Juha Leino
email: juha.leino@uta.fi

Kari-Jouko Räihä
email: kari-jouko.raiha@uta.fi

Sanna Finnberg
email: sanna.finnberg@uta.fi

All the News That's Fit to Read: Finding and Recommending News Online

Keywords: News, recommending, sociality, online

Our survey study of 147 Finns shows that online news is becoming the most important news source today: Online newspapers have bypassed paper newspapers and also TV and radio in importance, especially among young adults. Although most respondents routinely visited their preferred news sites directly, recommendations from their social network also played an important role in helping them find salient news. We analyzed the factors that affected which recommendations were read and why, and also discuss participants' expectations on the behavior of the receivers of the recommendations. The person recommending and the means of recommending affect what gets read. In contrast with previous studies, we found that the role of email as a recommendation tool is decreasing as the use of social media is becoming more common. However, personally targeted recommendations still have a better chance of being influential than recommendations made to the public at large.

Introduction

Online newspapers have become a major source of news [START_REF] Purcell | Understanding the Participatory News Consumer[END_REF] while media houses struggle to find new earning models to replace plummeting paper newspaper sales in an environment that is used to free access. Simultaneously, the huge number of possible news outlets and news items online means that we need efficient ways to find news items that we are interested to read.

News recommending is an important part of online newspapers' strategy today, as attested by the number of news sites that integrate recommending and social aspects. Huffington Post's integration of Facebook and NYTimes's TimesPeople that allows following other users and editors are popular examples, but many others, such as BBC and Reuters, are also in the game.

We conducted a survey study to see how users in Finland today keep up with news and how they find salient news items online. The most popular method of finding news was to access news sites that the user knew and liked directly. In addition, nearly half of the 147 respondents typically also used recommendations from other people. While recommender sites, aggregation services, and feeds were all used to locate news by some of the respondents, their share in the equation pales in comparison to the recommendations from other people. The popular channels for giving and receiving recommendations were social media and networks, email, IRC, and instant messaging (IM).

In fact, news recommending appears to be part of the normal social intercourse [START_REF] Erdelez | Sharing Tools on Newspaper Web Sites: An Exploratory Study[END_REF] and is used to maintain relations and frequently share emotions. Recommendations are often related to on-going conversations or meant to be used as material for later conversations. These conversations take place both face-to-face and online, sometimes starting in one and continuing in the other. Thus, it is easy to see why increasingly ubiquitous social media plays such an important role in news recommending.

The more personal and direct the recommendation is, the more likely the item is read. On the other hand, when the recommendation is broadcast to the world-at-large, the item itself becomes the point of decision-we ask ourselves if this is an item that interests me. While social media leads in the number of recommendations made in it, its lead in effectiveness is not as clear. Consequently, when designing ways to recommend news, especially in social media, giving people information about the item behind the link is important to help them make up their mind about reading it. Also, giving recommendations a personal feeling helps.

We undertook this study to better understand online news recommending practices as we were designing a news recommending solution in a research project. We wished to understand the underlying dynamics of news recommending to design a system that answers actual user needs rather than imposes new dynamics on users. This paper focuses on the survey results and design implications on a general level.

After reviewing briefly the related work and introducing our method, we dive into the results. We first discuss the media the respondents used to keep up with the news and then look at how they found salient news items online. Recommending practices, how and why news items are recommended, are studied in more detail. After discussing which news recommendations get read and why, we look at the implications the current practices have for supporting news recommending activity online.

Related Work

Today we have access to countless different news sources online and face the challenge of finding interesting news items from the seemingly limitless number of items [START_REF] Chen | Short and Tweet: Experiments on Recommending Content from Information Streams[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF][START_REF] Purcell | Understanding the Participatory News Consumer[END_REF]. This problem of information overload has given birth to such services as Google News and Digg.com, and the research community is constantly looking for new ways to improve news recommendations [START_REF] Phelan | Using Twitter to Recommend Real-time Topical News[END_REF], e.g. by combining collaborative and content-based filtering with each other [e.g. 15] or with other approaches, today increasingly with social network information [e.g. [START_REF] Gürsel | Producing Timely Recommendations from Social Networks through Targeted Search[END_REF][START_REF] Guy | Personalized Recommendation of Social Software Items Based on Social Relations[END_REF][START_REF] Liu | Use of Social Network Information to Enhance Collaborative Filtering Performance[END_REF][START_REF] Phelan | Using Twitter to Recommend Real-time Topical News[END_REF].

The phenomenal growth of social networking [START_REF] Gürsel | Producing Timely Recommendations from Social Networks through Targeted Search[END_REF] has resulted in them "fast overtaking traditional web as the preferred source of information" [START_REF] Gupte | News Posting by Strategic Users in a Social Network[END_REF] and "transforming the way information is created and distributed" [START_REF] Lerman | Analysis of Social Voting Patterns on Digg[END_REF]. Social scientists have long known that social networks are central to the spreading of information [START_REF] Lerman | Analysis of Social Voting Patterns on Digg[END_REF], and today online networks are central to information dissemination, search, marketing, and discovery [START_REF] Lerman | Information Contagion: An Empirical Study of The Spread of News on Digg and Twitter Social Networks[END_REF]. In addition, social networks are used for spreading and recommending news [START_REF] Java | Why We Twitter: Understanding Microblogging Usage and Communities[END_REF]. In fact, today social media sites are often "the first to break the important news" [START_REF] Lerman | Information Contagion: An Empirical Study of The Spread of News on Digg and Twitter Social Networks[END_REF]. In contrast, Bernstein et al. [START_REF] Bernstein | Enhancing Directed Content Sharing on the Web[END_REF] found that email is the preferred medium for content sharing and that social networking sites are used for that to a much lesser degree.

In real life, we rely on recommendations from our friends and family in decision making [START_REF] Liu | Use of Social Network Information to Enhance Collaborative Filtering Performance[END_REF]. The person recommending an item also matters online, as we are interested in items on which our friends have shown interest [START_REF] Gürsel | Producing Timely Recommendations from Social Networks through Targeted Search[END_REF][START_REF] Guy | Personalized Recommendation of Social Software Items Based on Social Relations[END_REF]. Sharing online is after all a social act meant to develop social capital by strengthening weak ties (bridging social capital) and maintaining strong ties (bonding social capital) with friends and family, although especially social networking is more associated with bridging than bonding social capital [START_REF] Bernstein | Enhancing Directed Content Sharing on the Web[END_REF][START_REF] Ellison | The Benefits of Facebook "Friends:" Social Capital and College Students' Use of Online Social Network Sites[END_REF]. In fact, familiarity network is a clearly better indicator of user interest than similarity network [START_REF] Guy | Personalized Recommendation of Social Software Items Based on Social Relations[END_REF]. In contrast, collaborative filtering does not distinguish friends from other neighbors who share similarities with the user [START_REF] Liu | Use of Social Network Information to Enhance Collaborative Filtering Performance[END_REF]. The explicit social information and social processes offered by social networking sites lend themselves well to recommendation generation and can be a reliable source of recommendations [START_REF] Chen | Short and Tweet: Experiments on Recommending Content from Information Streams[END_REF][START_REF] Gürsel | Producing Timely Recommendations from Social Networks through Targeted Search[END_REF].

Consequently, many social networking sites are adding recommending features [START_REF] Guy | Personalized Recommendation of Social Software Items Based on Social Relations[END_REF]. Facebook, for example, is encouraging users to "set up their Facebook accounts for news reading" [START_REF] Kirkpatrick | Facebook Could Become World's Leading News Reader (Sorry Google)[END_REF], and pundits are discussing the possibilities of Facebook becoming the "world's leading news reader" [START_REF] Kirkpatrick | Facebook Could Become World's Leading News Reader (Sorry Google)[END_REF]. Furthermore, Hitwise data shows that Facebook was in fact the 3rd biggest source of visits to news and media sites while Google News was 11th and Twitter 39th (March, 2010-one week's data) [START_REF] Hopkins | Twitter and News and Media Websites[END_REF].

In the beginning of social networks, users mainly posted personal information but today these networks "have metamorphosed into a forum where people post information such as news that they deem to be of common interest" [START_REF] Gupte | News Posting by Strategic Users in a Social Network[END_REF]. In effect, users have become strategic thinkers who weigh in various factors when deciding if and what to post and propagate [START_REF] Gupte | News Posting by Strategic Users in a Social Network[END_REF]. Thus, we need to understand better the characteristics and driving motivations behind the user activity and the effects of the social networks [START_REF] Lerman | Information Contagion: An Empirical Study of The Spread of News on Digg and Twitter Social Networks[END_REF].

Method and Participants

The study was carried out as an online survey designed to provide foundation for designing a news sharing and recommending service. The survey topics concerned reading news (online and offline), recommending news, receiving news recommendations, recommender systems, and the impact of recommendations. The survey form was built so that the respondents needed to answer only the relevant questions. For instance, if the "Yes" radio button was ticked on a question about using news aggregators, the form opened to display more detailed questions (2-13 questions per topic), but if "No" was ticked, no further questions about the topic were asked.

