Modeling and querying facts with period timestamps in data warehouses

Abstract

In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science

“…Typically, enterprise data comes from enterprise data warehousing, where ‘enterprise big tables’ are constructed for analytical subjects by pulling mixed information from all relational and non-relational data sources and business lines and organized for customers 29 , 30 . Such big tables are indexed per all customers of the enterprise involving different products, services, processes, and communications with the enterprise.…”