1) Does TerminusdB support raw time-series data (one dimensional) for example electrocadiogram (ECG)? At the moment we have them including the metadata in column based CSV format. FYI, the size is around 1 MB for one minute raw ECG data duration.
2) For automated ECG analysis the data is transformed using time-frequency distribution (two dimensional), and the intermediate data must be kept in-memory for feature extraction purpose. Just wondering does TerminusDB can support this intermediate format/structure of time-frequency as well? FYI, the one minute time-frequency ECG data transformed from (1) will need around 4GB of working memory. For real-time analysis of longer ECG data duration from (1), for example 30 minutes duration or 30 MB data size, we need around 3 TB of working memory.
The graph database is still in its infancy and there are a lot of graph query languages about. We played around with using some that already exist (especially SPARQL) but decided that we wanted a number of features that were very non-standard (such as CLP(fd)).
Using JSON-LD as the definition, storage and interchange format for the query language has advantages. Since we can marshall JSON-LD into and out of the graph, it is easy to store queries in the graph. It is also very simple to write query libraries for a range of languages by just building up a JSON-LD object and sending it off.
We are firmly of the belief that datalog-style query languages which favour composibility will eventually win the query language wars - even if it is not our particular variety which does so. Composibility was not treated as centrally as it should have been in most of the graph languages.