Agnes Koschmider, Judith Michael, Bernhard Thalheim (Hrsg.): EMISA Workshop 2020 CEUR-WS.org Proceedings 25 Towards Intelligent Personal Task and Time Management: Requirements and Opportunities for Advanced To-do Lists Michael Fellmann1, Fabienne Lambusch,2 and Maria Dehne3 Abstract: Today’s working world can be characterized by an increase in flexibility, complexity and speed. For employees, it is challenging to keep pace to dynamic professional requirements and to constantly collect and prioritize necessary tasks in order to stay well-organized. While there is a plethora of IT-supported to-do lists that help to remember important or necessary tasks, these lists are predominantly rather simple and provide only little support for managing work and life. Hence in our paper, we focus on advanced approaches for personal task and time management via improved IT-supported to-do lists. Such lists could proactively support the user by (i) collecting and prioritiz- ing tasks, (ii) providing context-sensitive reminders and (iii) tracking activities in order to provide insights regarding progress, productivity and health-related aspects that in sum could be considered as “intelligent”. Towards the IT-supported realization of such lists, we collect initial requirements by analyzing existing and upcoming tools as well interviews we conducted about work organization with professionals in the IT-domain. Based on this, we provide an integrated requirements catalogue and comment on opportunities for further research. Keywords: Task and Time Management, To-do Lists, Assistance Systems. 1 Motivation The digital transformation has rapidly changed and continues to change the world of work tremendously. On the bright side, improved work flexibility [SCT12] in terms of content, time and location provides employees with additional autonomy with regard to how they do their jobs. On the dark side, work can be characterized by high complexity, time-pres- sure, constant interruptions and multi-tasking as well as work-intensification that is ongo- ing over decades [GM01]. In sum, employees face growing challenges upon managing their work and keeping track of relevant tasks as well as managing progress, productivity and health. Therefore, it is of vital importance to equip employees with powerful tools in order to tackle these challenges and be successful. In this regard, it can be observed that in daily work, many activities or projects typically involve a series of tasks, people, dead- lines and locations. No matter how big or small these projects are, success is always largely dependent on the organizing skills of the people involved. This is still a big challenge that 1 University of Rostock, Institute of Computer Science, Rostock, 18059, michael.fellmann@uni-rostock.de, https://orcid.org/0000-0003-0593-4956 2 University of Rostock, Institute of Computer Science, Rostock, 18059, fabienne.lambusch@uni-rostock.de, https://orcid.org/0000-0002-0303-1430 3 University of Rostock, Institute of Computer Science, Rostock, 18059, maria.dehne@uni-rostock.de Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 26 Michael Fellmann, Fabienne Lambusch, Maria Dehne often is mastered with the help of simple means such as paper-based to-do lists, or note- pads, despite the many technical possibilities. However, these methods are time-consum- ing and also not always effective, since e.g. reminders are missing. In spite of a plethora of applications on the market that are designed to provide better time and task manage- ment, most of them are rather a digitalized version of paper-based to-do lists or notebooks and lack intelligent features such as context-sensitive reminders. Context-sensitive means that, for example, reminders of important tasks such as project planning do not appear when the user is attending a meeting or that reminders appear only on pre-determined locations or situations. While some to-do lists already provide such features, a more so- phisticated approach should consider the task context as well. With this, using an email or calendar application could trigger another task context and thus different reminders than e.g. working with an Integrated Programming Environment (IDE). State-of-the-art tools are largely unable to learn or draw logical conclusions that would be needed for such be- haviors. A further example illustrating this deficiency is booking a conference trip that takes place for several days abroad. This usually includes booking a hotel and means of transport. However, state-of-the-art to-do list tools usually cannot infer this even if such a behavior occurred frequently in the past sequences of user actions. In sum, intelligent to- do lists would relieve the user by automatically collecting and resubmitting tasks, while recognizing priorities, scopes of tasks, and deadlines. Beyond that, they could additionally assist the user in tracking activities in order to provide insights regarding progress, produc- tivity and health-related aspects. In this regard, they could e.g. suggest tasks implying concentrated and complex work when the user is at its daily performance peak or could remind the user to take a break. Finally, an intelligent to-do list should provide integration with established software like Outlook and fitness trackers. To sum up, support for per- sonal task and time management with intelligent to-do lists is highly relevant still today. Despite this relevance, requirements for intelligent to-do lists are still an under-researched topic which we address with our preliminary contribution. To do so, we elicit requirements from literature, existing tools and interviews and compile them into a preliminary require- ments catalogue. 