This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.

As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures, including Ligra, Gemini, and GraphIt. We find that those systems suffer from severe cache miss penalty because of the irregular and uncoordinated memory accesses in processing FPPs. In this paper, we propose ForkGraph, a cache-efficient FPP processing system on multi-core architectures. In order to improve the cache reuse, we divide the graph into partitions each sized of LLC (last-level cache) capacity, and the queries in an FPP are buffered and executed on the partition basis. We further develop efficient intra- and inter-partition execution strategies for efficiency. For intra-partition processing, since the graph partition fits into LLC, we propose to execute each graph query with efficient sequential algorithms (in contrast with parallel algorithms in existing parallel graph processing systems) and present an atomic-free query processing method by consolidating contending operations to cache-resident graph partition. For inter-partition processing, we propose two designs, yielding and priority-based scheduling, to reduce redundant work in processing. Besides, we theoretically prove that ForkGraph performs the same amount of work, to within a constant factor, as the fastest known sequential algorithms in FPP queries processing, which is work efficient. Our evaluations on real-world graphs show that ForkGraph significantly outperforms state-of-the-art graph processing systems (including Ligra, Gemini, and GraphIt) with two orders of magnitude speedups.

The recent scale-up of GPU hardware through the integration of multiple GPUs into a single machine and the introduction of higher bandwidth interconnects like NVLink 2.0 has enabled new opportunities of relational query processing on multiple GPUs. However, due to the unique characteristics of GPUs and the interconnects, existing hash join implementations spend up to 66% of their execution time moving the data between the GPUs and achieve lower than 50% utilization of the newer high bandwidth interconnects. This leads to extremely poor scalablity of hash join performance on multiple GPUs, which can be slower than the performance on a single GPU. In this paper, we propose MG-Join, a scalable partitioned hash join implementation on multiple GPUs of a single machine. In order to effectively improve the bandwidth utilization, we develop a novel multi-hop routing for cross-GPU communication that adaptively chooses the efficient route for each data flow to minimize congestion. Our experiments on the DGX-1 machine show that MG-Join helps significantly reduce the communication overhead and achieves up to 97% utilization of the bisection bandwidth of the interconnects, resulting in significantly better scalability. Overall, MG-Join outperforms the state-of-the-art hash join implementations by up to 2.5x. MG-Join further helps improve the overall performance of TPC-H queries by up to 4.5x over multi-GPU version of an open-source commercial GPU database Omnisci.

Year-round ultraviolet exposure silently causes skin damage that goes unnoticed until sunburn. Current personal wearables for monitoring UV exposure have not seen significant uptake, which may be attributed to their one-size-fits-all aesthetic or inapplicability to people with different skin tones. We present EcoPatches, inkjet-printable chemical patches that mediate a person's relationship with their environment by allowing them to create designs and formulations that resonate with them. Supporting human- and machine-interpretability for EcoPatches' visual changes means that users can glance at their EcoPatch during the day to see large exposure changes or take a picture of their EcoPatch with a smartphone app for more accurate and precise readings. We conducted an online survey to elicit visual design recommendations that support these features. We also evaluated both interpretation methods, finding that they achieved strong Pearson correlation coefficients with the \projectnames' known exposure levels (human: 0.79, app: 0.90).

Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how system-designers choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.

Interaction-based systems have been widely used in many enterprises like Grab to enable quick and easy analysis of large-scale spatial data. Unlike traditional instruction-based query processing systems, modern interaction-based systems allow users to issue complex queries through simple interactions with a Graphical User Interface (GUI). While such systems have significantly transformed the process of spatial query processing, they still rely on a process-after-query approach for executing the queries. Even though the user is continuously interacting with the GUI, the actual processing is only initiated after the user completes their interactions, thus wasting the opportunities to reduce the response time of query processing.

