Mobile Query Interfaces

Transcription

1 Mobile Query Interfaces Matthew Krog Abstract There are numerous alternatives to the application-oriented mobile interfaces. Since users use their mobile devices to manage personal information, a PIM interface is a good solution to implement on a handheld device. Most PIM systems exist as desktop systems and need to be adapted using some heuristic or guidelines before they are suitable for mobile devices. One of the key elements of any PIM system is the query handling. The most natural user search behaviour is orienteering, which involves refining ones search through a series of iterative query steps until the target result is reached. Four PIM query systems are surveyed and it is suggested that the Feldspar and LiFiDeA be combined, adapted and implemented as a mobile query interface. 1. Introduction Most mobile interfaces today such as those found on iphone and Android smartphones are application oriented [9]. Users open different applications to perform different tasks. For example a user will open an application to send and receive s and a music application to listen to music. This application design is central to many of the very successful desktop systems and likely one of the main reasons for its adoption into so many mobile interfaces today. between mobile devices and a desktop system, which raises the question of whether app-oriented interfaces are indeed best suited for mobile platforms, or whether an alternative approach may actually be better [6][11]. There are a number of alternative systems that exist such as Life-Streams [7], Haystacks [12] and Stuff I ve Seen (SIS) [6]. Many do away completely with the app-oriented and hierarchical file systems of the popular desktop interfaces. Instead they rely on powerful query systems that allow the user to retrieve and organise files. Most of the alternatives are desktop solutions but lend them-selves to being effective mobile solutions too. One of the challenges in porting such a system to a mobile platform is designing an effective and user-friendly query system that takes into consideration, the limitations and constraints of handheld devices. Section 2, discusses how mobile devices are well-suited to these alternative systems and what constraints need to be considered when designing an interface for such systems. Section 3, considers how users currently perform search, in particular what strategies they use and behaviours they exhibit when retrieving information. Finally section 4, describes a number of alternative query systems and examines what makes them effective in a mobile context. These application-based interfaces have been well received thus far [9]. However, there are many inherent differences 1

2 2. Mobile Interface Design 2.1 Personal information management (PIM) When designing a mobile interface it is important to consider how people use their mobile devices. It is crucial to determine what exactly the users needs are, what type of information they will be using and how they plan to interact with this information [8][2]. People use cell phones for making calls, sending messages, taking photos, listening to music, browsing the internet, playing games, getting GPS directions and even for organising their life with tools such as calendars, to-do lists and alarms [9][17]. The information that is accessed on a mobile is therefore quite personal to the user of the device. An ideal mobile interface is therefore one that lends itself to personal information management (PIM). PIM interfaces for mobile devices are not widespread and must be developed by adapting concepts from already existing PIM desktop interfaces such as Life-Streams [7] or Stuff I ve Seen [6]. The information being accessed (i.e. being queried) by the user in a PIM system is familiar to the user. This is important to consider, as it will greatly affect the way the way a user searches for information and thus the look and feel of the query system. For example a query system for mobile web search will have completely different requirements to that for searching through personal information [4][5]. In the case of web search, the user is generally unfamiliar with the results being displayed in the result set and the query system needs to provide more information describing each result so that the user can select the best match. When searching for personal information the user normally has background knowledge about the item they are looking for (such as related items, date, etc). An effective query system can exploit this extra information to assist the user in finding the correct result [6][5]. 2.2 Designing UIs for Mobile Devices Due to the nature of mobile devices being small and portable, designing interfaces for them brings forth many new challenges and limitations. Things such as small screen size, low resolution, an always-changing context, smaller amounts of user attention and inefficient data entry that normally do not need to be considered in desktop systems now become very restricting [8][2]. A perfect example of this are static user interface elements such as scroll bars that make desktop systems easy to use, which are not viable in a mobile environment due to limited screen real-estate [13]. There are extensive guidelines available for interface design in general. However, recommendations for mobile interfaces are scarce [8]. It is of value to follow these suggestions outlined in [8] when designing a new mobile interface. The Slide-Film interface is a new concept for mobile web search that abolishes scrolling and increases the view-ability of search result summaries [15]. It was developed in response to a problem with scrolling on small screens, which made users lose a sense of overall context. While there were no significant speed improvements over traditional web-search solutions, users did report higher preference rates towards this system. As mentioned this is a type of web search, and thus the requirements will differ from that of a PIM query solution [4][5]. However, it serves to illustrate how one should go about designing an interface that takes into account mobile constraints highlighted above. 2

3 3. User Search Behaviours Two papers are of significant value in the investigation of user search habits, behaviours and preferences. They are Improved Search Engines and Navigation Preference in Personal Information Management [1] and The Perfect Search Engine is Not Enough [16]. 3.1 Navigation vs Search [1] This is covered in great detail by the first of the two papers. The reasons presented for people possibly favouring search over navigation is that search is more flexible and does not require users to remember location specific details but rather allows them to specify any attribute of the target item. However, this is not the case, It was found that users prefer to use navigation rather than search to perform information retrievals. In the majority of cases search is only used when users cannot remember the location of a file. Even improving the efficiency of the search engine seems to have no sway on user preferences. The following reasons are possible explanations for users preference towards navigation, - Consistency - Recognition over Recall - Procedural vs Declarative memory - Cognitive Automation - Location Metaphor It is reasonable to conclude then, that if the above design concepts are addressed by a search based query system then users may begin to start using search more often. It is important to note that in this paper they tested the search features of desktop systems (Macintosh and Windows desktop searches). In these systems, desktop search is still a relatively new concept (especially in terms of file organisation) and considered as a minor feature. Folders have been around much longer and users are thus generally more comfortable with this concept. It is not clear how users will react to search if it is to be made a central theme in a mobile interface. It is also important to note that hybrid systems that combine search and navigation (this includes most PIM systems) are not investigated in [1]. 3.2 Orienteering and Teleporting [16] The second paper identifies two searching strategies, orienteering and teleporting, that users employ in achieving their search needs. In PIM systems, often the user knows what they are searching for (for example phone number, address or a particular photo). Teleporting is the process of jumping directly to the desired target using a single search query. Orienteering on the other hand, is refining search results through a series of query steps, until the desired target is reached. Orienteering seems to be the most natural way users find information. In fact, even when the search target is known in advance, a keyword search is only performed 39% of the time. The advantages of orienteering are that it 1. Decreases cognitive load. 2. Allows users to maintain sense of location. 3. Provides a better understanding of search results. From these advantages it can be seen that orienteering search can address some of the design factors that make navigation so attractive (e.g. Recognition over Recall, Location metaphor). From this it can be considered that a mobile interface based 3

4 around an orienteering search mechanism may yet be viable. 4. Query Systems 4.1 LiFiDeA Semantic Representation [11] Since all items have a text representation, the user is able to perform a key word search to find a document. Once a document has been selected, the user is able to browse through related documents using tags and links. Tags are used to group a set of documents together. Links are used to associate two related documents. Studies have shown that users are not willing to annotate files except for assigning simple tags [14]. Thus links between documents are built automatically and their weights calculated from user click activity. Results Display The results for each document are those files that are related to it. Associative browsing can be achieved by clicking on the links. LiFiDeA displays links in a list ranked according to their relative weights. Displaying links in this way removes the ability to see the nature of the relationship, fortunately it turns out this is not necessary for associative browsing [14]. Mobile Implementation Representation of linked documents in terms of a list is suited to the mobile environment. As specified in earlier sections, the query system for a mobile PIM system should be orienteering. Evaluation Good at finding known items. System can learn to predict associations with small amount of click data. 4.2 FELDSPAR [3] Feldspar is a desktop query system that uses associative retrieval of personal information [3]. Feldspar takes the philosophy that people remember things by association and uses an orienteering approach to retrieve information. Users are able to specify multiple queries to refine their search. Data Structure Results can be filtered using eight different data types; , person, folder, file, webpage, date, event and other. The file and folder types allow the query system to run if necessary on an underlying file system that supports hierarchical files and folders. These types can always be removed if the underlying file system is a single unified index such as that used in Life-Streams or SIS. They can also be changed to support other mobile types such as text messaging and calls. Results Display All results are displayed in a list that can be sorted by the column headings. Support is provided for direct search as the result set can be filtered using a keyword search at anytime. Mobile Implementation Developing a mobile interface of this system may be challenging, as there is a lot to display on a small screen. Unique Features Queries are built up as sentences. Linking phrases such as relates to and where is makes query creation intuitive and easy. Predictive suggestions for next filter value. Evaluation Users enjoyed utilising the interface, finding it easy to learn. Tasks assigned during testing were completed faster and with fewer errors. 4.3 FaThumb [10] A faceted approach to searching large data sets in a mobile environment. The layout of the interface is mapped to the keypad allowing users of non-touch 4

5 screen phones to use the system. Each key is mapped to one function. Facets are extracted from data sets and used to filter results. User can select up to 9 different facets per filtration level. Results displayed A maximum of 9 results displayed per page. User selects chosen file using keys 1 to 9. Results can be sorted by a particular attribute. Mobile implementation This system is designed to work on mobile device. Unique Features Regularly used queries can be saved, allowing faster access in the future. Evaluation The system was compared to that of a general text entry search system (direct search). It performed remarkably better when the target item the user was searching for was unknown. However, when the target was know the text entry (direct search) was superior. 4.4 Stuff I ve Seen (SIS) [6] The SIS system follows the philosophy that most information that a user accesses has already been accessed before. In other words, the user essentially revisits previously seen files most of the time. It does not support hierarchical folder structures instead it creates its own single unified index of all previously seen files. The queries return results that are rich in contextual cues such as previews ad descriptions. Since the user has seen these files before, the result set can effectively be skimmed for relevant information. The query system enables iterative filtering (orienteering). Results displayed Results include a preview and normally show the first 300 characters describing a particular file. This can be turned off to allow for more search results to be displayed. Results can also be sorted by date or rank. There are two main views. Side view provides a simplified interface whereas top view is more flexible and allows sophisticated filtering. Mobile Implementation Small mobile screen is not ideal for displaying this kind of interface. 5