2 Background Increasing the degree of automation for to-do lists is a great challenge. Although they are a popular tool for managing personal information, unfortunately they do not yet act ac- cording to user behavior. Furthermore, entries are currently only written in free text, from which the system cannot derive any useful information [GR08]. In this way, GIL and RATNAKAR emphasize the capability of to-do list systems to extract details from the user’s free text and create a task [GR08]. An early approach in this direction is the concept of RHAICAL [FM05]. Moreover, once a task (e.g. visiting a conference) has been recognized, advanced approaches try to create action plans for tasks (e.g. book hotel, book transporta- tion) [Ko13]. One important problem here is that systems would need to have “common sense” or domain knowledge. An example for the former would be that the systems knows how long a project status meeting usually lasts. An example for the latter would be that it Intelligent To-do List 27 should know when people usually have dinner or how long a dinner usually lasts. It could even imply to draw logical conclusions, such as not inviting a vegetarian to a dinner in a steakhouse. The need of learning “common sense” knowledge and acting accordingly to save the user time when inputting data has already been put forth by [Mu00]. However, in order to provide an effective support in personal task and time management, also user preferences are important and could complement “common sense” knowledge. This has already been acknowledged by Berry et al. [Be06] and is explored more recently by GEETHA et al. [GAK18]. In this context, it is stated that the biggest time management problem is purely personal. Every person, especially very busy workers, have different background preferences regarding the calendar. This includes e.g. priorities and times of tasks, but also to what extent these tasks are shared with others. In this direction, PTIME was developed as one of the first applications that memorized and learned the preferences of the user [Be06]. More recently, the INTELLIGENT DAILY SCHEDULER was developed which automatically generates free time slots for upcoming tasks from the free time of the personal calendar and learns by repetitions [GAK18]. While it is important to recognize to-dos and make plans, it is equally important to remind the user if he or she is unaware of upcoming tasks or appointments. However, already two decades ago, studies have shown that many users have a problem with their reminders because they appear at inap- propriate times. This led to the observation that context-information is needed for the gen- eration of adequate reminders [DA00]. Regarding timing for reminders, much can be learned from the stream of research concerned with timing for work interruptions, see e.g. [Ri17] for a literature review. Regarding location-aware reminders, current approaches try to additionally infer the correct location for task reminders [SMO18]. To summarize, there are ongoing developments in regard to to-do list item creation, task planning and context- sensitive reminders. In spite of this, statements about requirements are scattered among these works and also do not consider two important aspects. First, they do not investigate what current tools developed outside scientific research offer the user in response to (pre- sumed) market demands. Second, they do not contain empirical statements about what employees consider as important features. Our contribution hence lies in addressing this gap by summarizing requirements found in literature and derived from state-of-the-art tools and interviews with employees. Our requirements are then compiled into a prelimi- nary requirements catalogue. 3 Requirement Elicitation 3.1 Sources for Requirement Derivation and Procedures Requirements were collected using two different methods. First, literature and tools were studied. Since some tools have not yet been described in scientific papers, we opted against separating requirements from scientific papers and those identified by inspecting tool de- scriptions. Searching for literature and tools was accomplished using various web search engines with combinations of keywords such as “artificial intelligence”, “time manage- ment”, “task management”, “calendar tool”, and “time tracking”. For the identification of 28 Michael Fellmann, Fabienne Lambusch, Maria Dehne state-of-the-art tools, we used one major product weblog where innovative products are announced, namely on PRODUCTHUNT. Second, we conducted semi-structured in-depth interviews. An interview guideline was prepared in advance and followed during the in- terview. In the first part of the interview, partners were asked questions about their current methods for time and task management. They were then asked whether they could imagine using applications that solve such tasks in an intelligent way and what functions these applications should have (the term “intelligent” was clarified beforehand). Since the inter- viewees should also consider visionary future technologies and not only focus on the state- of-the-art, the next part asked for functions of such systems that could be developed in the next 20 to 30 years. At the end of the interview, the interviewed persons prioritized the functions collected in part 2. A total of four people with a background in IT-industry took part. A fifth participant served as a pretest. However, since the results of this pre-test were also helpful for the evaluation, it was also included in the overall evaluation of the inter- views. The evaluation of the interviews was based on MAYRING [MF14] using the soft- ware MAXQDA to support the interpretation and coding process. Finally, a consolidated requirements model was created (see Section 3.2). 