Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying ad hoc views on the KG and producing a rewriting of the graph containing merely the data to be processed. Graph traversal and rule extraction are this way boosted. Using graph embeddings methods, the different ad hoc views can be transformed into low-dimensional representation following the same data format. This way a single machine learning procedure can address the different modalities, simplifying the architecture of our system. The experiments we executed demonstrate that our approach reduces the cost of execution and improves the accuracy of analytics.

中文翻译：

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迈向多模态大数据分析的通用框架

大数据架构中的多模式分析意味着数据处理任务的复合配置。数据中的每种模式都需要触发特定数据处理任务的特定分析。可以通过仔细校准不同任务共享的资源来实现可伸缩性，以寻求与它们强加的多种要求的权衡。我们提出了一种在同一数据处理方法中解决多模式分析的方法，以获得可以充分利用大数据基础设施并行处理潜力的简化架构。首先将多个数据源集成到一个统一的知识图谱（KG）中。不同的数据模式通过指定临时的来解决KG 上的视图并生成仅包含要处理的数据的图形的重写。图遍历和规则提取以这种方式得到提升。使用图形嵌入方法，可以将不同的即席视图转换为遵循相同数据格式的低维表示。这样，单个机器学习过程就可以解决不同的模式，从而简化我们系统的架构。我们执行的实验表明，我们的方法降低了执行成本并提高了分析的准确性。