High-Performance Data Analytics (HPDA) combines high-performance computing (HPC) with data analytics to uncover patterns and insights in dual-intensive applications that are both data-intensive and compute-intensive. Traditional big data frameworks and HPC technologies often struggle to address these demands independently, prompting researchers to explore their integration. Spark, known for its efficient in-memory computing with RDDs, and MPI, a foundational standard in HPC, are prominent candidates for such integration. This survey explores the integration of Spark and MPI for HPDA, highlighting their potential for unified data processing and computation. We first classify application workloads and review the characteristics and limitations of traditional frameworks. Then, we analyze the challenges and requirements of integrated architectures, focusing on the specific implementations of typical middleware-level architectures. Through comparative analysis, we highlight their advantages and limitations. Finally, we present application examples, outline key challenges and future research directions, and briefly discuss progress in integration approaches for other technology combinations.

With the flourish of the Web, online review is become a more and more useful and important information resource for people. As a result, automatic review mining has become a hot research topic recently. Traditional review mining based on feature extracts product feature and opinion word independently, and seldom considers their association information. In this paper, we only focus on Chinese product review. We propose a method based on Chinese dependency grammar to extract feature-opinion word pairs. Specifically, we use Chinese dependency grammar to set several rules, then we make use of these rules to extract candidate feature-opinion word pairs. Finally, we filter out mismatched feature-opinion words pairs by feature ranking and Named Entity Recognition (NER) system. Experiment shows that our method in Precision is rather high.

A modified collaborative filtering recommendation has been put forward for clustered data in social networks. Firstly, the basic model is built up, which shows the outline of current existing types of the recommendation platform. Secondly, on this basis, the vertexes and edges in the model are divided as the entities of the social networks with properties given formally. This framework implements traditional collaborative filtering based on clustered datasets using improved k-means clustering method, and our design has put forward to picking up the active users among all of them and let them help our system generate recommendations more precise and faster. Finally, we have crawled through data from two famous movie recommendation websites, MovieLens and Imdb, in the experiment test. From the experiment results, it is concluded that the implemented collaborative filtering recommendation system is performed better than the naive one both in precision and stability, which aims to supply collaborative filtering recommendation and actively contributing recommendation that suits users' tastes, has completed the task of improving collaborative recommendation and reached the expected goals.