3.2 Consolidated Preliminary Requirements Model The consolidated requirements catalogue has been developed based on all requirements identified using the sources and procedures described in the section before. This involved a process of consolidation, clustering and ordering of the requirements. The final catalogue is presented in the form of a mind map (cf. Fig. 1). It moreover indicates the source as well as the frequency range of elicited requirements per category. Fig. 1: Requirements catalogue for IT-supported “intelligent” to-do lists Intelligent To-do List 29 Requirements fall into five broad categories: Task Management, Tracking, Reminder, Preference Management and Cross-cutting Requirements. In more detail, Task Manage- ment contains requirements regarding the creation of tasks and work support. The former mainly comprises “intelligent” assistance for the creation of tasks based on textual de- scriptions, e-mails or from voice messages as well as classification of tasks according to pre-defined categories and prioritization. The latter comprises requirements for working with the to-do list such as recommendations for the next best action, sharing to-dos with colleagues and receiving predictions for the time needed to physically change the location that e.g. depends on the transportation means and traffic, which some of the advanced tools already provide. Since the to-do list should adapt to the context, Tracking is required. Here, location, mood and other tracking data (e.g. time-use or physiological data) have been elicited. Tracking such data can be used for more adequate Reminders that could be context-based, location-based or mood-based. While location- and mood-based reminders simply take the users’ GPS position and emotional state into account, context-based re- minders could be adaptive to the current situation in complex ways, e.g. considering what the user has done before, what the user could do now and what the goals of the user are. In regard to Preference Management, the system should be able to learn preferred timeslots or locations for engaging in to-dos based on previous data (e.g. no appointments on early Monday morning) as well as provide the possibility for various user-defined set- tings. Finally, in regard to Cross-cutting Requirements, the system should be capable to leverage background knowledge such as preferences of co-workers (e.g. working times, diet preferences for meetings) or common-sense knowledge (e.g. public holidays, average speed of transportation means) and should be simple to use and accessible from every- where, which could be accomplished via cloud-based access. 3.3 Discussion Regarding requirements elicitation from literature, despite the large amount of popular guidebook literature, surprisingly little works are available on the precise topic of IT-sup- ported personal task und time management, and even more so in regard to to-do lists. In addition, found literature mainly offered descriptions of developed tools from which re- quirements had to be derived since they were not explicitly mentioned. As a further limi- tation of our research, we focused on functional requirements and some cross-cutting as- pects, leaving non-functional requirements largely open for future research. Regarding tool analysis, PRODUCTHUNT was useful to get an overview of current tools on the market. Most of the functions however had to be derived from user comments or by downloading and testing the tools, since often no in-depth documentation was available. Finally, regard- ing requirements elicitation with interviews, it was helpful that participants were invited to actively think about requirements of advanced future tools. A major limitation of the research in this direction is the number of five interviewees with IT-background which creates potentials for future research. 30 Michael Fellmann, Fabienne Lambusch, Maria Dehne 4 Conclusion and Research Opportunities Despite the fact that personal time and task management is one of the most important topics in work life, surprisingly little research is available so far regarding the require- ments for intelligent task and time management tools that could be embodied in “intelli- gent” to-do lists. Therefore, our work makes a contribution in this field, although our re- sults are very preliminary. However, we provide a preliminary overview of key require- ments of intelligent task and time management systems that support the user in the creation of to-dos and provide context-sensitive reminders or suggestions for relevant tasks. Future research opportunities lie in the interrelation of these requirements, e.g. context-sensitive reminders require in some form tracking the user. Further research opportunities lie in the selection, adjustment or adaptation, application and finally evaluation of research results of various sub-fields of Computer Science, Business Information Systems and Organiza- tional Psychology. In regard to task management, the question is how natural language processing for extraction of information from texts could be combined with other data (e.g. previous tasks performed on the day) to increase the accuracy of do-do item genera- tion. Moreover, psychological models could be used to explore the question how the or- dering of daily tasks may impact the individual, e.g. in terms of perceived progress or fatigue at the end of the work day. This could be relevant to optimize the ordering of to- dos. Likewise, the utility of physiological models of cognitive performance in relation to the time of day for task ordering could be studied. Regarding tracking, further questions would be to analyse the prospects and limitations of integrating work-related time track- ing data with more physiological tracking data into a combined approach. For example, heart rate variability (HRV) allows to detect stress, but the question is whether such data could be applied in task scheduling to avoid stressful working conditions. Regarding re- minders, the challenge is how to predict the acceptability and utility of a reminder that might interrupt the user. Extensive prior research on work interruptions can be leveraged on this aspect as well as machine learning techniques. 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