The form had 29 questions where different properties (such as the importance of various news media or the importance of a recommender's identity) were evaluated with a 7-point interval scale (1-7; 7 signifying strong agreement) while activity frequencies were asked with a 5-point scale. All these non-open-ended questions had an openable text field for commenting. In addition, there were several open-ended questions with text fields for answering. An English translation of the survey form, originally in Finnish, is available at: http://tinyurl.com/6e9n5af. The participants were solicited at three Finnish universities through mailing lists distributed to staff and students in several departments. Four movie tickets were raffled among the respondents. We received 147 responses (from 83 males and 63 females). Fifty-eight respondents were students and further 36 worked at a university.

Our respondents were on the younger side with the mode being the 20-29 age group (Table 1). The slant towards younger respondents means that we have to be careful when contrasting age groups, given the smallness of Below 20, 50-59, and Over 60 age groups. Consequently, our results as a whole represent more the attitudes of adults and young adults than those of high-schoolers (or below) or seniors. Therefore, the results offer a good foundation for designing, as they provide a glimpse of up-and-coming use practices. Survey-studies face well-known problems: We cannot be sure how seriously the respondents took their task, and the results are based on self-report, not observation. On the other hand, there is no other practical way of collecting such information in large scale. To improve the reliability of the data, the responses were screened for bogus entries; most respondents included verbal comments indicating that they had given considered responses.

When we study representatives of one culture, as most studies in fact do, we have to be careful when generalizing the results to other cultures. Culture is in many ways a significant factor in communication, as collectivist cultures (e.g. South Korea) emphasize relationship-building aspects in communication while individualistic cultures (e.g. USA) focus on information [5, pp. 196-205]. Scandinavian countries tend to be amalgamations of both approaches [START_REF] Kalogeraki | The Divergence Hypothesis in Modernization Theory Across Three European Countries: the UK, Sweden and Greece[END_REF].

Reading News

Our results show that online news constitutes today the most important source for news, at least for young adults. While TV and paper newspapers are still clearly in the picture, their grasp on the audiences is weakening, especially when it comes to younger people. Table 2 summarizes how important the respondents found different media for themselves in following news on average and by age and gender groups.

Overall, online newspapers were clearly the most important medium for following news. It had both the highest rating (5.99) and the lowest standard deviation (STDEV) (1.28). Paper newspapers (4.56/1.96) and TV (4.13/1.96) followed online newspapers, but with lower ratings and higher STDEVs, underlining that they were important only to some respondents. The same applies to online tabloids (4.12/2.10) that had the highest STDEV in the sample. (Tabloids in Finland are between traditional newspapers and yellow journalism-credible sources of news but tend to go for a more scandalizing angle.) Online tabloids were clearly more important than paper tabloids (2.32/1.49). Together, online tabloids and newspapers are a more important source of news than their paper cousins and, in fact, dominate as a source of news for the respondents on average. While online newspapers are a more important source of news for the younger, practically all read them (96%).

TV is still an important source of news for many as are paper newspapers. Paper newspapers were read by 119 (81%) respondents and subscribed to by 59 (40%). Still, 19% claimed never to read them while only 4% claimed not to read online newspapers.

Computers vs. Mobiles for Reading Online News

On average, the 96% of the respondents who read online papers spent 1 h 10 min a day to read them on PC. The respondents who read online news on a mobile phone (27%) spent on average 15 minutes a day reading news on it.

Altogether 102 respondents (69%) had an Internet connection on the mobile, but only 38% of them read news with the mobile. The four respondents who described their use mentioned reading news when on the move, e.g. sitting in a bus or train.

How the Participants Found Online News

We asked the respondents how they typically found the news they read (Table 3). On average, each respondent used 2.5 ways to find online news. Although the respondents predominantly found news by going to news sites they knew and visited regularly, recommendations also had an important role, as 70 respondents found news through recommendations from others, with 22% of them receiving recommendations Several times a day and 53% at least Several times a week. Moreover, 15 used recommendation systems, such as Slashdot.org or digg.com, and 15 found news by newsletters from news services. Table 3. How participants typically found online news (later in the paper, the numbers of users for a medium may be higher than here, as here we show the number of respondents who mention using the method typically).

Method Users

Accessing online papers directly 123 (84%) Accessing TV news online services directly 68 (46%) RSS etc. feeds 32 (22%) News aggregation services 30 (20%) Recommendations from people 70 (48%)

Recommendation systems 15 (10%) Newsletters from the news services 15 (10%) Other 8 (5%)

Of the News aggregation services, Ampparit (a Finnish news aggregator) was the most popular (25 mentions) and Google News the second most popular (5 mentions).

Respondents overwhelmingly used only one news aggregator.

Among the RSS feeds mentioned by name (one user mentioned using over 400 feeds and another about 80, and predictably neither named them), the most popular ones were HS (the largest-circulation newspaper in Finland) (8 mentions), YLE (Finland's national broadcasting company) (6 mentions), and BBC (6 mentions).

Recommending News

Roughly one third of the respondents (31%) recommended news at least Several times a week and over half (59%) recommended them at least Several times a month. There is a clear symmetry and reciprocity in making and receiving recommendations: The respondents who made more recommendations also received more of them, and the respondents who made fewer recommendations also received fewer. The correlation is highly significant (r = .5943, p < .0001).

In addition, the respondents reported receiving more recommendations than making them (which might be at least partially explained by one person often recommending one item to more than one person, considering that Social media and IRC were the most popular ways of recommending).

Reciprocity was not as evident in the rating of how important the respondents considered recommending news back to people from whom they had received recommendations (3.75). On the other hand, respondents did find being able to comment or otherwise acknowledge the recommendation rather important (4.54). In addition, when asked why they recommended news, two respondents stated they did it to get recommendations back. Moreover, another two respondents mentioned having stopped giving recommendations to somebody because of lack of response. Thus, reciprocity appears not limited to recommending news back but also involves other means of responding to the recommendation, as responding showed appreciation.

How Respondents Made News Recommendations

Figure 1 shows how the respondents recommended news. Social media and IRC are the winners, closely followed by IM. Tell a friend (emailing through the news site) type of features, on the other hand, were not popular, as further attested by the fact that in Other, giving the recommendation face-to-face was mentioned 21 times, consequently making it more typical than using Tell a friend. While the difference to the results of Bernstein et al. [START_REF] Bernstein | Enhancing Directed Content Sharing on the Web[END_REF] is striking-they found email to be the most common tool for online sharing-the importance of social media in sharing evident in our results concurs with some other studies (e.g. [START_REF] Gupte | News Posting by Strategic Users in a Social Network[END_REF]). The reason behind the difference can be the different respondent profiles (inc. cultural factors) or that they studied sharing any web content while we focused solely on sharing news.

On average, the respondents who made recommendations used two means for it. Some means appeared to go better together than others. IRC and Social media use were clearly connected, as 65% of IRC users also used Social media and 58% of Social media users also used IRC. Similar reciprocities were found for IRC -IM and IM -Social media. Email, on the other hand, was a natural companion to Tell a friend (69% of its users also used email) and to some degree, those who used Other (34% also used Email, the most popular second method for Other users). It appears that email is the core means for all those not in the previous cluster.

In effect, we see two clusters (circled in Figure 2): One for ICR, IM, and Social media, and one for Email, Other, and Tell a friend. To be sure, the borders are porous and the clusters are not clearly defined, but some formation is evident, nonetheless. The same clusters can be seen in some other characteristics as well. For instance, TV as a medium for news did not appeal to those who used Social media, IM, and IRC to recommend news. These groups rated its importance below 4 (3.65-3.95), while those who used Email, Tell a friend, and Other means to recommend news rated it somewhat higher (4.41-4.66). We also analyzed the age distribution of users of each recommendation medium (Figure 3). Those using Email to recommend news users were on average slightly older: 61% were 30 or over while in the whole sample only 45% were 30 or over. In contrast, those using IM were chiefly in the younger age groups.

In Other, in addition to the 21 mentions of recommending news face-to-face, 6 recommended them over the phone and 1 mentioned using SMS. Bernstein et al. [START_REF] Bernstein | Enhancing Directed Content Sharing on the Web[END_REF] also found face-to-face link sharing to be very common.

What Kinds of News Are Recommended

Figure 4 shows the most common types of news categories (those mentioned in 10 responses or more). The similarity in the sent and received news types is evident.

Why News Are Recommended

When the respondents were asked why they recommended news, 6 reasons were mentioned by more than 10 respondents: Sharing funny things (77 times), Informing (56 times), Conversation (24 times), Participation (15 times), Feeling that the item would interest the receiver (13 times), and Staying in touch (12 times).

