Modern large-scale services such as search engines, messaging platforms, and serverless functions, rely on key-value (KV) stores to maintain high performance at scale. When such services are deployed in constrained memory environments, they present challenging requirements: point operations requiring high throughput, working sets much larger than main memory, and natural skew in key access patterns. Traditional KV stores, based on LSM- and B-Trees, have been widely used to handle such use cases, but they often suffer from suboptimal use of modern hardware resources. The FASTER project, developed as a high-performance open-source KV storage library, has demonstrated remarkable success in both inmemory and hybrid storage environments. However, when tasked with serving large skewed workloads, it faced challenges, including high indexing and compactions overheads, and inefficient management of non-overlapping read-hot and write-hot working sets. In this paper, we introduce F2 (for FASTER v2), an evolution of FASTER designed to meet the requirements of large skewed workloads common in industry applications. F2 adopts a two-tier record-oriented design to handle larger-than-memory skewed workloads, along with new concurrent latch-free mechanisms and components to maximize performance on modern hardware. To realize this design, F2 tackles key challenges and introduces several innovations, including new latch-free algorithms for multi-threaded log compaction, a two-level hash index to reduce indexing overhead for cold records, and a read-cache for serving read-hot records. Our evaluation shows that F2 achieves 2–11.9× better throughput compared to existing KV stores, effectively serving the target workload. F2 is open-source and available as part of the FASTER project.

Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available for navigation purposes, may consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a cloud-offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEON, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

With hundreds of millions of users, spreadsheets are one of the most important end-user applications. Spreadsheets are easy to use and allow users great flexibility in storing data. This flexibility comes at a price: users often treat spreadsheets as a poor man's database, leading to creative solutions for storing high-dimensional data. The trouble arises when users need to answer queries with their data. Data manipulation tools make strong assumptions about data layouts and cannot read these ad-hoc databases. Converting data into the appropriate layout requires programming skills or a major investment in manual reformatting. The effect is that a vast amount of real-world data is "locked-in" to a proliferation of one-off formats. We introduce FlashRelate, a synthesis engine that lets ordinary users extract structured relational data from spreadsheets without programming. Instead, users extract data by supplying examples of output relational tuples. FlashRelate uses these examples to synthesize a program in Flare. Flare is a novel extraction language that extends regular expressions with geometric constructs. An interactive user interface on top of FlashRelate lets end users extract data by point-and-click. We demonstrate that correct Flare programs can be synthesized in seconds from a small set of examples for 43 real-world scenarios. Finally, our case study demonstrates FlashRelate's usefulness addressing the widespread problem of data trapped in corporate and government formats.

Due to poor signal strength, multipath effects, and limited on-device computation power, common GPS receivers do not work indoors. This work addresses these challenges by using a steerable, high-gain directional antenna as the front-end of a GPS receiver along with a robust signal processing step and a novel location estimation technique to achieve direct GPS-based indoor localization. By leveraging the computing power of the cloud, we accommodate longer signals for acquisition, and remove the requirement of decoding timestamps or ephemeris data from GPS signals. We have tested our system in 31 randomly chosen spots inside five single-story, indoor environments such as stores, warehouses and shopping centers. Our experiments show that the system is capable of obtaining location fixes from 20 of these spots with a median error of less than 10 m, where all normal GPS receivers fail.

We demonstrate the CLEO mobile sensing platform, including ultra-portable, low-power sensors, phone-based data upload software, and a location resolution and data management web service in the cloud. The sensing platform leverages cloud-offloaded GPS (CO-GPS) for location sensing. A sensor communicates with mobile devices using an audio-port-based connector. The back-end cloud service supports OData protocol to make data accessible to other systems, such as World-Wide Telescope (WWT).

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available, consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a Cloud-Offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEO, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.