6 4.5 Summary Table 1. Showing characteristics of 4 PIM Query systems Query Type Data Structures Unique Features Current Platform LiFiDeA FELDSPAR FaThumb SIS Teleporting Orienteering Orienteering Orienteering Adapted to any Tags & Links Adapted to any Sentence Creation Query Depends on Dataset, 9 filters per level Faceted, Allows saving of regular queries Requires a single unified index Iterative Query Desktop Desktop Mobile Desktop 5. Conclusion Its been seen that a PIM user-interface is well suited as a mobile system. An orienteering query system for the mobile is crucial as this is the most natural way for users to search through personal information. Four PIM query systems were examined; While LiFiDeA does not provide orienteering search support it does provide a unique way for browsing related documents (using links). This system could perhaps be incorporated with Feldspar, one of the orienteering search interfaces. Feldspar is very flexible and user friendly and would combine well with LiFeDeA providing unique search and navigation as hybrid interface. The challenge lies in adapting these systems from their desktop versions so that they can run seamlessly on a mobile device. References [1] Bergman, O. et al., (2008). Improved Search Engines and Navigation Preference in Personal Information Management, ACM Transactions on Information Systems (TOIS), 26 (4), Article 20, September [2] Boonlit, A., & Dongsong, Z., (2005). Interface Design for Mobile Applications, AMCIS 2005 Proceedings. Paper 494. Retreived from [3] Chau, D.H. et al., (2008). What to Do When Search Fails: Finding Information by Association, CHI '08 Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, p , ACM New York, NY, USA, [4] Chen, J., et al, (2009). imecho: An Associative Memory Based Desktop Search System, CIKM '09 Proceeding of the 18th ACM conference on Information and knowledge management, p , ACM New York, NY, USA, [5] Cutrell, E., (2006), Searching to eliminate personal information management, Communications of the ACM - Personal information management, 49 (1), January 2006, ACM New York, NY, USA. [6] Dumais, S., (2003). Stuff I ve Seen: A System for Personal Information Retrieval and Re-Use, SIGIR '03 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, p , ACM New York, NY, USA, [7] Freeman, E., & Gelernter, D. (1996). Lifestreams: a storage model for personal data. ACM SIGMOD Record, 25 (1), March

7 [8] Gong, J., & Tarasewich, P. (2004). Guidelines for handheld mobile device interface design. Proceedings of DSI 2004 Annual Meeting. In Proceedings of the 2004 DSI Annual Meeting. Retrieved from doi= conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology, September 10-12, 2007, Singapore. [9] Hall, S., & Anderson, E. (2009). Operating Systems for Mobile Computing. Journal of Computing Sciences in Colleges, 25 (2), [10]. Karlson, A., et al, (2006). FaThumb: a facet-based interface for mobile search, CHI '06 Proceedings of the SIGCHI conference on Human Factors in computing systems, p , ACM New York, NY, USA, [11]. Kim, J. et al, (2010). Building a Semantic Representation for Personal Information, CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management, p , ACM New York, NY, USA, 2010 [12]. Krager, D.R., Quan, D., (2004). Haystack: a user interface for creating, browsing, and organizing arbitrary semi-structured information, CHI '04 extended abstracts on Human factors in computing systems, p , ACM New York, NY, USA, [13] Landay, J., & Kaufmann, T. (1993). User interface issues in mobile computing. Workstation Operating Systems, Proceedings., Fourth Workshop on, [14] Sauermann, L., & Heim, D., (2008), Evaluating Long-Term Use of the Gnowsis Semantic Desktop for PIM, The Semantic Web - ISWC 2008, p , 2008, Springer Berlin/Heidelberg. [15] Shtykh, R.Y., & Jin, Q. (2008). Improving Mobile Web Search Experience with Slide-Film Interface. Signal Image Technology and Internet Based Systems, SITIS '08. IEEE International Conference. pp , Nov Dec [16] Teevan, et al., (2004). The perfect search engine is not enough: a study of orienteering behavior in directed search, Proceedings of the SIGCHI conference on Human factors in computing systems, p , April 24-29, Vienna, Austria, [17] Viljamaa,T., & Vaittinen, T., (2007). Unified media UI structure and design for mobile devices, Proceedings of the 4th international 7