In addition to Conversation and Participation being common reasons for recommending news, many reasons mentioned by fewer than 10 respondents were also related to maintaining social bonds: Topic of common interest (3 times), Sharing an emotion (3 times), Giving a good feeling (2 times), and so on.

With 24 respondents referring to starting or maintaining an ongoing conversation as a reason for recommending news, news recommendations are clearly part of overall social behavior. The conversations can take place face-to-face, virtually (computer-mediated), or partially virtually and partially face-to-face.

Furthermore, out of the 9 respondents who reported having stopped recommending news to somebody, 2 explained that it was because they were having less and less to do with the person. All this goes to underline how recommending news is part of overall maintaining of human relationships within our social network.

Framing the Recommendation: What to Include

The respondents saw being able to include one's comment (4.81/1.90) and being able to include a piece of the article (4.20/1.90) as quite important. Commenting helps avoid misunderstandings as to sender's opinion-"That's where I say what I think about the article, I don't want somebody to think that I actually consider the item important, usually the opposite"-and allows the receiver estimate if he or she should read the item: "Comment could be used to emphasize what made the news article important in the first place or why it's sent to this person".

Using Social Media to Recommend

Social media was the single biggest means of sending and receiving news recommendations. Social media here is largely synonymous with Facebook, as only it and Twitter were mentioned, and Twitter use was very low in comparison: 56 respondents recommended news in Facebook while only 4 did so in Twitter.

Many Social media users gave a description of how they recommended news in social media. These descriptions show that like with Tell a friend, tools provided by news sites to add news items to Facebook are not that popular-only 2 mentioned using them. One reason might be that other methods offer better ways to comment the recommendation: "… Facebook-I post the interesting URL there and write as short a comment as possible".

In fact, no method offered by Facebook for recommending was a clear winner. Respondents mentioned using Wall, Inbox (private messages), Feeds, Status, Share feature, Chat, and Groups for sharing news items. In effect, Facebook offers both means to recommend directly to persons and means to recommend to the world-atlarge. As a result, Social media recommendations can be direct or indirect, i.e. personal or aimed at a larger audience. In addition, Facebook is also an IM environment: "Facebook. That's where I talk with my homies the most as I am surfing and I drop them a hint if I read something funny…" Thus, categorizing Facebook simply as a Social media is somewhat misleading-as a social media site, it actually covers many bases in online communication. Although Twitter has caught on slowly in Finland, it appears to be coming along, as more people receive (and would like to receive) recommendations from it than currently use it for making recommendations.

Which Recommendations Get Viewed and Why

How respondents acted on recommendations

Overall, a significant number of recommendations get read (Table 4): 65% read All or Most news items recommended to them, 83% read at least About half, and only 4% claimed never to read them. Interestingly, the decision on whether or not to read an item appears to hinge significantly on the relation between the sender and the receiver of the recommendation: "I don't get that many news recommendations but when I do, I read them (if they're from somebody I know well)". In fact, the respondents rated the importance of the person making the recommendation to reading the recommended news item as 5.45, thus underlining the importance of the sender to reading. In addition, the medium through which the recommendation is received also plays a role. Figure 5 summarizes the effect of the medium through which the recommendation was received. If Social media has shone in other aspects, here it shines for all the wrong reason: Only Other had a lower reading rate. While 61% of the users who received recommendations through Social media read All or Most recommended items, the respondents who received recommendations through Email, IM, and IRC read over 70% of them, and the respondents who received recommendations through Tell a friend read 90% of them. We also found that the perceived importance of Social media was lower than that of others for its users-only Tell a friend was perceived as less important by its users than Social media. Thus, although Social media was used a lot, other (less used) means were considered more important means of recommending, perhaps due to their directness. The importance of the medium shows even more pronouncedly in the respondent comments: "The recommendations I get in Facebook are not as 'important' as the ones I receive in email. I check out the links in Facebook only if I got extra time. The links I get in email I check almost always, because they are directed to me and are thus more personal." Repeatedly, the message is the same-if it is directed personally to me, it is important, but if it is not directed at me personally, it is not as important. Facebook is mentioned repeatedly because of its popularity, but at the end of the day Facebook simply appears to represent a medium where the recommendations are not direct or personal but rather broadcast at the world-at-large. Email is considered more important because it is both direct and personal. Thus, how direct and personal a recommendation is determines to a large extent if it gets read, somewhat reducing the importance of Social media in news recommendations.

We conjecture from our data that when it comes to indirect, non-personal recommendations, different factors determine if the item gets read or not. First, the significance of the headline to an item getting read was rated very significant at 5.63 (1.33). The significance of the title is likely to increase when the recommendation is neither direct nor personal. Therefore, whether or not the receiver reads an article recommended in Facebook depends much more on the information available about the item itself, such as title, since its importance cannot be determined from the relationship (e.g. "Because of my relationship to this person I should check it out" and "This person often makes good/bad recommendations" type of factors).

The title is a good indication of the topic and theme of a news item, and resultantly a very important factor in deciding whether or not to check the item out. If the topic and theme are shown in the recommendation, the receiver can easily decide whether it is of interest. When it comes to news in particular, the source is also likely to be important as it gives hints of the treatment of the topic (e.g. Fox News vs. NYTimes). Therefore, when the person making the recommendation is not the deciding factor, the available information on the topic and source becomes a more significant factor.

When there are too many recommendations that are not salient to the receiver, they all tend to get treated as spam. For example, while some respondents received news recommendations from online services, at least some ignored them: "I get some but I consider them spam and I don't read them…" Consequently, in giving news recommendations, a large enough number of the recommendations need to interest the receiver or the recommendations are perceived as spam.

This applies not only to services but also to people who make personal recommendations, as seen in: "Well, if it's from some jerk-off, then I won't bother". As O'Donovan and Smyth [START_REF] O'donovan | Trust in Recommender Systems[END_REF] state, we also need to trust the quality of the recommendations a person makes, and the past quality of the recommendations the person has made appears to weigh in the equation. Also, the trustworthiness of the person making the recommendation may be context-specific.

How Respondents Expected Others to Act

The respondents expected the receivers to read their recommendations roughly as often as they reported reading the recommendations they received. The correlation is highly significant (r = .4016, p < .0001). Interestingly, the respondents rated the importance of reading recommendations sent to them (4.79) much higher than the importance of others reading their recommendations (3.58). In addition, the respondents rated the possibility of being able to comment received recommendations at 4.54 but getting a response from the receiver of their own recommendations only at 3.51: "This is a difficult one, because [getting a response] is not that significant but … since we tend to talk and joke [about it], it is important in that sense." In effect, the respondents placed more responsibilities on themselves than others.

In summary, making recommendations between people who know each other and engage in many forms of social intercourse is different from recommending news to unknown people. When the sender and the receiver know each other, the sender considers the interests of the receiver, and news recommending takes place within the larger frame of social intercourse. On the other hand, when making recommendations to a faceless mass, for instance "digging" something, we are more involved in spreading the word. The content of the piece becomes important instead of the recommendation being aimed at building and maintaining relationships. It seems likely that the recommended items and topic are also different.

Recommendation sites, Aggregation services, Feeds, and Recommendation Emails

While recommendation sites (e.g. Slashdot, Digg, and Reddit), aggregation services (e.g. Ampparit and Google News), feeds, and email recommendations from online services were used by some respondents, their share of the news recommending cake pales next to the human-to-human(s) recommending activity taking place. However, to their own users, they were important to finding news.

The 36 respondents who used aggregation sites and services (typically used by 30) rated their importance at 5.03. A few mentioned using filtering to get rid of Big Brother (a reality TV-show) and similar types of news or to get news only from some categories. Their comments underline that aggregation site users are happy with the services: "An easy way to get a general view of especially the foreign news without any personal trouble" and "They are easy and pleasant to follow during the day…" Feeds were used by 34 respondents (typically used by 32) of whom 85% were males. The feed users rated their importance to news following at 5.28, therefore more important than aggregation sites or recommendation sites were to their users.

Recommendation sites, mainly Slashdot (15 mentions), Digg (5 mentions) and Reddit (3 mentions), were used by 19 respondents (89% males). Recommender site users rated their importance to following news at 4.47, therefore also fairly high.

The 19 users who received email recommendations from online services (typically used by 15) all used different services-no service was mentioned even twice. Some were connected to the user's work, some to hobbies, and some to more general interests, such as economic newspapers. What set the respondents who received such email recommendations apart from the overall sample was their age: 37% were 40 years old or older, while in the whole sample only 11% were 40 or older. Interestingly, the respondents who received email recommendations from online services did typically not pay that much attention to them. No respondent said anything positive about them but many mentioned not reading them: "I don't remember. I ignore most of them.", "I get some but I consider them spam and I don't read them…" and "Much of the time I don't read them…" 8 Who to "Follow" for News Recommendations

The respondents were also asked to rate the importance of following friends, editors/journalists, specialists (in a given field), unknown people who shared the same interests, and of getting automatically generated recommendations that took their interests into consideration. We asked them to rate them independent of whether they used such systems, as we wanted to probe their attitudes towards such features.

