{'fraction_non_alphanumeric': 0.027624309392265192, 'fraction_numerical': 0.0, 'mean_word_length': 5.293436293436294, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modeling dimensions in the XDW model - a LVM-driven approach

Since the introduction of eXtensible Markup Language (XML), XML repositories have gained a foothold in many global (and government) organizations, where, e-Commerce and e-Business models have maturated in handling daily transactional data among heterogeneous information systems. Due to this, the amount of data available for enterprise decision-making process is increasing exponentially and are being stored and/or communicated in XML. This presents an interesting challenge to investigate models, frameworks and techniques for organizing and analysing such voluminous, yet distributed XML documents for business intelligence in the form of XML warehouse repositories and XML marts. In our previous work, we proposed a Layered View Model (LVM) driven, conceptual modelling framework for the design and development of an XML Document Warehouse (XDW) model with emphasis on conceptual and logical semantics. There, we presented a view-driven framework to conceptually model and deploy meaningful XML FACT repositories in the XDW model. Here, in this paper, we look at the hierarchical dimensions and their theoretical semantics used to design, specify and define dimensions over an XML FACT repository in the XDW model. One of the unique properties of this LVM-driven approach is that the dimensions are considered as first-class citizens of the XDW conceptual model. Also, here, to illustrate our concepts, we use a real-world case study example; a logically grouped, geographically dispersed, XDW model in the context of a global logistics and cold-storage company.