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Connection to external DBMS

One of the key capabilities of Cubisio is the ability to interact with third-party databases 'on the fly' without the need to copy data into its internal structures. It demonstrates higher efficiency in situations where data is periodically updated

Import of data from files

In Cubisio, data loading from files is performed in two stages - first, the user configures the file parser in their workspace, and then launches the import procedure into any connected DBMS or internal storage

Interaction with web services

The system has a constructor that allows configuring the model of interaction with an external web service based on the HTTP protocol, using it as an element of the data model and interacting with it in the same way as with a DBMS

Extraction from open sources

Cubisio is integrated with browser automation tools and crawling script management, allowing users to construct data loading processes from web pages. Based on the Cubisio cluster, an infrastructure for information loading can be configured

A cube is a key element that allows combining multiple sources into a single multidimensional data model.

Construction of the cube

All work on creating a cube occurs exclusively in a graphical editor - adding sources from the workspace to the construction panel, defining a set of fields to be placed in the cube, linking sources and describing dimensions.

Data visualization

The elements of the cube construction can be visualized as a data table at any time.

Query transformation

All analytical queries to the cube go through a series of transformations and are ultimately executed directly on the DBMS side. If any of the connected DBMS does not support the required functions, the cube performs them independently.

Self-contained system

The cube can be used by third-party systems as a standalone component that allows linking heterogeneous sources. It has its own API in the form of a JSON query language and is available through the HTTP protocol.

By using slices, the user creates a cube, extracts sets of data from it, calculates additional indicators, and creates new modifications.

Slices

Cubisio offers a graphical editor with a wide range of built-in data transformation functions and mathematical operators for calculating various indicators for constructing slices.

Tables

The process of constructing a slice is accompanied by displaying the results as a data table.

Slice chains

Another slice can be used as a data source for a slice. Thus, slice chains can be generated, with the help of which an analyst can sequentially perform data transformations, adding new calculations to each subsequent slice.

API

Similar to the cube, slices can also be used by third-party systems to extract data through the HTTP API.

Cubisio is equipped with a visual constructor of automation schemes for managing the processes of collecting and processing data.

Drag-and-Drop

The construction of the data processing algorithm is carried out by dragging functional blocks from the library to the workspace without the need for coding.

Types of data processing

A wide range of applications for data processing blocks:

Mathematical and statistical modeling;
Classification and clustering of data;
Building predictive models;
Machine learning;
Processing of text and images;
Analysis of financial markets, etc.

Plugins

The library of functional blocks can be easily expanded by connecting plugins with sets of operators covering the required subject area.

Отладка

Analytical processes can also be launched in debug mode and step-by-step execution mode for on-the-fly debugging and improvement of the process.

Cubisio provides an ontology editor for maintaining directories, classifiers, and modeling related data structures.

Ontologies

The ontology editor allows you to visually (on a diagram) create a set of interrelated concepts (data types) that will help analysts formalize and describe almost any subject area.

Data input forms

For each concept in the ontology, an input form is automatically created, allowing the user to fill it with data (instances). Input forms can be used in dashboards.

Data storages

A corresponding physical representation is created in the DBMS for the ontological model, where all the instance part is stored. Instances of concepts can be used in analytical chains in cubes.

Refinement of ontologies

Ontologies can be extended or refined by other ontologies. This allows linking different subject areas and connecting them to each other.

Cubisio offers extensive data and processing results visualization capabilities, including graphs, maps, timelines, interactive drill-down reports with diagrams, charts, and schematics.

Interactivity

The widgets and presentations are interactive and can dynamically rebuild as data is updated and global and contextual filters are applied. They can be used for information dashboards on video walls in conference rooms or situation centers.

Fine-tuning

A large set of options in widget settings and the ability to link them to data allow for detailed customization of the appearance and behavior of widgets.

Multilevel nesting

Widgets support multilevel nesting. Examples of this include diagrams nested in table cells, photographs of people in graph nodes, or annotated text in a tooltip on a map.

Associativity

Widgets, arranged in a presentation, begin to interact with each other. Thus, when a user performs an action on one widget, such as searching or filtering data, the other widgets are immediately updated.

Alienation

Widgets are alienable and can be seamlessly embedded in third-party web applications while retaining all their qualities and the ability to control them from the application using the API.

Filtering

Widgets support multilevel nesting. Examples of this include diagrams nested in table cells, photographs of people in graph nodes, or annotated text in a tooltip on a map.

The analytical graph allows the user to visually investigate data by step-by-step applying transformation operators to them.

Analytical graph

The analytical graph is a diagram where data, ontology entities, operators, and widgets come together. This allows the user to visually create schemes of deep analytical processing, based on the content of the data.

Autonomy

Created schemes can function in autonomous mode, allowing the user to observe real-time changes.

Graphical reuse

The objects used on the graph (data collections, widgets) can be reused in cubes, dashboards, and reports.