@360 Open Scientific Digital Library, ASE@360 , 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Online social networking becomes an integral part of our everyday life and thus, social computing is get- ting huge attention in these years. One of the areas of social computing is to understand humans' social or tie strength by observing measurable social interactions. Thanks to today's communication and social media services that open tremendous opportunities for communicating through electronic media, such as through mobile phone calls, SMS, emails, or social media tools. That has made it possible to automatically measure and predict human's social strength. The social strength is defined as a metric that represents the tie strength of the relation between persons, calculated based on the frequency, duration, context and media type of the electronic communication be- tween the persons. For example, a family relation is generally considered to be stronger than a relation between coworkers in our society, but the strength of the relation is intrinsic and have been cumbersome to measure. This paper thus presents reflection of user- defined ranking of social prioritization in comparison with machine defined social strength. The study found that there is significant difference in results between the algorithmic and the user-defined strength ranking, which indicates the inability of the algorithms to capture intrinsic knowledge (such as the importance of family bonds and non-electronic interaction). This would mean that the participants' ranking was colored by their interaction in real-life. This study also found several implications, in which diverse source and volume of interaction data are considered as key performance issues for algorithmic strength ranking.

  • 11.
    Rana, Juwel
    et al.
    Lulea University of Technology.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    Dynamic media distribution in ad-hoc social networks2012Inngår i: 2012 Second International Conference on Cloud and Green Computing (CGC), IEEE, 2012, s. 549-556Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a social distribution mechanism for finding, connecting and propagating contents in social networks using context and communication history of users together with meta information about the content. The proposed mechanism operates by propagating invitation messages to a prioritized group of users to build up an ad-hoc social network from the original social network while at the same time preventing spamming the users with unwanted invitation messages. The proposed mechanism makes it possible to implement and deploy a wide variety of services targeting the specific needs of a user. As a proof-of-concept, the paper shows how the proposed social distribution mechanism can be used to invite users to a shared space, which can contain various kinds of collaboration tools allowing a group of users to communicate and solve problems together. In order to investigate the efficiency of the proposed mechanism, the paper also presents result from a simulation study. The result shows that the social distribution mechanism should consider both social strength and context for propagating social media contents as some of the recipients may have equal interest of the social media but different level of trust about the originator.

  • 12.
    Rana, Juwel
    et al.
    Luleå University of Technology.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    Enriching and simplifying communication by social prioritization2010Inngår i: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010) : Odense, Denmark, 9 - 11 August 2010, Piscataway, NJ: IEEE Press, 2010, s. 336-340Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a framework for developing applications that harness information available on social networking sites and telecom platforms to make communication simpler and richer. The framework builds an aggregated social graph by first aggregating user's contact information from multiple communication environments and then calculating the social strength between the users, based on interaction pattern and process mining techniques. The aggregated social graph describes users communication patterns which can be used to simplify and enhance communication services by inviting users for collaboration, selecting suitable tools for communication and prioritizing information flows. The framework also enables new types of applications such as social search clients, smart dialers or contact applications. In addition, the paper also presents an prototype implementation together with an evaluation comparing methods to compute social strengths based on on-line interaction datasets

  • 13.
    Rana, Juwel
    et al.
    Luleå University of Technology.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    Modeling unified interaction for communication service integration2014Inngår i: The Fourth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies : UBICOMM 2010 / [ed] Jaime Lloret Mauri, Red Hook, NY: Curran Associates, Inc., 2014, s. 373-378Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Social network inspired communication services has made a huge success, allowing users to communicate and share information in new fashion. At the same time, telecom operator's services are becoming more open, which makes it possible to develop improved social networking services and integrate them with mobile platforms. One problem that needs to be addressed when developing such services how to fetch useful social information and make it available for the services running in the cloud or in the client devices. This paper presents a generalized on-line interaction model that collects useful information from well known social networking services, and transforms the information into unified interaction patterns, which can be utilized for social data propagation or for discovering communication patterns. Ultimately, this allows the applications to incorporate social data for enabling smarter functions. For example, the data model can be useful for presenting information about callers or adding news feeds to the classical address book, prioritizing information of the contacts, inviting user for forming micro-communities. The paper also discusses the identity problem in the social media and identifies major challenges to solve that problem

  • 14.
    Rana, Juwel
    et al.
    Luleå University od Technology.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    Supporting ubiquitous interaction in dynamic shared spaces through automatic group formation based on social context2012Inngår i: International Conference on Social Informatics (Social Informatics), Lausanne: IEEE, 2012, s. 121-130Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper investigates how the management of groups that communicate electronically, such as group formation, can be simplified based on users' context and social relations. This work builds on a framework for Aggregated Social Graphs, where each node represents the relational strength to other users. The strength of a relation is calculated by utilizing information on how we communicate using mobile phone calls, emails, and social networks in combination with additional sources of information such as from calendars. A contextual group management schema is presented where contextual parameters such as tags, locations and objects are used to prune an aggregated social graph in order to automatically form a group. The schema is implemented in a runtime environment based on the Distributed Shared Memory service available at Ericsson Labs. The feasibility of the proposed schema is then studied through a prototype implementation both in a web-browser and as a mobile app. The study shows that a group can be formed automatically and that a lightweight communication session then can be initiated for that group.

