Answering complex queries in store fulfillment, such as ''What percentage of employee-assigned actions remain unresolved?'' or ''How many worklists for a specific product type were completed within a timeframe at each location?'' requires precise, multi-hop reasoning across datasets with varying granularities. This paper introduces an ontology-based knowledge graph (KG) approach integrated with a structured text-to-Cypher generation pipeline, enabling accurate retrieval for such queries. Benchmarking against a robust hybrid search baseline combining BM25 and semantic search, our method demonstrates superior performance in addressing multi-hop and cross-granularity questions. Leveraging a KG schema designed to capture intricate relationships (e.g. (OrderLineItem)-[:INVOLVES_ACTION]->(Action)-[:INVOLVES]->(BatchProcess)-[:IS_COMPLETED_AT]->(Location)), we reveal universal patterns for constructing and querying highly relational data. This work highlights the transformative potential of ontology-driven KGs to improve reasoning, data aggregation, and decision-making, with broader implications for any domain requiring structured, multi-relational data analysis.

Summary The northwest region in China located at arid and semiarid areas, atmospheric precipitation converted by the cloud is an important part of water resources, and if we fully utilize cloud for cloud‐water conversion to alleviate the scarcity of water, thereby it is particularly important to analyze the macroscopic characteristics and the changing trends of clouds in the northwest region. In this paper, the 2016 Level 1 data of Fengyun Satellite has been calibrated, corrected, and processed, using the improved multi‐spectral thresholding method to calculate cloud coverage and cloud classification data. The optical thickness inversion uses the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) radiation transmission mode to establish a radiation look‐up table with optical thickness as a function variable under different conditions of observation geometry, underlying surface type, and atmospheric environment. The results show that the coverage of clouds in the northwest region accounts for about 45%, and the cloud coverage changes with the seasons. The classification of clouds mainly consists of high clouds, low clouds, and cumulonimbus. The optical thickness of clouds is largely distributed between 10 and 25.

R is one of the most popular languages in the data science, statistical and machine learning (ML) community. However, when it comes to scalable data analysis and ML using R, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large datasets efficiently, and (b) knowledge about the appropriate computing environments to scale R scripts from desktop exploratory analysis to elastic and distributed cloud services. In this tutorial we will discuss solutions that demonstrate the use of distributed compute environments and end to end solutions for R. We will present the topics through presentations and worked-out examples with sample code. In addition, we will provide a public code repository that attendees will be able to access and adapt to their own practice. We believe this tutorial will be of strong interest to a large and growing community of data scientists and developers using R for data analysis and modeling.