In recent years, we have witnessed significant efforts to improve the performance of Online Analytical Processing (OLAP) on graphics processing units (GPUs). Most existing studies have focused on improving memory efficiency since memory stalls can play an essential role in query processing performance on GPUs. Motivated by the recent rise of just-in-time (JIT) compilation in query processing, we investigate whether and how we can further improve query processing performance on GPU. Specifically, we study the execution of state-of-the-art JIT compile-based query processing systems. We find that thanks to advanced techniques such as database compression and JIT compilation, memory stalls are no longer the most significant bottleneck. Instead, current JIT compile-based query processing encounters severe under-utilization of GPU hardware due to divergent execution and degraded parallelism arising from resource contention. To address these issues, we propose a JIT compile-based query engine named Pyper to improve GPU utilization during query execution. Specifically, Pyper has two new operators, Shuffle and Segment , for query plan transformation, which can be plugged into a physical query plan in order to reduce divergent execution and resolve resource contention, respectively. To determine the insertion points for these two operators, we present an analytical model that helps insert Shuffle and Segment operators into a query plan in a cost-based manner. Our experiments show that 1) the analytical analysis of divergent execution and resource contention helps to improve the accuracy of the cost model, 2) Pyper significantly outperforms other GPU query engines on TPC-H and SSB queries.

Nonvisually Accessible Video Calling (NAVC) is a prototype that detects visual conversation cues in a video call and uses audio cues to convey them to a user who is blind or low-vision. NAVC uses audio cues inspired by movie soundtracks to convey Attention, Agreement, Disagreement, Happiness, Thinking, and Surprise. When designing NAVC, we partnered with people who are blind or low-vision through a user-centered design process that included need-finding interviews and design reviews. To evaluate NAVC, we conducted a user study with 16 participants. The study provided feedback on the NAVC prototype and showed that the participants could easily discern some cues, like Attention and Agreement, but had trouble distinguishing others. The accuracy of the prototype in detecting conversation cues emerged as a key concern, especially in avoiding false positives and in detecting negative emotions, which tend to be masked in social conversations. This research identified challenges and design opportunities in using AI models to enable accessible video calling.

Traditionally, FPGAs were programmed using low-level Hardware Description Languages (HDLs) like Verilog or VHDL, which made it extremely difficult to design, build and maintain systems for FPGAs. However, the recent release of OpenCL SDKs by FPGA vendors like Xilinx and Altera have significantly improved the programmability of FPGAs and have brought new research opportunities for query processing systems on FPGAs. It remains an open question whether and how we can optimize OpenCL based database engines for FPGAs. There is a gap on optimizations and tuning between OpenCL and FPGA, since OpenCL is mainly designed for parallel multi-/many-core architectures. In this paper, we attempt to answer this question under the context of pipelined query execution. For this, we first perform a detailed study of database engines on the latest generation of FPGAs. We then design an FPGA based shared pipeline query execution system (FADE) which exploits the hardware features of FPGAs and minimizes inefficiencies like the high communication reconfiguration overhead. Our experiments show that our design achieves significant performance speedup over existing approaches for pipelined query executions on FPGA. Finally, we also present the challenges and opportunities for query processing on the latest generation FPGAs.

This paper describes a micro-stress intervention system for information office workers in the workplace, their responses to the interventions and machine learning models to predict the most opportune timing for providing the interventions. We studied 30 office workers for 10 days and examined their work patterns by monitoring their computer and application usage, sleep, activity, heart rate and its variability, as well as the history of micro-stress interventions provided through our desktop software. We analyzed temporal patterns of stress intervention acceptance/rejection and the relationships between their subjective and objective responses to the interventions and perceived work engagement, challenge and stress levels. We then developed machine learning models to predict better stress intervention delivery timing based on this multi-modal data. We found that features from computer and application usage, activity, heart rate variability and stress intervention history showed up to 80.0% accuracy in predicting good or bad intervention timing using a multi-kernel support vector machine algorithm. These findings could help practitioners design the most effective, just-in-time, closed-loop, stress interventions. To our knowledge, this is one of the first papers to review opportune stress interventions' delivery timing research, which could have a big influence in designing stress intervention technologies.

