We develop a Personal Knowledge Graph Question-Answering (PKGQA) assistant, seamlessly integrating information from multiple mobile applications into a unified and user-friendly query interface to offer users convenient information retrieval and personalized knowledge services. Based on a fine-grained schema customized for PKG, the PKGQA system in this paper comprises Symbolic Semantic Parsing, Frequently Asked Question (FAQ) Semantic Matching, and Neural Semantic Parsing modules, which are designed to take into account both accuracy and efficiency. The PKGQA system achieves high accuracy on the constructed dataset and demonstrates good performance in answering complex questions. Our system is implemented through an Android application, which is shown in https://youtu.be/p732U5KPEq4.

Fast in-memory key value stores are the keys to building large-scale Internet services. The state-of-the-art solutions mainly focus on optimizing the performance for read-intensive workloads. Nevertheless, a wide range of applications demonstrate a significant amount of updates and range queries, which scale poorly with the current implementations. In this paper, we present BiloKey, a highly scalable in-memory key value store on multi-core machines, significantly outperforming Redis and Memcached for a variety of mixed read and write workloads. To achieve this, BiloKey leverages a fast bi-index comprised by a Hash Table index and a SkipList index, where the former supports feature rich operations including GET, UPDATE and DELETE with O(1) complexity, while the latter supports SCAN with O(log N) complexity. Furthermore, to make the bi-index design scale well, BiloKey adopts three techniques: lazy synchronization for reducing the overhead of maintaining index consistency, lock-free data structure for supporting multi-writers, and locality-aware data parallel processing for preserving the data locality of requests. Compared with two popular in-memory KV stores (i.e., Redis and Memcached), experimental results show that: (1) for write-intensive workloads, BiloKey outperforms Redis and Memcached by 7.8x and 3.7x on average (up to 11.5x and 4.8x), respectively; (2) for scan-intensive workloads, BiloKey achieves an average speedup of 2.3x against Redis; (3) for read-intensive workloads, BiloKey also outperforms Redis and Memcached by 1.2x and 1.8x on average.

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise.