Institutions small and large often use student teaching assistants (TAs) to provide office hours, tutor students, and grade student work, and the quality of a course's TAs can greatly affect the quality of the course. In this BOF we will discuss the training we provide to prepare TAs for their course duties and the mentoring we provide to foster their role as future educators. Improving the effectiveness of TA training and mentoring can have an immediate impact on the quality of teaching, potentially improve retention and diversity in computer science, and have a long-term impact on all aspects of our field as our current students and TAs progress through their industry and academic careers. Our goal is to provide a forum for you to disseminate your TA mentoring practices and for you to hear the mentoring practices of others, with the goal to develop a collection of best practices for TA training and mentoring for computer science. This BOF is appropriate for professors or instructors of any computer science course or summer program that uses undergraduate or graduate student teaching assistants.

Many mobile applications rely on location information gained from location services on mobile devices. However, continuously tracking the device location with high accuracy drains the battery quickly. Furthermore, sensing the same location can be redundant when multiple devices are co-located. In this paper, we develop a crowdsourcing-based location service, E2A2 (energy efficient and accuracy aware), which places colo-cated devices into groups, and uses group location to represent individual device location. The E2A2 location service aims to reduce individual device battery consumption associated with location services while simultaneously maintaining high location accuracy for each device. Our experimental results from a prototype system show the effectiveness of our proposed solution with different mobility patterns. We also present results on the impact of different system parameters and the number of users in a group. Compared to running GPS location services on individual devices separately, our E2A2 service saves on average 33% battery consumption rate when 4 devices are co-located at walking speed and 26% battery consumption rate when 4 devices are colocated on the same bus while meeting the same accuracy requirements.

Crowd-sourcing social computing systems represent a new material for HCI designers. However, these systems are difficult to work with and to prototype, because they require a critical mass of participants to investigate social behavior. Service design is an emerging research area that focuses on how customers co-produce the services that they use, and thus it appears to be a great domain to apply this new material. To investigate this relationship, we developed Tiramisu, a transit information system where commuters share GPS traces and submit problem reports. Tiramisu processes incoming traces and generates real-time arrival time predictions for buses. We conducted a field trial with 28 participants. In this paper we report on the results and reflect on the use of field trials to evaluate crowd-sourcing prototypes and on how crowd sourcing can generate co-production between citizens and public services.

A promising approach to scaling Web applications is to distribute the server infrastructure on which they run. This approach, unfortunately, can introduce latency between the application and database servers, which in turn increases the network latency of Web interactions for the clients (end users). In this paper we introduce the concept of source-to-source holistic transformations - transformations that seek to optimize both the application code and the database requests made by it, to reduce client latency. As examples of our concept, we propose and evaluate two source-to-source holistic transformations that focus on hiding the latencies of database queries. We argue that opportunities for applying these transformations will continue to exist in Web applications. We then present algorithms for automating these transformations in a source-to-source compiler. Finally, we evaluate the effect of these two transformations on three realistic Web benchmark applications, both in the traditional centralized setting and a distributed setting.

We consider the problem of simultaneous source location: selecting locations for sources in a capacitated graph such that a given set of demands can be satisfied simultaneously, with the goal of minimizing the number of locations chosen. For general directed and undirected graphs we give an O (log D )-approximation algorithm, where D is the sum of demands, and prove matching Ω(log D ) hardness results assuming P ≠ NP . For undirected trees, we give an exact algorithm and show how this can be combined with a result of Räcke to give a solution that exceeds edge capacities by at most O (log 2 n log log n ), where n is the number of nodes. For undirected graphs of bounded treewidth we show that the problem is still NP -hard, but we are able to give a PTAS with at most (1 + ϵ) violation of the capacities for arbitrarily small ϵ, or a ( k +1) approximation with exact capacities, where k is the treewidth.

The backend database system is often the performance bottleneck when running web applications. A common approach to scale the database component is query result caching, but it faces the challenge of maintaining a high cache hit rate while efficiently ensuring cache consistency as the database is updated. In this paper we introduce Ferdinand, the first proxy-based cooperative query result cache with fully distributed consistency management. To maintain a high cache hit rate, Ferdinand uses both a local query result cache on each proxy server and a distributed cache. Consistency management is implemented with a highly scalable publish/subscribe system. We implement a fully functioning Ferdinand prototype and evaluate its performance compared to several alternative query-caching approaches, showing that our high cache hit rate and consistency management are both critical for Ferdinand's performance gains over existing systems.

For their scalability needs, data-intensive Web applications can use a database scalability service (DBSS), which caches applications' query results and answers queries on their behalf. One way for applications to address their security/privacy concerns when using a DBSS is to encrypt all data that passes through the DBSS. Doing so, however, causes the DBSS to invalidate large regions of its cache when data updates occur. To invalidate more precisely, the DBSS needs help in order to know which results to invalidate; such help inevitably reveals some properties about the data. In this paper, we present invalidation clues, a general technique that enables applications to reveal little data to the DBSS, yet limit the number of unnecessary invalidations. Compared with previous approaches, invalidation clues provide applications significantly improved tradeoffs between security/privacy and scalability. Our experiments using three Web application benchmarks, on a prototype DBSS we have built, confirm that invalidation clues are indeed a low-overhead, effective, and general technique for applications to balance their privacy and scalability needs.