Design implications for mobile user interfaces of Internet services

Internet services are becoming essential in people's daily lives. In addition to accessing them on a PC, Internet services offer functionality and content that are also relevant for mobile use. At the same time, mobile devices of today are technologically sophisticated enabling online access anytime, anywhere. The remaining challenge is to utilize the capabilities of a mobile device in a way that offers people a positive user experience when they are using Internet services on the go. This Thesis belongs to the area of Human-Computer Interaction focusing on the use of Internet services on a mobile device. It considers the limitations of a mobile device in terms of user interface design and its goal is to define design implications that assist in designing mobile user interfaces for Internet services. The design implications mainly aim to give guidance on how to design a mobile Web browser, but they are completed with research findings on designing a mobile client application for an Internet service. The research was implemented through user needs studies, user interface design, and user evaluations. The research studies focused on two approaches that support the use of Internet services on mobile devices: the Minimap Web browser and the Image Exchange mobile client application presented these two approaches. The resulting design implications suggest that the following aspects should be considered when designing mobile user interfaces for Internet services: content optimization, utilization of desktop and mobile usage patterns, full exploitation of device capabilities, compensation for device resources, and content updating. The possible differences in characteristics of a mobile Web browser and a mobile client application are also examined. Finally, this Thesis discusses the latest developments that enable alternative ways to support Internet services on mobile devices in the future

AndroMedia : Towards a Context-aware Mobile Music Recommender

Portable music players have made it possible to listen to a personal collection of music in almost every situation, and they are often used during some activity to provide a stimulating audio environment. Studies have demonstrated the effects of music on the human body and mind, indicating that selecting music according to situation can, besides making the situation more enjoyable, also make humans perform better. For example, music can boost performance during physical exercises, alleviate stress and positively affect learning. We believe that people intuitively select different types of music for different situations. Based on this hypothesis, we propose a portable music player, AndroMedia, designed to provide personalised music recommendations using the user's current context and listening habits together with other user's situational listening patterns. We have developed a prototype that consists of a central server and a PDA client. The client uses Bluetooth sensors to acquire context information and logs user interaction to infer implicit user feedback. The user interface also allows the user to give explicit feedback. Large user interface elements facilitate touch-based usage in busy environments. The prototype provides the necessary framework for using the collected information together with other user's listening history in a context- enhanced collaborative filtering algorithm to generate context-sensitive recommendations. The current implementation is limited to using traditional collaborative filtering algorithms. We outline the techniques required to create context-aware recommendations and present a survey on mobile context-aware music recommenders found in literature. As opposed to the explored systems, AndroMedia utilises other users' listening habits when suggesting tunes, and does not require any laborious set up processes

Adaptive user interfaces for mobile map-based visualisation

Mobile devices today frequently serve as platforms for the visualisation of map-based data. Despite the obvious advantages, mobile map-based visualisation (MMV) systems are often difficult to design and use. Limited screen space, resource constraints and awkward interaction mechanisms are among the many problems with which designers and users have to contend. Adaptive user interfaces (AUIs), which adapt to the individual user, represent a possible means of addressing the problems of MMV. Adaptive MMV systems are, however, generally designed in an ad-hoc fashion, making the benefits achieved difficult to replicate. In addition, existing models for adaptive MMV systems are either conceptual in nature or only address a subset of the possible input variables and adaptation effects. The primary objective of this research was to develop and evaluate an adaptive MMV system using a model-based approach. The Proteus Model was proposed to support the design of MMV systems which adapt in terms of information, visualisation and user interface in response to the user‟s behaviour, tasks and context. The Proteus Model describes the architectural, interface, data and algorithm design of an adaptive MMV system. A prototype adaptive MMV system, called MediaMaps, was designed and implemented based on the Proteus Model. MediaMaps allows users to capture, location-tag, organise and visualise multimedia on their mobile phones. Information adaptation is performed through the use of an algorithm to assist users in sorting media items into collections based on time and location. Visualisation adaptation is performed by adapting various parameters of the map-based visualisations according to user preferences. Interface adaptation is performed through the use of adaptive lists. An international field study of MediaMaps was conducted in which participants were required to use MediaMaps on their personal mobile phones for a period of three weeks. The results of the field study showed that high levels of accuracy were achieved by both the information and interface adaptations. High levels of user satisfaction were reported, with participants rating all three forms of adaptation as highly useful. The successful implementation of MediaMaps provides practical evidence that the model-based design of adaptive MMV systems is feasible. The positive results of the field study clearly show that the adaptations implemented were highly accurate and that participants found these adaptations to be useful, usable and easy to understand. This research thus provides empirical evidence that the use of AUIs can provide significant benefits for the visualisation of map-based information on mobile devices

Usability of mobile applications: literature review and rationale for a new usability model

The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in amobile context. This increase in usefulness has come at the expense of the usability of these devices in somecontexts. We conducted a small review of mobile usability models and found that usability is usually measured interms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend tobe overlooked in the usability models that are most prominent despite their likely impact on the success or failureof an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile ApplicationDevelopment) usability model which was designed to address the limitations of existing usability models whenapplied to mobile devices. PACMAD brings together significant attributes from different usability models inorder to create a more comprehensive model. None of the attributes that it includes are new, but the existingprominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation.We performed a literature search to compile a collection of studies that evaluate mobile applications and thenevaluated the studies using our model