It turned out that the respondents were not that enthusiastic of any of these options. Getting automatic recommendations based on their interests was the most highly rated (3.06/1.85). The comments, while often mildly positive-"Could be interesting…", "If such feature existed…I'd sure use it"-also focused on potential problems and showed a decided lack of faith in automatic systems, e.g. "I don't believe there could be an efficient automatic news recommending system for my interests" and "Would be an interesting feature if it worked, otherwise it would simply be irritating if it produced extraneous, uninteresting stuff." The lack of trust on automatic recommendations appears symptomatic, and this can be one significant reason behind their relative lack of popularity among our respondents.

Following friends or editors were not greeted with enthusiasm, either, at 2.29 and 2.20, respectively. Neither did following unknown people with shared interests at 2.48 fare much better. In fact, following specialists in a particular field was seen as more important than the other options at 2.77. We postulate that the reason for this is partially the control it offers over the type of news. In addition, the effect of the authority position such people may enjoy should also be studied, as our results do not shed light on this aspect of the equation.

Consequently, recommendations coming from friends were important and likely to be read (if direct and personal) but following friends was not considered important. We conjecture that the reason is that simply following friends means that the friends have not pre-selected news items that are likely to be of interest to us (quality). Also, following friends does not strengthen our social ties the way making and commenting recommendations does (part of larger social intercourse). Thus, if a news recommending system enables following, it should also provide features that enable sociality or improve the recommendation quality from a dull stream of what others are reading.

Implications of Current Practices

Quality vs. Sociality

There appear to be two dimensions working in news recommending practices: 1) getting good recommendations, and 2) recommending being part of the larger social intercourse between people. The two are, of course, not contradictory to each other, as the people who know us are in position to give us good recommendations. Social recommending serves both needs but emphasizes social aspects while automatic news recommendations, such as those based on collaborative filtering, tend to emphasize quality aspects and ignore social aspects.

Significantly, many recommendation providers have begun to add social aspects to their service, e.g. following friends in Digg. On the other hand, sociality-emphasizing recommending systems cannot ignore the quality issue, or they might end up being regarded as spam and therefore ignored, as has happened to recommendation emails. The reason why Email and Tell a friend recommendations get read is not only because they are from our social contacts (personal) and likely of high quality. They are also typically not too many. On the other hand, Facebook recommendations not made personally to us are judged on other merits than maintaining social connections, and so the title and other aspects that tell us about the news item itself become more important. With this type of recommendations we need to help the user receive only good recommendations and not be flooded with semi-targeted ones.

The challenge is to apply the two ingredients in right proportions to different services. Is the purpose of Digg to provide a social experience or help people find salient reading? If it is to provide good recommendations, then the focus should be on quality of recommendations and social aspects should be used to spice the experience but not allowed to come in the way of recommendations. In contrast, in Facebook news recommendations are a spice and a consequence of the larger social experience, something that nicely integrates into the social intercourse. Consequently, Facebook needs to give tools that make news recommending within its ecosystem an easy and enjoyable addition to the overall social experience and make sure that these tools do not distract users from the social experience.

Of course, these examples describe exactly what Digg and Facebook are doing. There are also many other approaches being developed and tested today, such as NYTimes's TimesPeople (based on following other users and NYTimes editors and journalists) and Huffington Post's integrations with Facebook (the integration means that the readers do not need to build their social networks once again in yet another service). The key is to know what one's focus is and not to try to be a be-all-end-all.

Design Implications

How to approach news recommending and the inherent recommendation quality vs. supporting social intercourse equation hinges very much on the service and its focus and its business model. News providers need to decide how and at what level they want to take part in existing social networks and recommending services. Judging by the number of Facebook and Digg icons popping up next to news items, the news providers do see some level of integration with these services as desirable.

Provide item title and other salient information in the URL: Whether a news item is recommended directly to the reader or to the world-at-large, the news provider should strive to provide as much information about the news item as possible. A trusted person making a recommendation combined with an interesting item is a winning combination.

Although in the context of social network integration the news provider can to some degree affect the amount of information provided to the receiver, many news items are still recommended as URLs and whatever else accompanies the recommendation is beyond the provider's control. News providers should therefore strive to provide as much information about the item in the URL as feasible. In an URL like http://www.somepaper.com/article/iU6040?type=mN?src=mv all we see is the source (somepaper). In comparison, http://www.somepaper.com/news/asia/2010-April-12/Isthe-West-Engaging-China.html gives us a lot of information to whet our appetite.

Make it personal and direct: Personal and direct recommendations are likely to get read: If a friend who knows us recommends us personally something, it means that he or she has selected it for us and has a reason to believe that we should read the item and by reading it we can strengthen the relationship (we might discuss the article over lunch) and by not reading we might hurt the relationship (the sender asks us about the article or refers to it and realizes that we did not read it). Therefore, the recommendation is likely to be good and we stand to gain social capital (or lose it, perhaps even a stronger motivator).

Consequently, a social networking site-or any other site, for that matter-should make sure that there are tools for direct recommending, from one user to one or more users (that may provide features to facilitate commenting or discussing the item). In fact, this is what social networking is about in part. By providing such tools, we can make it more likely that users do news recommending within our service rather than switching to another service or email.

Recommending to the world-at-large: On the other hand, social networking sites also need to allow recommending news to the world-at-large. While enabling such recommendations and providing social networking tools, such as a commenting feature, is a start, there are also other possibilities to enhance such recommendations.

Although following friends or other users was not rated that important in our study, the success of TimesPeople has shown that it does have its place even if it is not to everyone's liking. Giving the users who agree to be followed a possibility of selecting what they recommend (instead of a stream of everything they view) has two advantages. First, it allows the users being followed to show explicit liking-not everything they view is to their liking-and consequently improves the quality of recommendations and keeps their number down, and second, it provides privacy protection that encourages users to allow the following to begin with.

To make it easier for users to follow recommendation streams, the number of items has to be sensible and they have to have a high enough occurrence of salient items not to be seen as spam. One approach is to support filtering the streams (also discussed by Bernstein et al. [START_REF] Bernstein | Enhancing Directed Content Sharing on the Web[END_REF]). We found that some feed and aggregation site users had a tendency to filter recommendations explicitly to get certain types of news and to avoid other types. While these means were not used by the majority, their users were quite happy with them, suggesting that services should support such means. Another approach is to automatically filter the recommendation stream to reduce the number of items and improve the overall quality of recommendations. Social networks offer an especially good foundation for filtering with the ready networks of friends in place.

While our participants found most news by going directly to the news sites they knew and liked, recommendations from others also played a significant role in how they located salient news items. In effect, news recommending is part of the normal social intercourse where we keep in touch with people and share emotions and information.

The more directly the news recommendation is made, the more likely we are to read the item. For less direct recommendations, we make the decision more based on what we know about the item, i.e., we try to judge if the item interests us. Consequently, the sites involved in news recommending have to decide whether their focus is on sociality (with news recommendations being only a part of the larger social intercourse taking place between people) or quality recommendations (with social aspects providing spice to the experience). The question is to understand one's focus and to blend the right mixture for it. Understanding the larger social intercourse taking place between people helps us understand better how to integrate recommending news and other online information into that intercourse.

If we do not provide means for recommending with sociality taken into consideration, humans will find a way to add sociality to the experience, for example by emailing the URLs to each other. Email was not built with news recommending in mind but it continues to serve it well, in part because it opens way for all kinds of related social discourse. If we want to take things beyond this, we need to design for supporting human behavior. One of the secrets behind Facebook is that it supports well human sociality and consequently offers support for what we do all the time in any case-deal with other humans for fun and profit.

Fig. 1 .

 1 Fig. 1. Recommending means used by respondents (darker top area indicates respondents who did not use any other means to recommend news).

Fig. 2 .

 2 Fig. 2. What other means were used with each means.

Fig. 3 .

 3 Fig. 3. Recommendation means used by age groups.

Fig. 4 .

 4 Fig. 4. Most common news types recommended.

Fig. 5 .

 5 Fig. 5. What share of the recommended news gets read by the means of recommending.

Table 1 .

 1 Participants by age (one did not provide) and gender (one did not provide).

		< 20	20-29	30-39	40-49 50-59	60 >	Total
	Males		40	32	7	2	1	83
	Females	4	37	15	5		2	63
	Total	4	77	48	12	2	3	146
	Percentage 2.72% 52.38% 32.65% 8.16% 1.36% 2.04%

Table 2 .

 2 Importance of different media by age and gender (differences between all distributions are statistically significant except for paper newspaper-Internet tabloid and TV-Internet tabloid pairs).

