A Business and Technologies journey

Some time ago I wrote the first part of this blog . In this second part I will detail how the temporal query engine is actually implemented in MarkLogic. The objective of the engine is to identify period of time during the Tests when multiple conditions are met and especially when a temporal pattern is realised. This blog is quite technical with some advanced concepts which are not that easy to explain, especially by a non native English speaker :) . Don't hesitate to contact me if you want more details. The data loaded in MarkLogic Context sources First we have data sources which provide metadata about the context of the Tests. Depending on the use case it could be: Weather data Test conditions Crew/expert reports: documents/unstructured content Configuration of the product during the test Etc. In MarkLogic these sources can be structured or unstructured data and could eventually contain geospatial shapes. Data is stored in documents (xml or json). The time s...

With the new year, it's time for a retrospective of the past year and prepare for 2019. First, 2018 was the year of GDPR (The EU G eneral D ata P rotection R egulation). GDPR had been in the agenda of global companies and consulting firms for at least 2 years for being the most important change in data privacy regulation in 20 years. From the MarkLogic perspective GDPR, is a great opportunity to demonstrate the value of an operation DataHub  to store data and metadata together to support robust data governance required by the regulation.Concrete uses cases deal for example with consent management in customer 360 hub or tackling right to be forgotten by managing data lineage from golden record to source dataset records.  A detailed illustration is described is this blog post :  How a Data Hub can help to find back memory to tackle GDPR right to be forgotten . And a more generic illustration is details here :  Metadata in operationa...