Yhteisöllinen energiatehokkuus mobiililaitteilla

We have created a mobile energy measurement application and gathered energy measurement data from over 725,000 devices, running over 300,000 applications, in heterogeneous environments, and constructed models of what is normal in each context for each application. We have used this data to find energy abnormalities in the wild, and provide users of our application advice on how to deal with them. These abnormalities cannot be discovered in laboratory conditions due to the rich interaction of the smartphone and its operating environment. Employing a collaborative mobile energy awareness application with thousands of users allows us to gather a large amount of data in a short time. Such a large and diverse dataset has helped us answer many research questions. Our work is the first collaborative approach in the area of mobile energy debugging. Information received from each device running our application improves the advice given to other users running the same applications. The author has developed a context data gathering hub for smartphones, discovered the need for a common API that unifies network connectivity, energy awareness, and user experience, and investigated the impact of mobile collaborative energy awareness applications, to find previously unknown energy bugs on smartphones, and to improve users' knowledge of smartphone energy behavior.Viime vuosien aikana älypuhelinten laitteistot ovat kehittyneet entistä tehokkaammiksi, mutta akkuteknologia ei ole kehittynyt yhtä nopeasti. Tämä on synnyttänyt tarpeen tehostaa sekä laitteiston että ohjelmiston energiatehokkuutta. Älypuhelimen energiatehokkuuden optimointi on haastavaa, koska toimintaympäristö on moninainen ja käsittää paitsi laitteiston ja sen asetukset, niin myös sovellukset, jotka käyttävät laitteiston toimintoja. Tässä väitöstyössä on keskitytty mobiililaitteiden energiaongelmien ja poikkeamien löytämiseen ja niiden korjaamiseen. Väitöskirja käsittelee yhteisöllisen metodin käyttöä energiankulutukseen liittyvien epätehokkuuksien löytämisessä ja korjaamisessa mobiililaitteilla. Tätä metodia on ensimmäistä kertaa sovellettu mobiililaitteille väitöstyöhön liittyvässä Carat-projektissa. Projektissa on luotu energianmittaussovellus mobiililaitteille ja kerätty energiamittauksia yli 725 000 laitteelta ja 300 000 sovelluksesta monipuolisissa ympäristöissä. Näiden pohjalta on tehty malleja sovellusten normaalista energiankulutuksesta eri konteksteissa. Tietojen ja mallien avulla on löydetty energiapoikkeavuuksia tavallisessa käytössä olevilta laitteilta ja annettu sovelluksen käyttäjille neuvoja poikkeavuuksien korjaamiseen. Väitöstyön aikana kerätty suurikokoinen ja monipuolinen aineisto on auttanut vastaamaan moniin kysymyksiin koskien älypuhelinten energiankulutusta arkikäytössä. Kaikkia poikkeavuuksia ei voida löytää laboratorio-olosuhteissa, sillä mobiililaitteen ympäristö vaikuttaa vahvasti sen toimintaan. Esitetty menetelmä on ensimmäinen, joka soveltaa yhteisöllistä lähestymistapaa mobiililaitteiden energiaongelmien löytämiseen. Kirjoittaja on kehittänyt kontekstitietojen keräysratkaisun älypuhelimille. Hän on huomannut tarpeen järjestelmälle, joka yhdistää mobiililaitteen tilanteen, käytön, energiatehokkuuden ja käyttäjäkokemuksen. Työssä on kehitetty uusi menetelmä energiapoikkeamien analyysiin yhteisöllisesti kerättyjen mittausten perusteella sekä tutkittu energiatehokkuussovellusten vaikutusta eri mobiililaitteilla. Näiden avulla on löydetty ennen tuntemattomia energiaongelmia älypuhelimista ja parannettu käyttäjien ymmärrystä älypuhelinten energiakäyttäytymisestä