Recently, field-programmable gate array (FPGA) vendors (such as Altera) have started to address the programmability issues of FPGAs via OpenCL SDKs. In this paper, we analyze the performance of relational database applications on FPGAs using OpenCL. In particular, we study how to improve the performance of data partitioning, which is a very important building block in relational database. Since the data partitioning causes random memory accesses, it is time-consuming, and then, it has been the major bottleneck for database operators, such as partitioned hash join. In particular, we import the state-of-the-art OpenCL implementation of data partitioning from OmniDB, which was originally designed and optimized for CPUs/GPUs, and we find that this implementation suffers from both lock overhead and memory bandwidth overhead. Accordingly, we present a multikernel approach to address the lock overhead by leveraging two emerging features (task kernel and channel) from Altera OpenCL software development kit. In order to reduce the memory bandwidth overhead, on-chip buckets are used to reduce the number of random global memory transactions. We further develop an FPGA-specific cost model to guide the parameter configuration. We evaluate the proposed design on a recent OpenCL-based FPGA. We have applied our optimized partitioning method to a number of data processing tasks, including hash join, histogram, and hash search. Our experimental results demonstrate that our cost model can accurately guide the user to determine the optimal parameter combination for data partitioning and the optimal parameter combination can achieve 16.6× speedup over the default multithreaded implementation.

In HCI research, attention has focused on understanding external influences on workplace multitasking. We explore instead how multitasking might be influenced by individual factors: personality, stress, and sleep. Forty information workers' online activity was tracked over two work weeks. The median duration of online screen focus was 40 seconds. The personality trait of Neuroticism was associated with shorter online focus duration and Impulsivity-Urgency was associated with longer online focus duration. Stress and sleep duration showed trends to be inversely associated with online focus. Shorter focus duration was associated with lower assessed productivity at day's end. Factor analysis revealed a factor of lack of control which significantly predicts multitasking. Our results suggest that there could be a trait for distractibility where some individuals are susceptible to online attention shifting in the workplace. Our results have implications for information systems (e.g. educational systems, game design) where attention focus is key.

While email provides numerous benefits in the workplace, it is unclear how patterns of email use might affect key workplace indicators of productivity and stress. We investigate how three email use patterns: duration, interruption habit, and batching, relate to perceived workplace productivity and stress. We tracked email usage with computer logging, biosensors and daily surveys for 40 information workers in their in situ workplace environments for 12 workdays. We found that the longer daily time spent on email, the lower was perceived productivity and the higher the measured stress. People who primarily check email through self-interruptions report higher productivity with longer email duration compared to those who rely on notifications. Batching email is associated with higher rated productivity with longer email duration, but despite widespread claims, we found no evidence that batching email leads to lower stress. We discuss the implications of our results for improving organizational email practices.

The release of OpenCL support for FPGAs represents a significant improvement in extending database applications to the reconfigurable domain. Taking advantage of the programmability offered by the OpenCL HLS tool, an OpenCL database can be easily ported and re-designed for FPGAs. A single SQL query in these database systems usually consists of multiple operators, and each one of these operators in turn consists of multiple OpenCL kernels. Due to the specific properties of FPGAs, each OpenCL kernel can have different optimization combinations (in terms of CU and SIMD) which is critical to the overall performance of query processing. In this paper, we propose an efficient method to implement database operators on OpenCL-based FPGAs. We use a cost model to determine the optimum query plan for an input query. Our cost model has two components: unit cost and query plan generation. The unit cost component generates multiple (unit cost, resource utilization) pairs for each kernel. The query plan generation component employs a dynamic programming approach to generate the optimum query plan which consider the possibilities to use multiple FPGA images. The experiments show that 1) our cost model can accurately predict the performance of each feasible query plan for the input query, and is able to guide the generation of the optimum query plan, 2) our optimized query plan achieves a performance speedup 1.5X-4X over the state-of-the-art query processing on OpenCL-based FPGAs.

The release of OpenCL support for FPGAs represents a significant improvement in extending database applications to the reconfigurable domain. Taking advantage of the programmability offered by the OpenCL HLS tool, an OpenCL database can be easily ported and re-designed for FPGAs. A single SQL query in these database systems usually consists of multiple operators, and each one of these operators in turn consists of multiple OpenCL kernels. Due to the specific properties of FPGAs, each OpenCL kernel can have different FPGA-specific optimization combinations, in terms of CU (compute unit) and SIMD (kernel vectorization), which are critical to the overall performance of query processing. Due to the resource limitation of an FPGA image, our query plan also considers the possibility of using multiple FPGA images. In this paper, we propose an FPGA-specific cost model to determine the optimal query plan in less than one minute. In particular, the FPGA synthesis time is significantly reduced by avoiding the need to evaluate all the feasible query plans on real FPGAs. Our cost model has two components: unit cost and optimal query plan generation. The first component generates multiple (unit cost, resource utilization) pairs for each kernel. The second component employs a dynamic programming approach to generate the optimal query plan which considers the possibility of using multiple FPGA images. The experiments show that 1) our cost model can accurately predict the performance of each feasible query plan for the input query, and can guide the optimal query plan generation, 2) our optimized query plan achieves a performance speedup 1.5×-4× over the state-of-the-art query processing on OpenCL-based FPGAs.

