Data pipelines (i.e., converting raw data to features) are critical for machine learning (ML) models, yet their development and management is time-consuming. Feature stores have recently emerged as a new "DBMS-for-ML" with the premise of enabling data scientists and engineers to define and manage their data pipelines. While current feature stores fulfill their promise from a functionality perspective, they are resource-hungry---with ample opportunities for implementing database-style optimizations to enhance their performance. In this paper, we propose a novel set of optimizations specifically targeted for point-in-time join, which is a critical operation in data pipelines. We implement these optimizations on top of Feathr: a widely-used feature store, and evaluate them on use cases from both the TPCx-AI benchmark and real-world online retail scenarios. Our thorough experimental analysis shows that our optimizations can accelerate data pipelines by up to 3× over state-of-the-art baselines.

Populating Knowledge Base (KB) with new knowledge facts from reliable text resources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs.However, the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors.In this paper, we propose a novel joint inference framework to allow interactions between the two subtasks and find an optimal assignment by addressing the coherence among preliminary local predictions: whether the types of entities meet the expectations of relations explicitly or implicitly, and whether the local predictions are globally compatible.We further measure the confidence of the extracted triples by looking at the details of the complete extraction process.Experiments show that the proposed framework can significantly reduce the error propagations thus obtain more reliable facts, and outperforms competitive baselines with state-of-the-art relation extraction models.

Mobile karaoke has attracted more attention as a popular mobile entertainment and social network platform, where music recommendations are highly desired to improve its user experiences. Traditional music recommendation methods suffer from the data sparsity issue and usually ignore the social interactions among users. In this paper, we propose a novel parallel community-based matrix factorization method which exploits implicit user behavior data to model user preferences from both social level, via community detection, and individual level. Both offline evaluation on a real dataset from Changba and online traffic investigations show the effectiveness of our method.

With smartphones further integrating into our lives, more people start to sing using mobile karaoke apps instead of going to a KTV club. However, the playback and record APIs of Android systems do not respond in real-time when called. Thus, an Android karaoke app will have to align the record music and the original accompaniment when super-posing those two audios. Dynamic time warping (DTW) based algorithms are usually used to find the optimal alignment between two audios and yield best result so far. In this paper, we propose a simple yet robust approach by considering waveform similarities to solve this problem. Experimental results show that our method outperforms the state-of-the-art method in both accuracy and robustness across different genres and devices.

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. However, existing solutions suffer from two limitations: 1) they cannot answer SPARQL queries with wildcards in a scalable manner; and 2) they cannot handle frequent updates in RDF repositories efficiently. Thus, most of them have to reprocess the dataset from scratch. In this paper, we propose a graph-based approach to store and query RDF data. Rather than mapping RDF triples into a relational database as most existing methods do, we store RDF data as a large graph. A SPARQL query is then converted into a corresponding subgraph matching query. In order to speed up query processing, we develop a novel index, together with some effective pruning rules and efficient search algorithms. Our method can answer exact SPARQL queries and queries with wildcards in a uniform manner. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solution.