Data Commandr: http://conceptoriented.com http://conceptoriented.com (Version 0.6.0, 2017-05-14) Data Commandr (DC) is a web application for working with table

Data Commandr: http://conceptoriented.com http://conceptoriented.com (Version 0.6.0, 2017-05-14) Data Commandr (DC) is a web application for working with tables. Its goal is to make complex operations with data tables as easy as working with classical spreadsheets (but it is not a spreadsheet application) for producing reports, data integration, data migration and other data wrangling tasks. Its main distinguishing feature is that it does not use traditional (set-oriented) queries but rather relies on column formulas to derive new data. I would like to get your option about Data Commandr from three points of view: - [As developers] How do you like the current UI and general design? How it can be improved? - [As users] Can such a tool be useful for working with tables? - [As investors] What other tasks can be solved by using this approach? For example, I want to pivot in the direction of stream analytics. Or it could be applied to tasks where Spark is normally used (DC might have much higher performance and much simpler data processing language.) Other applications: data dictionary and data catalogs. Thank you in advance for testing the app and your feedback. COMPARISON: - [Spreadsheets] In spreadsheets, a cell is defined as a function of other cells so we get a functional model where the result of each cell (one value) is computed by evaluating other cell formulas. In contrast, DC uses columns instead of cells so that a model is a number of column formulas. - [Set-oriented approaches] In set-oriented approaches (RM, Hadoop, Spark etc.), new data is derived by applying an operation to input sets and producing an output set. Data transformations are represented as a graph of set operations. In contrast, data transformations in DC are represented as a graph of function operations. - [Research] Formally, Data Commandr relies on the concept-oriented model of data [2]. In particular, DC does not rely on such difficult to use and understand operations like join and group-by. Instead, it uses link columns [4] and accumulate functions [1], respectively. - [Competitors] When I started this project [5] I knew only one similar product which is Power BI and its DAX language. Now there exist such relatively new products like airtable, fieldbook and rowshare. [1] A. Savinov. From Group-By to Accumulation: Data Aggregation Revisited. Proc. IoTBDS 2017, 370-379, 2017. https://www.researchgate.net/publication/316551218_From_Group-by_to_Accumulation_Data_Aggregation_Revisited https://www.researchgate.net/publication/316551218_From_Grou... [2] A. Savinov. Concept-Oriented Model: the Functional View. arXiv:1606.02237 [cs.DB] 2016. https://arxiv.org/abs/1606.02237 https://arxiv.org/abs/1606.02237 [3] A. Savinov. DataCommandr: Column-Oriented Data Integration, Transformation and Analysis. Proc. IoTBD 2016, 339-347. https://www.researchgate.net/publication/301764506_DataCommandr_Column-Oriented_Data_Integration_Transformation_and_Analysis https://www.researchgate.net/publication/301764506_DataComma... [4] A. Savinov. Joins vs. Links or Relational Join Considered Harmful. Proc. IoTBD 2016, 362-368. https://www.researchgate.net/publication/301764816_Joins_vs_Links_or_Relational_Join_Considered_Harmful https://www.researchgate.net/publication/301764816_Joins_vs_... [5] A. Savinov. ConceptMix: Self-Service Analytical Data Integration Based on the Concept-Oriented Model, Proc. DATA 2014, 78-84 https://www.researchgate.net/publication/265301356_ConceptMix_Self-Service_Analytical_Data_Integration_based_on_the_Concept-Oriented_Model https://www.researchgate.net/publication/265301356_ConceptMi...