Crowdsensed Mobile Data Analytics

Mobile devices, especially smartphones, are nowadays an essential part of everyday life. They are used worldwide and across all the demographic groups - they can be utilized for multiple functionalities, including but not limited to communications, game playing, social interactions, maps and navigation, leisure, work, and education. With a large on-device sensor base, mobile devices provide a rich source of data. Understanding how these devices are used help us also to increase the knowledge of people's everyday habits, needs, and rituals. Data collection and analysis can thus be utilized in different recommendation and feedback systems that further increase usage experience of the smart devices. Crowdsensed computing describes a paradigm where multiple autonomous devices are used together to collect large-scale data. In the case of smartphones, this kind of data can include running and installed applications, different system settings, such as network connection and screen brightness, and various subsystem variables, such as CPU and memory usage. In addition to the autonomous data collection, user questionnaires can be used to provide a wider view to the user community. To understand smartphone usage as a whole, different procedures are needed for cleaning missing and misleading values and preprocessing information from various sets of variables. Analyzing large-scale data sets - rising in size to terabytes - requires understanding of different Big Data management tools, distributed computing environments, and efficient algorithms to perform suitable data analysis and machine learning tasks. Together, these procedures and methodologies aim to provide actionable feedback, such as recommendations and visualizations, for the benefit of smartphone users, researchers, and application development. This thesis provides an approach to a large-scale crowdsensed mobile analytics. First, this thesis describes procedures for cleaning and preprocessing mobile data collected from real-life conditions, such as current system settings and running applications. It shows how interdependencies between different data items are important to consider when analyzing the smartphone system state as a whole. Second, this thesis provides suitable distributed machine learning and statistical analysis methods for analyzing large-scale mobile data. The algorithms, such as the decision tree-based classification and recommendation system, and information analysis methods presented in this thesis, are implemented in the distributed cloud-computing environment Apache Spark. Third, this thesis provides approaches to generate actionable feedback, such as energy consumption and application recommendations, which can be utilized in the mobile devices themselves or when understanding large crowds of smartphone users. The application areas especially covered in this thesis are smartphone energy consumption analysis in the case of system settings and subsystem variables, trend-based application recommendation system, and analysis of demographic, geographic, and cultural factors in smartphone usage.Erilaiset älylaitteet, erityisesti älypuhelimet, ovat muodostuneet oleelliseksi osaksi arkipäivän elektroniikan käyttöä. Älypuhelinten käyttö ei rajoitu perinteisiin kommunikaatiotoimintoihin, vaan niillä on voitu korvata monia muita laitteita ja palveluita, kuten pelit, kartat, sosiaalinen media, ja monet Internetin kautta saavutettavat palvelut. Koska laitteita on saatavilla monissa eri hintaluokissa, ne ovat pääsääntöisesti lähes kaikkien saatavilla, myös maailmanlaajuisesti. Aina mukana kannettavan älypuhelimen käyttö tuottaa runsaasti henkilökohtaista tietoa, mikä tarjoaa mahdollisuuden analysoida käyttäjien päivittäistä elämää. Henkilökohtaisia suosituksia hyödyntäen käyttäjille voidaan tarjota tietoa, joka auttaa parantamaan käyttäjäkokemusta ja laajentamaan älylaitteen käyttömahdollisuuksia. Joukkoistava havainnointi tarkoittaa tiedonkeräysmenetelmää, jossa useat erilliset laitteet osallistuvat automaattisesti suuremman datajoukon kartuttamiseen. Puhelinlaitteista tällaista kerättävää dataa ovat muun muassa tieto suorituksessa olevista ja asennetuista sovelluksista, erilaiset järjestelmäasetukset, kuten verkkoyhteystiedot ja näytön kirkkaus, sekä lukuisat muut järjestelmätason parametrit, kuten suorittimen ja muistin käyttö. Automaattista datan keräystä voidaan täydentää käyttäjille lähetettävillä kyselyillä. Älypuhelimista kerättävän datan analysoinnissa on monia vaiheita, jotka tekevät koko prosessista haasteellisen. Automaattisesti kerättyyn dataan päätyy helposti virheitä ja puutteita, joiden käsittely on hallittava. Datan määrä kasvaa helposti teratavuluokkaan, jolloin analysointiin tarvitaan suurten datajoukkojen käsittelyyn sopivia hajautettuja laskenta-alustoja ja algoritmeja. Hyödyllisten suositusten generoimiseksi puhelinlaitteisiin liittyvän analyysin halutaan usein olevan reaaliaikaista, mikä asettaa lisää haasteita analyysin suorituskyvylle. Tässä väitöskirjassa esitetään menetelmiä joukkoistetusti havainnoidun älypuhelindatan käsittelemiseksi tehokkaasti ja hyödyllistä informaatiota tuottaen. Väitöskirjan alussa kuvaillaan älypuhelindatan keräämistä prosessina, datan esikäsittelyä ja siistimistä hyödylliseen ja käsiteltävään muotoon. Väitöskirja esittää, että puhelinlaitteen tila tulisi ottaa huomioon kokonaisuutena, jossa useat eri tekijät, kuten samanaikaisesti suoritettavat sovellukset ja toisiinsa liittyvät järjestelmäasetukset vaikuttavat toisiinsa. Tämän jälkeen väitöskirjassa esitetään joitakin sopivia tilastollisen analyysin ja koneoppimisen menetelmiä, joita väitöskirjan tutkimuksessa on käytetty älypuhelindatan analysointiin. Kaikki näistä menetelmistä ovat suoritettavissa hajautetussa laskentaympäristössä ja toteutettu Apache Spark -järjestelmää käyttäen. Lopuksi väitöskirja näyttää, kuinka analyysiä sovelletaan käytännössä käyttäjille suunnatun palautteen ja suositusten generointiin. Päähuomion saavat puhelinlaitteiden energiankulutuksen analysointi, puhelinsovellusten trendien havainnointi, ja erilaisten kulttuuristen ja sosioekonomisten taustatekijöiden huomiointi mobiilikäyttöä tutkittaessa