		TV	Radio	Newspaper (paper)	Newspaper (Internet)	Tabloids (paper)	Tabloids (Internet)
	AGE (146 respondents as one did not provide age)
	<20	5.75	3.75	6.50	6.75	3.00	4.00
	20-29	3.94	2.91	4.36	6.14	2.30	4.08
	30-39	3.75	2.96	4.30	6.08	2.21	4.25
	40-49	5.42	3.33	5.75	4.75	1.58	3.33
	50-59	5.50	4.00	6.00	4.00	2.50	4.00
	60>	7.00	4.33	6.00	6.33	6.00	6.33
	Below 40	3.92	2.95	4.41	6.14	2.29	4.14
	40 and over	5.71	3.59	5.82	4.94	2.47	3.94
	GENDER (146 respondents as one did not provide gender)
	Female	4.78	3.46	4.87	6.02	2.57	4.54
	male	3.65	2.70	4.30	5.95	2.13	3.83
	TOTAL SAMPLE (147 respondents)
	AVG	4.13	3.03	4.56	5.99	2.32	4.12
	STDEV	1.96	1.79	1.96	1.28	1.49	2.10

Table 4 .

 4 How many recommended items respondents read and expected others to read.

		Read	Expected others to read
	Frequency No.	%	No.	%
	All	37	26 %	34	26 %
	Most	57	39 %	62	48 %
	About half	26	18 %	23	18 %
	Less than half	19	13 %	7	5 %
	None	6	4 %	3	2 %
	No reply	2		18
	Altogether 145		129

Acknowledgments

This work was supported by the ICT SHOK project on Flexible Services. The second author was supported by the Academy of Finland grant 1130044.


Stefan Hennig
email: stefan.hennig@tu-dresden.de

Jan Van Den Bergh
email: jan.vandenbergh@uhasselt.be

Kris Luyten
email: kris.luyten@uhasselt.be

Annerose Braune
email: annerose.braune@tu-dresden.de

User Driven Evolution of User Interface Models -the FLEPR Approach

Keywords: User interface models, model transformations, interactive model transformations, model consistency, model synchronization

In model-based user interface development, models at different levels of abstraction are used. While ideas may initially only be expressed in more abstract models, modifications and improvements according to user's feedback will likely be made at the concrete level, which may lead to model inconsistencies that need to be fixed in every iteration. Transformations form the bridge between these models. Because one-to-one mappings between models cannot always be defined, these transformations are completely manual or they require manual post-treatment. We propose interactive but automatic transformations to address the mapping problem while still allowing designer's creativity. To manage consistency and semantic correctness within and between models and therefore to foster iterative development processes, we are combining these with techniques to track decisions and modifications and techniques of intraand inter-model validation. Our approach has been implemented for abstract and concrete user interface models using Eclipse-based frameworks for model-driven engineering. Our approach and tool support is illustrated by a case study.

Introduction

Industrial and particularly safety critical systems have to meet extensive requirements since the cost of failure is high and might result in loss of life. Thus, also the User Interfaces (UI) to operate such a industrial system need to be clear and without ambiguities with respect to defined behavior of the system. Specifically in the industrial automation domain, Visualization Systems enable factory-trained operators to monitor the operative states of automation systems as well as to operationally intervene in the process.

Since these operators should react appropriately to exceptions in the technical process even in extreme situations, they are required to give an early feedback to the system design. Therefore, a User-centered Design (UCD) [START_REF] Norman | User Centered System Design: New Perspectives on Human-computer Interaction[END_REF] process is recommended. UCD is an iterative process: Ideas and concepts of the UI will be concretized into prototypes that are tangible for end-users in an early point of time. According to the end-users' feedback after evaluation, the prototype will be improved or modified and thereafter be evaluated again. Fig. 1 gives an overview of this iterative process. Moreover, Model-based User Interface Development (MBUID) procedures have established to ensure the quality, usability, and sustainability of interactive applications. Since visualization systems are interactive applications [START_REF] Sheridan | Supervisory control[END_REF], it is recommended to apply the methods proposed by MBUID: Basically, it uses models at different levels of abstraction, whereas moving from more abstract models to more concrete models means adding new information related to the UI design of the particular level of abstraction. This concretization is achieved by transformations. These are completely manual or require manual post-treatment because one-to-one mappings between models cannot always be defined.

Concepts

User-centered design processes are about continuous improvement of user interface prototypes, already in early development stages. In MBUID, these modifications can take place at a concrete level where the UI is getting more concrete. However, altering UI models at the concrete level might not influence the abstract models, which leads to model inconsistencies. Therefore, we can identify two main challenges which arise if we want to combine MBUID with UCD: (1) Ambiguities during transformation and (2) Model Consistency.

In this paper, we propose a Flexible Workflow for early User Interface Prototyping (FLEPR) -an approach to resolve ambiguities during transformation interactively, thus keeping the user in control over design decisions. FLEPR combines interactive transformations with intraand inter-model validation and with techniques to track decisions and modifications. Therefore, FLEPR combines UCD with MBUID by keeping the models consistent and preserving their semantic correctness at any time. The FLEPR is supported by a proof-of-concept tool. This tool was used in a small project to show the feasibility of this approach.

UCD as an iterative process advocates the use of tangible prototypes in order to involve end-users in the design process as early as possible. These prototypes will be altered according to end-users' feedback after an evaluation -and then be evaluated and be improved again. This is what the term evolution implies -modifications to artifacts at all stages of software development [START_REF] Ivkovic | Tracing evolution changes of software artifacts through model synchronization[END_REF]. Once the end-users' needs are satisfied, the prototype can be provided with complete functionality and deployed as a product. Fig. 1 illustrates the UCD process. The input of end-users is of great importance to create an interactive system that is well received by and tailored for the target end-user group. However, for industrial automation (and by extension safety critical systems), we need to be wary of inconsistencies between user preferences and required system behavior. After all, a correct operation of the system is more important in this case.

The Cameleon Reference Framework [START_REF] Calvary | A unifying reference framework for multi-target user interfaces[END_REF] defines development steps for Modelbased User Interface Development (MBUID) by introducing UI models at different levels of abstraction. The Final UI level captures UIs ready for execution on a particular platform (see Product in Fig. 1). A Concrete UI level abstracts the Final UI from a particular platform. Movisa (see Section 3.2) is our representative for this level. It defines a high-fidelity concrete syntax which is tailored to the needs of automation engineers. Therefore, it corresponds to the prototype level of UCD depicted in Fig. 1 -an evaluation by end-users can be provided. Abstract UI definitions form the next level of abstraction proposed by the Cameleon Reference Framework. CAP3 (see Section 3.1) forms an appropriate realization. It provides a concrete syntax which supports interaction designers in their work (see Concepts in Fig. 1).

Transformations are the key to progress from one model to another in MBUID. Three types of transformations have been defined in [START_REF] Vanderdonckt | A MDA-Compliant environment for developing user interfaces of information systems[END_REF]: (1) Abstraction creates a more abstract model from a given model, (2) reification concretizes a model, and (3) translation produces models at the same level of abstraction. Another taxonomy, introduced by Mens et al. [START_REF] Mens | A taxonomy of model transformation[END_REF], distinguishes in that context vertical (abstraction, reification) and horizontal (translation) transformations. Furthermore, they define an additional dimension: A transformation is endogenous if source and target models are compliant to the same metamodel; it is exogenous if they are compliant to different metamodels. In [START_REF] Clerckx | The mapping problem back and forth: customizing dynamic models while preserving consistency[END_REF], the authors refer to transformations as mappings and explicitly include human intervention during the mapping as a possibility. This has not been fully explored in MBUID literature to the authors' knowledge. For resolving ambiguities while ensuring consistent system behavior, human intervention during transformations is inevitable.

When combining MBUID and UCD for the problem at hand, the starting point is a CAP3 model (see Fig. 2) situated on the abstract UI layer. Using a vertical, exogenous transformation, a CAP3 model will be refined into a Movisa model ( in Fig. 2) whereas unambiguous one-to-one mappings cannot be ensured. According to the end-user feedback, the Movisa model will be modified or improved using horizontal, endogenous transformations ( in Fig. 2). In other words, the Movisa model will be refactored. Refactoring the Movisa models might lead to inconsistencies with the CAP3 model. Resolving these issues requires Model Synchronization ( in Fig. 2, see also dashed arrow in Fig. 1). According to Ivkovic and Kontogiannis [START_REF] Ivkovic | Tracing evolution changes of software artifacts through model synchronization[END_REF], model syn-chronization is "[...] the process of establishing an equivalence between two models when one of them is altered." The aim of this paper is to combine the User Centered Design process and Modelbased User Interface Development techniques complying with the Cameleon Reference Framework. FLEPR enables a flexible workflow that solves ambiguous mappings during model refinement by means of interactive transformations ( in Fig. 2). It enables incorporating end-user feedback into concrete models and keeps track of these manual model refactorings ( in Fig. 2). As this can cause inconsistencies with abstract models, FLEPR provides model synchronization means ( in Fig. 2. After explaining the basics of both CAP3 and Movisa, the following sections discuss our FLEPR approach on a conceptual and on a technical basis. A case study proves its feasibility.