Given the importance of creativity for both personal and societal achievements, there have been consistent efforts to stimulate creative ability. But an important environmental factor --- blue (i.e., short wavelength) light --- has been relatively unexplored to date. Blue light improves a number of cognitive processes (e.g., attention, working memory and sleep) known to influence our creative abilities. In this study, we investigate the effects of blue light on enhancing creativity in tasks and compare it to the effects of walking, which has been shown to stimulate creative ability. Based on data from 21 participants over 2 weeks, we found that blue light resulted in a 24.3% increase in convergent thinking ability, while walking improved divergent thinking by 18%. We discuss the implications of the findings within the context of UbiComp research. To the best of our knowledge, this is the first systematic examination of the impact of blue light on convergent and divergent thinking ability.

Graphics Processing Units (GPUs) have evolved as a powerful query co-processor for main memory On-Line Analytical Processing (OLAP) databases. However, existing GPU-based query processors adopt a kernel-based execution approach which optimizes individual kernels for resource utilization and executes the GPU kernels involved in the query plan one by one. Such a kernel-based approach cannot utilize all GPU resources efficiently due to the resource underutilization of individual kernels and memory ping-pong across kernel executions. In this paper, we propose GPL, a novel pipelined query execution engine to improve the resource utilization of query co-processing on the GPU. Different from the existing kernel-based execution, GPL takes advantage of hardware features of new-generation GPUs including concurrent kernel execution and efficient data communication channel between kernels. We further develop an analytical model to guide the generation of the optimal pipelined query plan. Thus, the tile size of the pipelined query execution can be adapted in a cost-based manner. We evaluate GPL with TPC-H queries on both AMD and NVIDIA GPUs. The experimental results show that 1) the analytical model is able to guide determining the suitable parameter values in pipelined query execution plan, and 2) GPL is able to significantly outperform the state-of-the-art kernel-based query processing approaches, with improvement up to 48%.

Despite a long history and a large volume of affective research, measuring affective states is still a non-trivial task that is complicated by numerous conceptual and methodological decisions that the researcher has to make. We suggest that inconsistent results reported in some areas of research can be partially explained by the choice of measurements that capture different manifestations of affective phenomena, or focus on different elements of affective processes. In the present study we examine one of such topics - a relationship between stress and individual's work role. In a 2-week, multi-method in situ study we collected affective information from 40 subjects. All participants provided continuous physiological (cardiovascular) data for the entire duration of the study, submitted multiple daily self-reports of momentary affect, and filled out a onetime assessment of the global perceived stress. We found that individuals' job role (specifically, decision-making workload) was not related to the cumulative measures of momentary affect, but was negatively correlated with the overall level of perceived stress. We further found that this negative relationship was partially mediated by individuals' coping behaviors. Our results emphasize the important difference between fleeting and global (appraised) affective states, and remind about intervening variables that can significantly modify affective processes. We suggest directions for future research and discuss practical applications for stress management.

Accurate and timely assessment of collective emotions in the workplace is a critical managerial task. However, perceptual, normative, and methodological challenges make it very difficult even for the most experienced organizational leaders. In this paper we present a MoodTracker - a technological solution that can help to overcome these challenges, and facilitate a continuous monitoring of the collective emotions in large groups in real-time. The MoodTracker is a program that runs on any PC device, and provides users with an interface for self-report of their affect. The device was tested in situ for four weeks, during which we received over 3000 emotion self-reports. Based on the usage data, we concluded that users had a positive attitude toward the MoodTracker and favorably evaluated its utility. From the collected data we were also able to establish some patterns of weekly and daily variations of employees' emotions in the workplace. We discuss practical applications and suggest directions for future development.

