In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conducted in three parts: first, an initial survey provided broad insights into how calendars were used; second, this was followed up with personal interviews of a few participants which were transcribed and content-analyzed; and third, examples of calendar artifacts were collected to inform our analysis. Findings from our study include the use of multiple reminder alarms, the reliance on paper calendars even among regular users of electronic calendars, and wide use of calendars for reporting and life-archival purposes. We conclude the paper with a discussion of what these imply for designers of interactive calendar systems and future work in PIM research.
Jones @cite_33 framed the problems in PIM in terms of the canonical tasks of , , and . Keeping any kind of information involves a tradeoff between the likelihood it will be recalled in the future, and the costs of capturing and retaining it. Organizing involves filing it away such that it can be retrieved easily in the future, and while keeping the cost of organizing less than the cost of finding. Re-finding is a different problem from finding, since there are aspects to encountered information that make it personal.
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To keep or not to keep? People continually face variations of this decision as they encounter information. A large percentage of information encountered is clearly useless — junk e–mail, for example. Another portion of encountered information can be "used up" and disposed of in a single read — the weather report or a sports score, for example. That leaves a great deal of information in a middle ground. The information might be useful somewhere at sometime in the future. Decisions concerning whether and how to keep this information are an essential part of personal information management. Bad decisions either way can be costly. Information not kept or not kept properly may be unavailable later when it is needed. But keeping too much information can also be costly. The wrong information competes for attention and may obscure information more appropriate to the current task. These are the logical costs of a signal detection task. From this perspective, one approach in tool support is to try to decrease the costs of a false positive (keeping useless information) and a miss (not keeping useful information). But this reduction in the costs of keeping mistakes is likely to be bounded by fundamental limitations in the human ability to remember and to attend. A second approach suggested by the theory of signal detectability is relatively less explored: Develop tools that decrease the likelihood that "keeping" mistakes are made in the first place. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Scientific research performed with the involvement of the broader public (the crowd) attracts increasing attention from scientists and policy makers. A key premise is that project organizers may be able to draw on underused human resources to advance research at relatively low cost. Despite a growing number of examples, systematic research on the effort contributions volunteers are willing to make to crowd science projects is lacking. Analyzing data on seven different projects, we quantify the financial value volunteers can bring by comparing their unpaid contributions with counterfactual costs in traditional or online labor markets. The volume of total contributions is substantial, although some projects are much more successful in attracting effort than others. Moreover, contributions received by projects are very uneven across time—a tendency toward declining activity is interrupted by spikes typically resulting from outreach efforts or media attention. Analyzing user-level data, we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work. Although top contributor status is earned primarily through higher levels of effort, top contributors also tend to work faster. This speed advantage develops over multiple sessions, suggesting that it reflects learning rather than inherent differences in skills. Our findings inform recent discussions about potential benefits from crowd science, suggest that involving the crowd may be more effective for some kinds of projects than others, provide guidance for project managers, and raise important questions for future research. Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow. Traditionally users access their personal files mainly by usingfolder navigation. We evaluate whether recent improvements indesktop search have changed this fundamental aspect of PersonalInformation Management (PIM). We tested this in two studies usingthe same questionnaire: (a) The Windows Studya longitudinalcomparison of Google Desktop and Windows XP SearchCompanion, and (b) The Mac Studya large scale comparison of MacSpotlight and Sherlock. There were few effects forimproved search. First, regardless of search engine, there was astrong navigation preference: on average, users estimated that theyused navigation for 56-68 of file retrieval events but searchedfor only 4-15 of events. Second, the effect of improving thequality of the search engine on search usage was limited andinconsistent. Third, search was used mainly as a last resort whenusers could not remember file location. Finally, there was noevidence that using improved desktop search engines leads people tochange their filing habits to become less reliant on hierarchicalfile organization. We conclude by offering theoretical explanationsfor navigation preference, relating to differences between PIM andInternet retrieval, and suggest alternative design directions forPIM systems. Streaming media on the Internet has experienced rapid growth over the last few years and will continue to