Background

This section gives a brief introduction of the used modeling concepts CAP3 (Section 3.1) and Movisa (Section 3.2).

CAP3

CAP3 is a modeling language for abstract user interface models that provides both a concrete syntax (a graphical notation) and an abstract syntax (meta-model). Its concrete syntax is based upon the Canonical Abstract Prototypes notation [START_REF] Constantine | Canonical abstract prototypes for abstract visual and interaction design[END_REF] (CAP). CAP provides a number of abstract UI components to describe the structure of a (graphical) user interface in a way that is independent of any concrete toolkit or even modality. There are three main UI components: tool, container and active material. A tool allows a user to trigger a change in the UI or in the functional core (e.g. a button), a container can hold data (e.g. a label, image) or any other UI component (e.g. a window), and an active material is a combination of both previous components and thus can both hold data and trigger a change in the UI, data or functional core (e.g. a textfield, the Microsoft Ribbon). There are many other UI components besides these three that have much richer semantics and visuals, but are entirely optional as they are special cases of them.

The difference is illustrated in Fig. 3. Fig. 3a only uses the three basic UI components to specify a login dialog: A user can enter a login and a password as well as confirm or clear this information. Fig. 3b shows the same login dialog, but this time using richer UI components where appropriate. It shows that the login can be selected from a set of available options (using a selectable collection) and that the password has to be entered from scratch each time (using an input). The confirmation ends the activities in this dialog (using an end component) and visually confirms that Clear removes the data in the interface. The dashed rectangle (conceptual group) around the login and password show that both UI components belong together. Both versions of the login dialog, however, do not specify the relations between components. For example, they do not specify that confirmation can only be done when both login and password are specified or that the clear tool clears both the login and password. This behavior can be specified by using the additions made in CAP3; it adds the capability to specify behavior and relations to the domain model or functional core, as can be seen in Fig. 3c. It specifies that Confirm and Clear are only available after the login and password are specified using the enable relation. The fact that Clear clears both the login and password is shown using the update relation (thick gray dotted arrow). Fig. 3c also shows that the logins are fetched from the user database. Note that the conceptual group is used to reduce the number of arrows; all relations that are connected to a conceptual group can be replaced by relations to all its contained components.

Movisa

Movisa is a Domain Specific Language (DSL) to create models for technology independent development of interactive systems in industrial automation that require complex graphical representations (e.g. visualization of a plant process). This domain has certain requirements for the UI components, the behavior of those components, and the protocols to gather the data that influence this behavior. A Movisa compliant model contains three sub-models (see Fig. As shown in Fig. 4, the Presentation Model defines UI Components whose properties where classified in the following three categories: (1) The Representation category defines the initial appearance of a UI component on the screen. It encapsulates representations for properties such as Position, Size, or Border Color. (2) An Animation category captures properties defining the dynamic behavior of UI components in order to reflect the current state of the process. Thus, properties of the Animation influence properties of the Representation during runtime. (3) The Interaction category allows to configure the user's interaction means as well as the resulting actions. Based on a survey of conventional visualization systems for industrial automation, we deducted that these properties -and the relations between them (see Fig. 4) -meet the requirements of industrial automation.

Conventional visualization systems provide scripting environments in order to enable developers to integrate application specific logic. This is what the Algorithm Model provides in a technology independent manner using a customized Executable UML realization. Executable UML is a computationally complete abstract software specification or modeling language.

Movisa's Client Data Model contains modeling elements dedicated to configure communication parameters. Basically, these parameters depend on concrete data server specifications. The Client Data Model therefore provides an adapter-like architecture: An adapter for each data server specification allows fine-grained parameterization so that reliable communication relationships with any automation specific data server can be ensured. A common information model provides these data to the elements of the Algorithm Model and to the elements of the Presentation Model.

To illustrate Movisa's principles, we use the CAP3 model introduced in Section 3.1 and realize the Selectable Collection "Login" exemplarily on the concrete UI level. (This is the level of the Cameleon Reference Framework where Movisa resides; it has been discussed in Section 2.) Therefore, the Presentation Model defines a UI component Drop Down. This component has an appropriate Animation Property that is responsible to fill up the Drop Down component with available options from the "User database". An additional requirement may be not to show up the user name "Administrator". This can be realized with an appropriate set of actions -defined within the Algorithm Model -dedicated to remove this particular user name from the data set. Finally, the Client Data Model defines the required data items and the particular data server from which the data will be fetched.

In order to define an effective approach for the problem described in Section 2, we consider the following three perspectives: (1) A User Perspective examines the different users and roles involved in the UI development process; (2) a Conceptual Perspective derives an appropriate concept from the tasks of the user perspective; and (3) a Technology Perspective introduces the concrete technical realization of the derived concepts.

User Perspective

Users involved in the UI development process are characterized by different responsibilities and tasks, different knowledge and different skills. They are therefore assigned one-time to one of the following user roles:

Interaction Designer/Information Architect is familiar with the standards concerning usability (e.g. [START_REF]Ergonomic requirements for office work with visual display terminals (VDTs) -Part 11: Guidance on usability[END_REF]), dialog design (e.g. [START_REF]Ergonomic requirements for office work with visual display terminals (VDTs) -Part 10: Dialog principles[END_REF]), and/or information representation (e.g. [START_REF]EN ISO 9241-8: Ergonomic requirements for office work with visual display terminals (VDTs) -Part 8: Requirements for displayed colours[END_REF]) -with respect to safety critical systems. All of these can be described as common knowledge of the interface design; it is captured by conceptual models such as CAP3. This user role is therefore responsible for creating and maintaining the abstract UI model CAP3. Domain Expert is an Application Engineer. She or he has advanced knowledge about the technical process to be monitored and operated by the visualization system. Furthermore, he or she is acquainted with the devices realizing the technical process and the relationships between them. Domain experts are additionally characterized by knowing the physical backgrounds, possible events and hazardous situations as well as the details on the product to be manufactured (e.g. product compounds). Movisa has been designed to capture specific knowledge of the automation domain.

As this user role specifies these implementation details, it is mainly responsible for creating and maintaining the concrete UI model Movisa. End User knows about the (manufacturing) process and the product to be manufactured. She or he also has knowledge of how to react to particular events. They are intended to work with the product of the user-centered design process. Therefore, users of this role are responsible for evaluating the UI prototypes.

Fig. 5 shows these user roles assigned to the particular development step (see Fig. 2) which the users are mainly responsible for.

There is, however, no clear border between the responsibilities of the participating user roles. Interaction designers might collaborate with domain experts at the CAP3 level. Domain experts might collaborate with interaction designers at the Movisa level. In any case, the users have deep knowledge about there belonging domain, but they are neither familiar with model transformations nor with details of the models itself. Therefore, they have to be supported appropriately by dedicated tools.

Conceptual Perspective

The basis we use for this Section is a CAP3 model initially developed by the responsible users (see Section 4.1). Starting from that, Fig. 2 points out the different kinds of transformation while Fig. 5 introduces particular user roles mainly responsible for each part CAP3 introduces an Update relationship, however it makes no assertions which property of the particular Movisa UI component should be updated under which condition.

Clearly, these ambiguities cause problems because one element can be mapped to multiple alternatives. Decisions regarding these mappings are always subject to project specific requirements or assumptions, company guidelines, or even personal taste. In the best case, there is only a single one-to-one mapping possible avoiding all ambiguities. Nevertheless, these decisions influence the transformation process -it needs to be customized based on them. Since Domain Experts, as the users that steer this development step, are not familiar with transformations (see Section 4.1), the following four different approaches can increase accessibility of these transformations: (1) A default mapping is used within an automatic transformation process. The domain expert then needs to customize the resulting Movisa model subsequently according to project specific requirements. (2) A separate Mapping Model can be used which captures the project specific mappings between elements. This concept has been introduced by the Human Computer Interaction (HCI) community, e.g. in [START_REF] Clerckx | The mapping problem back and forth: customizing dynamic models while preserving consistency[END_REF]. Sottet et al. [START_REF] Sottet | Mapping Model: A First Step to Ensure Usability for Sustaining User Interface Plasticity[END_REF] have defined a metamodel to specify mappings between different kinds of models. Elements of a source model can be connected to elements of a target model on both the metamodel and the model level. UsiXML [START_REF]UsiXML V1.8 Reference Manual[END_REF] defines a mapping model, too, which only provides mappings on the model level. According to Siikarla and Systa, transformations relying on this additional information are called semi-automatic since they require "the transformation engineer [to make] decisions that guide an [...] ambiguous automatic transformation" [START_REF] Siikarla | Decision reuse in an interactive model transformation[END_REF].