We developed a feedback-loop, user-tailored advice system to provide stress interventions and advice about improving sleep, diet, and exercise habits at the workplace. Thirty participants joined a 2 week study: in the first week, we collected their behaviors about sleep, diet, exercise and stress levels using Fitbit and surveys. During the second week we continued monitoring, and based on the participants' measurements in the previous days, we also provided interventions and advice during the workday, and evaluated their preferences. We found that participants with higher stress levels liked stress interventions more and that somatic activities were most preferred and reduced stress levels the most. We observed individual preference differences in the types of advice; however, tracking and receiving advice raised users' awareness of their stress, sleep, exercise, and dietary behaviors. We found that the largest positive impact was on our participants' dietary behaviors.

A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research.

While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.

Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women's brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.

To explore the value of speech input focus for handling notifications, we built and deployed SpeechToast, an Outlook Add-in that replaces standard email notifications with a version that includes speech input commands (e.g. "open", "delete"). Notifications shown by SpeechToast have speech input focus when the audio context surrounding the computer is favorable for speech recognition. We deployed SpeechToast to 18 current users of email notifications for 4 weeks. Overall, speech input focus appealed to some participants, while non-users indicated their willingness to have it enabled as long as it did not detract from their experience. Our research suggests that selectively enabling speech input focus could provide natural and intuitive interactions that complement other input modalities.

Consumer-based synchronous video communication is on the rise and is viewed as a valuable medium to support long distance relationships. We were interested in the potential of asynchronous video to augment children's close friendships and what types of activities they would engage in using video. We explored both of these concepts through a 9-week field study with a group of six 9-10 year old girls. We see children as potential media trendsetters when it comes to video communication given their comfort with video and desire for rich social interactions. The results from this study were striking. Despite having frequent face-to-face interactions, the girls used our asynchronous video communication tool extensively to augment their existing relationships. Not only were they able to have rich conversations using asynchronous video, they also demonstrated a strong desire to share more than just a "talking head". The results from this work point to the need for video mediated communication to move beyond conversations, to the sharing of rich experiences.

Work teams are often geographically distributed, and in some cases, experience large time-zone differences with no overlap in working hours. We explored the use of asynchronous video in temporally distributed teams. We developed VideoThreads, which provides a novel thread-based visualization of video messages. Based on a deployment to four teams, we offer design recommendations and insights about the benefits of asynchronous video sharing.

Current interfaces for common information visualizations such as bar graphs, line graphs, and scatterplots usually make use of the WIMP (Windows, Icons, Menus and a Pointer) interface paradigm with its frequently discussed problems of multiple levels of indirection via cascading menus, dialog boxes, and control panels. Recent advances in interface capabilities such as the availability of pen and touch interaction challenge us to re-think this and investigate more direct access to both the visualizations and the data they portray. We conducted a Wizard of Oz study to explore applying pen and touch interaction to the creation of information visualization interfaces on interactive whiteboards without implementing a plethora of recognizers. Our wizard acted as a robust and flexible pen and touch recognizer, giving participants maximum freedom in how they interacted with the system. Based on our qualitative analysis of the interactions our participants used, we discuss our insights about pen and touch interactions in the context of learnability and the interplay between pen and touch gestures. We conclude with suggestions for designing pen and touch enabled interactive visualization interfaces.

Although domestic computer use is increasing, most efforts to reduce energy use through improved power management have focused on computers in the workplace. We studied 20 households to understand how people use power management strategies on their home computers. We saw computers in the home, particularly desktop computers, are left on much more than they are actively used suggesting opportunities for economic and energy savings. However, for most of our participants, the economic incentives were too minor to motivate them to turn off devices when not in use, especially given other frustrations such as long boot up times. We suggest research directions for home computer power management that could help users be more green without having to dramatically change their home computing habits.

For over a century, citizen scientists have volunteered to collect huge quantities of data for professional scientists to analyze. We designed Pathfinder, an online environment that challenges this traditional division of labor by providing tools for citizen scientists to collaboratively discuss and analyze the data they collect. We evaluated Pathfinder in a sustainability and commuting context using a mixed methods approach in both naturalistic and experimental settings. Our results showed that citizen scientists preferred Pathfinder to a standard wiki and were able to go beyond data collection and engage in deeper discussion and analyses. We also found that citizen scientists require special types of technological support because they generate original research. This paper offers an early example of the mutually beneficial relationship between HCI and citizen science.