  • 15.
    Rana, Juwel
    et al.
    Telenor Group, Norway.
    Kristiansson, Johan
    Ericsson Research.
    Synnes, Kåre
    Luleå University of Technology.
    The strength of social strength: an evaluation study of algorithmic versus user-defined ranking2014Inngår i: SAC '14 Proceedings of the 29th Annual ACM Symposium on Applied Computing, ACM Press, 2014, s. 658-659Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A family relation is generally considered to be stronger than a relation between coworkers in our society, but the strength of the relation is intrinsic and have been cumbersome to measure. The fact that we increasingly communicate electronically, such as through email, mobile phone calls or social media, has made it possible to automatically measure and analyze the relation between persons. This paper presents an evaluation study of social strength, where the social strength is defined as a metric that represents the tie strength of the relation between persons, calculated based on the frequency, duration, context and media type of the electronic communication between the persons.

    The study found that the Utility Function performs better because it emphasize the communication frequency between persons. There is however a significant difference in results between the algorithms and the user-defined ranking. This indicates the inability of the algorithms to capture intrinsic knowledge (such as the importance of family bonds and non-electronic interaction). This would mean that the participants' ranking was colored by their interaction in real-life. It is however evident from the study that the functions provide more accurate results when they utilize multiple sources of communication history over only a single source.

    Finally, capturing sufficient communication data from multiple data sources is very hard, as access to such data is restricted because of concerns regarding for instance business and privacy. A conclusion is that the algorithms requires a larger data set, preferably being captured continuously over a period longer than 2 weeks, to achieve a better accuracy that is closer to the ground truth. However, the study shows the feasibility of capturing social strength automatically and we believe that the results is an important step towards systems that reason about the relation between persons in order to make communications services more pervasive.

  • 16.
    Rana, Juwel
    et al.
    Luleå University of Technology.
    Morshed, Sarwar
    Luleå University of Technology.
    Synnes, Kåre
    Luleå University of Technology.
    End-user creation of social apps by utilizing web-based social components and visual app composition2013Inngår i: Proceedings of the 22nd international conference on World Wide Web companion, New York: ACM Press, 2013, s. 1205-1214Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a social component framework for the SatinII App Development Environment. The environment provides a systematic way of designing, developing and deploying personalized apps and enables end-users to develop their own apps without requiring prior knowledge of programming. A wide range of social components based on the framework have been deployed in the SatinII Editor, including components that utilize aggregated social graphs to automatically create groups or recommending/filtering information. The resulting social apps are web-based and target primarily mobile clients such as smartphones. The paper also presents a classification of social components and provides an initial user-evaluation with a small group of users. Initial results indicate that social apps can be built and deployed by end-users within 17 minutes on average after 20 to 30 minutes of being introduced to the SatinII Editor.

  • 17.
    Sotsenko, Alisa
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för medieteknik (ME).
    Jansen, Marc
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för medieteknik (ME).
    Milrad, Marcelo
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för medieteknik (ME).
    Rana, Juwel
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för medieteknik (ME). Telenor Grp, Norway.
    Using a Rich Context Model for Real-Time Big Data Analytics in Twitter2016Inngår i: 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW), IEEE, 2016, s. 228-233Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.

  • 18.
    Synnes, Kåre
    et al.
    Luleå University of Technology.
    Kranz, Mattias
    University of Passau, Germany.
    Rana, Juwel
    Luleå University of Technology.
    Schelén, Olov
    Luleå University of Technology.
    User-Centric Social Interaction for Digital Cities2013Inngår i: Creating Personal, Social, and Urban Awareness through Pervasive Computing / [ed] Bin Guo, Daniele Riboni and Peizhao Hu, IGI Global, 2013, s. 318-346Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Pervasive Computing was envisioned by pioneers like Mark Weiser, but has yet to become an everyday technology in our society. The recent advances regarding Internet of Things, social computing and mobile access technologies however converge to make pervasive computing truly ubiquitous. The key challenge is however to make simple and robust solutions for normal users, which shifts the focus from complex platforms involving machine learning and artificial intelligence to more hands on construction of services that are tailored or personalized for individual users.This chapter therefore discusses Internet of Things together with Social Computing as a basis for components that users in a ’digital city’ could utilize to make their daily life better, safer, etc. A novel environment for user-created services, such as social apps, is presented as a possible solution for this. The vision is that anyone could make simple service based on Internet-enabled devices (Internet of Things) and encapsulated digital resources such as Open Data, which also can have social aspects embedded.This chapter also aims to identify trends, challenges and recommendations in regard of Social Interaction for Digital Cities. This work will help expose future themes with high innovation and business potential based on a timeframe roughly 15 years ahead of now. The purpose is to create a common outlook on the future of information and communication technologies (ICT) based on the extrapolation of current trends and ongoing research efforts.

  • 19.
    Synnes, Kåre
    et al.
    Luleå Tekniska Universitet.
    Rana, Juwel
    Luleå Tekniska Universitet.
    Technical Foresight Report: Social Distribution Mechanisms2012Rapport (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    This report aims to identify trends, challenges and recommendations in regard of Social Distribution Mechanisms. This foresight will help expose future themes with high innovation and business potential based on a timeframe roughly 15 years ahead, or 2030! The purpose is to create a common outlook on the future of ICT and to establish a strong community across nodes and partner organizations.

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