(3) The source model (CAP3) can be annotated in order to remove ambiguities in a semi-automatic transformation. For example, the Eclipse Modeling Framework (EMF) [START_REF] Steinberg | EMF Eclipse Modeling Framework[END_REF] provides an EAnnotation element which enriches arbitrary modeling elements with additional information. XML introduces a similar mechanism with the <annotation> element. In both cases, such mechanism can be used to specify mappings between elements of two models. (4) In contrast to the previous two approaches which require user interventions before the actual transformation, interactive transformations require the user to intervene only during the transformation process. We argue that the last (4) approach is most suitable, despite the others being used more often in existing solutions. Defining mappings on metamodel as well as on model level [START_REF] Calvary | A unifying reference framework for multi-target user interfaces[END_REF] or annotating a model (3) requires deep knowledge of the model's abstract syntax. It cannot be assumed that the responsible user roles have this knowledge (see Section 4.1). Interactive transformations (4), however, do not expect users to have deep knowledge neither of abstract syntax nor of the transformation itself. While the user will guide the transformations, the transformations also have to guide the user; the more precise the descriptions of ambiguities are the more powerful is the procedure.

Model Refactoring (). This development step uses a horizontal, endogenous transformation to modify or improve a model: Once the Movisa model has been derived by transformation, a manual post-treatment is necessary because information e.g. regarding an element's position or size are not part of the CAP3 model. Moreover, the role end-user comes into play: He or she evaluates the actual design and according to this she or he gives her or his feedback to the domain expert. It entails the following tasks:

(1) Improvements such as element's size or border color can simply be made using Movisa's model editor.

(2) Modifications such as changing a Radio Button Group to a Drop Down element while preserving the functional behavior is a more sophisticated task. It can be done using appropriate transformations. Kolovos et al. define them as update transformations in the small: They "are applied in a user-driven manner on model elements that have been explicitly selected by the user" [START_REF] Kolovos | Update transformations in the small with the epsilon wizard language[END_REF].

Model Synchronization (). This is the process to manage consistency between two or more models if one of these models has been altered: After the Movisa model has been modified due to end-user feedback, the CAP3 model has to reflect these modifications. As stated in Section 2, there are two classes of modifications, those which do not entail updates in the CAP3 model and those which do. For example, changing only the border color of a particular UI element in the Movisa model has no effect to the CAP3 model. Adding a further item to a Drop Down box requires an update of the CAP3 model. Otherwise, the consistency is violated. According to Nuseibeh et al. [START_REF] Nuseibeh | Making inconsistency respectable in software development[END_REF], inconsistencies can be tolerated as long as they will be fixed in a future point in time. Therefore, we do not artificially constrain the domain expert in improving a model and treat consistency management in a subsequent validation procedure which is explicitly driven by the user (see , Fig. 5). Nuseibeh et al. [START_REF] Nuseibeh | Making inconsistency respectable in software development[END_REF] describe model consistency management basically with the tasks (1) detecting inconsistencies, (2) characterizing inconsistencies, and (3) handling inconsistencies. Following that scheme, our approach for model synchronization is a two-staged process:

Intra-and inter-model validation: The models are consistent if both syntactical and semantical correctness is guaranteed. While syntactical correctness appears if the model is compliant to its metamodel, the process of ensuring semantical correctness is subject to a check against a set of appropriate rules. Basically, these rules define to what extent the Movisa model is allowed to alter to be consistent to the CAP3 model. Additionally, semantical correctness has to be proven not only within one model but also between them. Solving inconsistencies: If inconsistencies were identified by the preceding validation, members of the particular user role (see , Fig. 5) decide whether to roll back the corresponding modifications or to update the counterpart model. The latter option will be supported by user-driven update transformations. The system therefore suggests valid alternatives.

Traceability. Inter-model validation, as previously introduced, needs knowledge about which elements of both models are interrelated. It is desirable to be able to reproduce all previous decisions since models may contain hundreds of elements and therefore require hundreds of decisions. Thus, a transformation (, Fig. 5) performed for a second time -after all other transformations (, , and , Fig. 5) are executed -should automatically produce the model which was the result of a previously performed transformation (, Fig. 5). Czarnecki and Helsen [START_REF] Czarnecki | Classification of model transformation approaches[END_REF] point out that "[t]ransformations may record links between their source and target elements" [START_REF] Czarnecki | Classification of model transformation approaches[END_REF] which fosters synchronization between models. It also enables reproducibility of transformation results. They prefer to store these links separately which has been consolidated by Van Gorp who stated in [START_REF] Van Gorp | Traceability and Fine-Grained constraints in interactive inconsistency management[END_REF]: "[T]raceability links should be treated as first class software artifacts."

Technology Perspective

Fig. 6 shows that the Decision Trace Repository is the core of the entire approach. It feeds the refinement transformation () in order to make all steps reproducible. It also supports the process of model synchronization () by providing information about how the elements of the CAP3 model are linked to the elements of the Movisa model. For that purpose, a model dedicated to capture these relationships should be provided separately, as [START_REF] Czarnecki | Classification of model transformation approaches[END_REF] and [START_REF] Van Gorp | Traceability and Fine-Grained constraints in interactive inconsistency management[END_REF] suggest. Czarnecki and Helsen [START_REF] Czarnecki | Classification of model transformation approaches[END_REF] propose to add an additional GUID 2 to each model element (in our case elements of both the CAP3 and the Movisa model). The third model responsible for capturing the relationships between these models only establishes links between these GUIDs. Fig. 7 shows the metamodel of our User Decision Repository, further on named FleprMap. As Section 4.2 claims, the FleprMap only establishes a relationship between source and target elements. Therefore, a particular Mapping points to the respective elements with the Source and Target references. These references can be of any type as long as they are derived from an EClass 3 . Transformations guide the different users through the entire user interface development process. Mens et al. [START_REF] Mens | A taxonomy of model transformation[END_REF] distinguish mainly declarative and operational transformation mechanisms. While declarative approaches have their strengths in compactness and maintainability because they hide procedural details, operational approaches may unfold their advantages if incremental model updates are required [START_REF] Mens | A taxonomy of model transformation[END_REF]. Nevertheless, a suitable transformation approach has to deal with the following model management tasks (see Section 4.2): (1) transformation, (2) comparison, (3) validation, and (4) merging. There are different types of languages and transformation approaches each addressing only some of these tasks. For example, OCL 4 [START_REF]Object Constraint Language[END_REF] is only for model validation; openArchitectureWare [START_REF]openArchitectureWare Project Website[END_REF] captures besides validation only transformation. With its set of dedicated languages, the model management tool Epsilon [START_REF]Epsilon Project Website[END_REF][START_REF] Kolovos | Epsilon development tools[END_REF] addresses all of the tasks. Furthermore, the Epsilon languages combine declarative and operational approaches. A further advantage is that it is tightly integrated into the Eclipse [START_REF]Eclipse Website[END_REF] platform, since both the CAP3 and the Movisa editors are distributed as Eclipse plugins. Epsilon's transformations also allow for user interaction. We used (1) the Epsilon Transformation Language (ETL) to realize the interactive transformations (derived in Section 4.2) to be applied during model refinement (see , Fig. 2), whereas the concrete mapping options are implicitly contained in the transformations; (2) the Epsilon Wizard Language (EWL) to foster user-driven model refactoring (see , Fig. 2) which has been introduced in Section 4.2; and (3) the Epsilon Validation Language (EVL) to apply a set of predefined validation rules in order to achieve model synchronization (see , Fig. 2) mentioned in Section 4.2.

Finally, the result of our investigation is a prototypical implementation of the aforementioned concepts (see 4.2) in the form of suitable Eclipse plugins realized with the technology introduced in this Section.

Case Study

Our laboratory plant is equipped with three tanks and pipes, pumps, or valves, respectively, between them. Fuel level sensors ensure that the water in the tank does not spill over; a programmable controller enables monitoring and operating. The requirement is to provide a user interface for observing the automatic process as well as enabling manual operator intervention.

Fig. 8 depicts the Abstract UI modeled in CAP3 using the CAP3 model editor. The UI contains four Container elements: (1) a top level container "Overview" contains all other UI elements, (2) an "Interaction" container captures interaction elements, (3) the container "Fuel level map" defines elements for monitoring the process, and (4) a Repeated Conceptual Group "Fuel Level Trends" providing appropriate trend charts. CAP3's Domain Objects represent process variables. During a refinement transformation (see , Fig. 6) a first ambiguity to be resolved appears when detecting the Element "FuelLevel Tank 1" which is connected to a Domain Object via an Update relationship. The domain expert therefore has to decide which Movisa element is suitable to monitor a process variable and in which way it can be animated according to its current value. Fig. 9 illustrates this interactive task where the user chooses to create an Image element. The transformations store these decisions in the FleprMap, the mapping model. (If the user performs this transformation again, the transformations recognize that both models exist and that the links stored within the FleprMap are valid -in that case no user interaction is required.) Once all ambiguities are resolved, a complete and valid Movisa model is available. Fig. 10a shows the entire model using Epsilon's model tree view. The "Interaction" container, e.g., has been transformed to a Simple Container containing two Buttons in order to "Start" and "Stop" an inter-tank transfer process and two Drop Down elements dedicated to choose the source and the target tank. Fig. 10b shows the Drop Down elements in the Movisa model editor.