increase in importance as broadband technologies and authoring tools continue to improve. As the Internet becomes an increasingly popular alternative to traditional communications media, Internet streaming will become a significant component of many content providers' communications strategies. Internet streaming, however, poses significant challenges for content providers, since it has significant distribution problems. Scalability, quality, reliability, and cost are all issues that have to be addressed in a successful streaming media offering. Streaming content delivery networks (streaming CDNs) attempt to provide solutions to the bottlenecks encountered by streaming applications on the Internet. However, only a small number of them has been deployed, and little is known about the internal organization of these systems. In this paper, we discuss the design choices made during the evolution of Akamai's CDN for streaming media. In particular, we look at the design choices made to ensure the network's scalability, quality of delivered content, and reliability while keeping costs low. Performance studies conducted on the evolving system indicate that our design scores highly on all of the above categories. Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization task, resulting in quantifiable goodness measures and new visualization methods. The task is information retrieval given the visualization: to find similar data based on the similarities shown on the display. The fundamental tradeoff between precision and recall of information retrieval can then be quantified in visualizations as well. The user needs to give the relative cost of missing similar points vs. retrieving dissimilar points, after which the total cost can be measured. We then introduce a new method NeRV (neighbor retrieval visualizer) which produces an optimal visualization by minimizing the cost. We further derive a variant for supervised visualization; class information is taken rigorously into account when computing the similarity relationships. We show empirically that the unsupervised version outperforms existing unsupervised dimensionality reduction methods in the visualization task, and the supervised version outperforms existing supervised methods. User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional modeling and analysis of online activity and mobility for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on kind of neural-networks, called self-organizing maps (SOM), for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations. We study the problem of place recognition. Given a photo, we estimate its location by scene matching to a large database of internet photos of known locations. Traditional strategies, which involve a linear scan of the database to find matching scenes, fail to scale. On the other hand, internet photos contain a massive amount of noise and redundancy, which is of little help for place recognition. By exploiting the scene distribution of photos, we summarize the database by a set of canonical views. The set of canonical views eliminates the noise and redundancy in internet photos, and provides a compact representation for the database. By restricting scene matching to the set of canonical views, we observe a good tradeoff between efficiency and recall: the average processing time for a query photo is reduced by 97 , while the recall rate for place recognition remains at 75 . As smartphones increasingly pervade our daily lives, people are ever more interrupted by alerts and notifications. Using both correlational and experimental methods, we explored whether such interruptions might be causing inattention and hyperactivity-symptoms associated with Attention Deficit Hyperactivity Disorder (ADHD) even in people not clinically diagnosed with ADHD. We recruited a sample of 221 participants from the general population. For one week, participants were assigned to maximize phone interruptions by keeping notification alerts on and their phones within their reach sight. During another week, participants were assigned to minimize phone interruptions by keeping alerts off and their phones away. Participants reported higher levels of inattention and hyperactivity when alerts were on than when alerts were off. Higher levels of inattention in turn predicted lower productivity and psychological well-being. These findings highlight some of the costs of ubiquitous connectivity and suggest how people can reduce these costs simply by adjusting existing phone settings. One aspect in which retrieving named entities is different from retrieving documents is that the items to be retrieved - persons, locations, organizations - are only indirectly described by documents throughout the collection. Much work has been dedicated to finding references to named entities, in particular to the problems of named entity extraction and disambiguation. However, just as important for retrieval performance is how these snippets of text are combined to build named entity representations. We focus on the TREC expert search task where the goal is to identify people who are knowledgeable on a specific topic. Existing language modeling techniques for expert finding assume that terms and person entities are conditionally independent given a document. We present theoretical and experimental evidence that this simplifying assumption ignores information on how named entities relate to document content. To address this issue, we propose a new document representation which emphasizes text in proximity to entities and thus incorporates sequential information implicit in text. Our experiments demonstrate that the proposed model significantly improves retrieval performance. The main contribution of this work is an effective formal method for explicitly modeling the dependency between the named entities and terms which appear in a document. Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I've Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation. Persistent identification is necessary for recognition, dissemination and (external) cross-references to digital objects. Uniform Resource Identifiers (URIs) provide an established scheme for this task, but do not guarantee stable and persistent identification. In the context of (personal) archives, stability is needed when references are be stored on a medium where later changes to identifiers cannot be corrected at all or only with a very large overhead, such as WORM media or tape archives. Additionally, resources like contacts or appointments do not have a URI, while other URIs, such as file system paths or the IMAP URI, are unstable by design and cannot represent the dynamic aspects of Personal Information Management (PIM). This paper discusses problems of archiving that arise with entity identification in PIM, especially on the example of the personal file system. This paper reports a study of Personal Information Management (PIM), which advances research in two ways: (1) rather than focusing on one tool, we collected cross-tool data relating to file, email and web bookmark usage for each participant, and (2) we collected longitudinal data for a subset of the participants. We found that individuals employ a rich variety of strategies both within and across PIM tools, and we present new strategy classifications that reflect this behaviour. We discuss synergies and differences between tools that may be useful in guiding the design of tool integration. Our longitudinal data provides insight into how PIM behaviour evolves over time, and suggests how the supporting nature of PIM discourages reflection by users on their strategies. We discuss how the promotion of some reflection by tools and organizations may benefit users. In a paper in the November 1970 Communications of the ACM, V.Y. Lum introduced a technique of file indexing named combined indices. This technique permitted decreased retrieval time at the cost of increased storage space. This paper examines combined indices under conditions of file usage with different fractions of retrieval and update. Tradeoff curves are developed to show minimal cost of file usage by grouping various partially combined indices. In this paper, we study the utility of opportunistic communication systems with the co-existence of network infrastructure. We study how some important performance metrics change with varying degrees of infrastructure and mobile nodes willing to participate in the opportunistic forwarding. In doing so, we observe phase transitions in the utility of infrastructure and opportunistic forwarding respectively at different points in the design space. We discuss the implications that this has for the design of future network deployments and how this observation can be used to improve network performance, while keeping cost at a minimum. We present a new task that predicts future locations of people observed in first-person videos. Consider a first-person video stream continuously recorded by a wearable camera. Given a short clip of a person that is extracted from the complete stream, we aim to predict that person's location in future frames. To facilitate this future person localization ability, we make the following three key observations: a) First-person videos typically involve significant ego-motion which greatly affects the location of the target person in future frames; b) Scales of the target person act as a salient cue to estimate a perspective effect in first-person videos; c) First-person videos often capture people up-close, making it easier to leverage target poses (e.g., where they look) for predicting their future locations. We incorporate these three observations into a prediction framework with a multi-stream convolution-deconvolution architecture. Experimental results reveal our method to be effective on our new dataset as well as on a public social interaction dataset. How do people keep track of their money? In this paper we present a preliminary scoping study of how 14 individuals in the San Francisco Bay Area earn, save, spend and understand money and their personal and family finances. We describe the practices we developed for exploring the sensitive topic of money, and then discuss three sets of findings. The first is the emotional component of the relationship people have with their finances. Second, we discuss the tools and processes people used to keep track of their financial situation. Finally we discuss how people account for the unknown and unpredictable nature of the future through their financial decisions. We conclude by discussing the future of studies of money and finance in HCI, and reflect on the opportunities for improving tools to aid people in managing and planning their finances. The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures first-class citizens of the web, I explore the idea of Visipedia, a visual interface for Wikipedia that is able to answer visual queries and enables experts to contribute and organize visual knowledge. Five distinct groups of humans would interact through Visipedia: users, experts, editors, visual workers, and machine vision scientists. The latter would gradually build automata able to interpret images. I explore some of the technical challenges involved in making Visipedia happen. I argue that Visipedia will likely grow organically, combining state-of-the-art machine vision with human labor. Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the T EAM F ORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the T EAM F ORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.