An evaluation points out that Drop Down elements are not suitable. Therefore, the domain expert invokes an appropriate EWL script directly within the model editor (see 10c shows the resulting interaction elements. The first Drop Down element was refactored to a set of Text Labels, the second one to a Radio Button Group. A subsequent model validation, as part of a Model Synchronization process (see , Fig. 6), points out that the Text Label alternative has led to semantical incorrectness (see Fig. 11a). The reason is that the CAP3 model defines the Tool "Start pump" to initialize an inter-tank transfer process as an atomic task -the end-user has to set source and target tank before the particular process variables will be updated5 using the Tool. Thanks to the FleprMap and to the validation rules, expressed in Epsilon EVL, the individual inconsistency can be resolved (see Fig. 11b) using the same procedure as provided for the model refinement process -clarify ambiguities interactively. The first step after selecting the Quick Fix (see Fig. 11b) is deciding whether to roll back the recent modifications or to update the CAP3 model. In this case study, the user chooses the latter option. Fig. 12 highlights the relevant parts of the updated CAP3 model introduced in Fig. 8. Since the "Source Tank Selection" is now realized by a set of Text Labels, the user decides to let the particular Text Label update the process variable immediately after clicking it. (It does not need to be an atomic task.) This can be achieved using an appropriate interaction property defined in Movisa's metamodel (see Section 3.2). A set of transformations update both the Movisa model by adding interaction properties to the Text Label components and the CAP3 model by moving the origin of the Update relationship from the Tool "Start pump" to the Tool "Source" (shape in Fig. 12). Since the Tool "Start pump" does not depend on the "Source Tank Selection" anymore, the transformations remove the Use relationship between the "Start Pump" Tool and the Selectable Collection (see shape in Fig. 12 compared to the same area of Fig. 8). Finally, these transformations update the FleprMap according to the latest modifications in the CAP3 model. All three models -the CAP3 model, the Movisa model, and the FleprMap -are consistent and semantically correct; together they describe a User Interface which is appreciated by end-users.

Related Work

Model-based UI development is most completely addressed by UsiXML [START_REF]UsiXML V1.8 Reference Manual[END_REF], a sophisticated XML-based User Interface Description Language. It fosters the MBUID process at each level of abstraction by defining appropriate models and providing tools to reify abstract models. Moreover, the tool ReversiXML, proposed by Vanderdonckt et al. [START_REF] Vanderdonckt | UsiXML: a user interface description language for specifying multimodal user interfaces[END_REF], can automatically reverse engineer web pages into UsiXML's CUI and AUI models. Another tool for UI Reverse Engineering is VAQUITA proposed by Bouillon et al. [START_REF] Bouillon | Flexible re-engineering of web sites[END_REF]. With it, one can create an XIML [START_REF] Puerta | XIML: a common representation for interaction data[END_REF] Presentation Model from any web site. For that purpose, VAQUITA lets developers participate in the engineering process. The purpose of both tools is the migration of an existing UI to another context of use. Beyond that, Stroulia et al. [START_REF] Stroulia | User interface reverse engineering in support of interface migration to the web[END_REF] as well as Ramón et al. [START_REF] Sánchez Ramón | Model-driven reverse engineering of legacy graphical user interfaces[END_REF] use reverse engineering methods to extract the knowledge included in legacy UIs into models for further treatment. The presented approaches have in common, that they always create the more abstract UI model from scratch. However, aspects of Model Synchronization are desirable, too: Information that is encapsulated in abstract models but not part of concrete models needs to be preserved.

Model synchronization, also referred to as Model Inconsistency Management, is addressed by xlinkit [START_REF] Nuseibeh | ViewPoints: meaningful relationships are difficult[END_REF] which uses XLink [START_REF]XML Linking Language (XLink) Version 1.1[END_REF] to establish relations between elements of XML based models. CAViT [START_REF] Van | CAViT: a consistency maintenance framework based on visual model transformation and transformation contracts[END_REF] is a consistency maintenance framework that binds particular transformation rules to OCL invariants. OCL checks if consistency constraints are satisfied; the particular transformation rule can resolve a possibly identified inconsistency. While the former approach requires XML based models, the latter one relies on OCL. Both prerequisites are not addressed by Eclipse based approaches.

Conclusion & Future Work

We investigated to what extend the User-centered Design process can be combined with the Model-based User Interface Development process. A prototype Eclipse implementation using different types of (interactive) transformation and intra-and inter-model validation in combination with a model dedicated to trace decisions, proves our approach to be feasible. It provides an iterative development procedure where modifications can take place at each level of abstraction defined by MBUID. A subsequent model synchronization ensures consistency during this process. Since the methods introduced in this paper enable a forward engineering as well as a reverse engineering, our approach provides a Round Trip Engineering in MBUID. While Clerckx et al. [START_REF] Clerckx | The mapping problem back and forth: customizing dynamic models while preserving consistency[END_REF] state that "[t]he aggregation of all of the abstract and concrete models is called the interface model", we have also to assign our FleprMap model to be that part of the interface model which ensures consistency.

Future work should be dedicated in a first instance to the usability during the development process. For example, the transformation environment should highlight the particular element if ambiguities have been identified, so that users can reason about the element's context in order to make the right decision. Future work should also be dedicated to maintainability of the transformation environment. The validation rules, for example, could be made explicit with the following consequences: (1) The transformation environment will be more generic and (2) the validation rules can simply be enhanced without knowledge of the transformation itself.

Fig. 1 :

 1 Fig. 1: Overview of the User-centered Design procedure.

Fig. 2 :

 2 Fig. 2: Overview of the different types of transformation.

Fig. 3 :

 3 Fig. 3: CAP3 login window using (a) the basic CAP UI components; (b) richer CAP UI components suggesting possible options, and (c) UI components specifying behavior and relations to the domain model.

1 Fig. 4 :

 14 Fig.4: Excerpt of Movisa's metamodel, the relevant part for this paper is the Presentation Model, which defines UI components.

Fig. 5 :

 5 Fig. 5: Different types of users are involved in the UI development process at different development steps.

Fig. 6 :

 6 Fig. 6: The overall concept showing details about each development step -Model Refinement, Model Refactoring, and Model Synchronization -using the transformations defined in Fig. 2. It also shows in what extend the users introduced in Fig. 5 are involved in the procedure.

Fig. 7 :

 7 Fig. 7: Simplified metamodel of the FleprMap, our Decision Trace Repository.

Fig. 8 :

 8 Fig. 8: Excerpt of the CAP3 model created in its dedicated model editor.

Fig. 9 :

 9 Fig. 9: Model Refinement: (a) notifies the user about ambiguities to be resolved; (b) suggests possible options.

Fig

  Fig.10b) -this invokes a refactoring transformation. First, the user chooses the element to be created using the same procedure as provided for the model refinement process (see Fig.9b). A transformation (1) creates this new model element, (2) adds it to the right position within the Movisa model, (3) ports all properties of the old element to the new one, (4) deletes the old element, and (5) updates the FleprMap.

Fig. 10 :

 10 Fig. 10: Model Refactoring: (a) shows a compact tree view of the Movisa model after the refinement process; (b) shows an excerpt of Movisa's model editor where a user starts to refactor it; (c) depicts the result of the refactoring process.

Fig.

  Fig.10cshows the resulting interaction elements. The first Drop Down element was refactored to a set of Text Labels, the second one to a Radio Button Group. A subsequent model validation, as part of a Model Synchronization process (see , Fig.6), points out that the Text Label alternative has led to semantical incorrectness (see Fig.11a). The reason is that the CAP3 model defines the Tool "Start pump" to initialize an inter-tank

Fig. 11 :

 11 Fig. 11: Problems View of the Eclipse tool: The validator has recognized semantical incorrectness between the CAP3 model and the altered Movisa model (a) and provides means to fix it (b).

Fig. 12 :

 12 Fig. 12: Excerpt of the CAP3 model after Model Synchronizing.

A Selectable Collection can also be represented by a Collection and a separate (nested) Selection Tool.

Globally Unique Identifier (GUID).

In industrial automation intervention in the process often requires to modify several process variables as an atomic task.

Acknowledgments

The authors would like to thank Henning Hager and Pieter Van Gorp for their contribution to this paper in the form of fruitful discussions. Part of this work is supported by the FWO project G.0296.08 and by the ESF project 080904707 (together with the Free State of Saxony).